# assets

url: https://api.github.com/repos/asg017/sqlite-jsonschema/releases/assets/112185832
node_id: RA_kwDOIX3aMs4Gr9Ho
name: sqlite-jsonschema-v0.2.3-alpha.5-loadable-macos-x86_64.tar.gz
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T20:53:12Z
updated_at: 2023-06-10T20:53:13Z
browser_download_url:
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node_id: RA_kwDOIX3aMs4Gr9Hp
name: sqlite-jsonschema-v0.2.3-alpha.5-loadable-macos-aarch64.tar.gz
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T20:53:12Z
updated_at: 2023-06-10T20:53:13Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-jsonschema/releases/assets/112185834
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name: sqlite-jsonschema-v0.2.3-alpha.5-loadable-linux-x86_64.tar.gz
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T20:53:12Z
updated_at: 2023-06-10T20:53:13Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-fastrand/releases/assets/112186328
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name: deno-windows-x86_64.fastrand0.dll
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T20:58:32Z
updated_at: 2023-06-10T20:58:32Z
browser_download_url:
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name: deno-linux-x86_64.fastrand0.so
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T20:58:32Z
updated_at: 2023-06-10T20:58:32Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-fastrand/releases/assets/112186330
node_id: RA_kwDOIu6Scs4Gr9Pa
name: deno-darwin-aarch64.fastrand0.dylib
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T20:58:32Z
updated_at: 2023-06-10T20:58:32Z
browser_download_url:
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node_id: RA_kwDOIu6Scs4Gr9Pb
name: deno-darwin-x86_64.fastrand0.dylib
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T20:58:32Z
updated_at: 2023-06-10T20:58:32Z
browser_download_url:
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node_id: RA_kwDOIu6Scs4Gr9Ph
name: sqlite-fastrand-v0.2.1-alpha.8-loadable-windows-x86_64.zip
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T20:58:36Z
updated_at: 2023-06-10T20:58:37Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-fastrand/releases/assets/112186338
node_id: RA_kwDOIu6Scs4Gr9Pi
name: sqlite-fastrand-v0.2.1-alpha.8-loadable-macos-x86_64.tar.gz
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T20:58:36Z
updated_at: 2023-06-10T20:58:37Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-fastrand/releases/assets/112186340
node_id: RA_kwDOIu6Scs4Gr9Pk
name: sqlite-fastrand-v0.2.1-alpha.8-loadable-macos-aarch64.tar.gz
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T20:58:37Z
updated_at: 2023-06-10T20:58:37Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-fastrand/releases/assets/112186341
node_id: RA_kwDOIu6Scs4Gr9Pl
name: sqlite-fastrand-v0.2.1-alpha.8-loadable-linux-x86_64.tar.gz
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T20:58:37Z
updated_at: 2023-06-10T20:58:37Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-regex/releases/assets/112189136
node_id: RA_kwDOIX3Uzc4Gr97Q
name: deno-darwin-x86_64.regex0.dylib
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T21:30:45Z
updated_at: 2023-06-10T21:30:46Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-regex/releases/assets/112189137
node_id: RA_kwDOIX3Uzc4Gr97R
name: deno-windows-x86_64.regex0.dll
content_type: application/json; charset=utf-8
state: uploaded
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updated_at: 2023-06-10T21:30:46Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-regex/releases/assets/112189139
node_id: RA_kwDOIX3Uzc4Gr97T
name: deno-darwin-aarch64.regex0.dylib
content_type: application/json; charset=utf-8
state: uploaded
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updated_at: 2023-06-10T21:30:46Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-regex/releases/assets/112189140
node_id: RA_kwDOIX3Uzc4Gr97U
name: sqlite-regex-v0.2.3-alpha.9-loadable-windows-x86_64.zip
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T21:30:46Z
updated_at: 2023-06-10T21:30:47Z
browser_download_url:
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node_id: RA_kwDOIX3Uzc4Gr97W
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content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T21:30:47Z
updated_at: 2023-06-10T21:30:47Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-regex/releases/assets/112189143
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content_type: application/json; charset=utf-8
state: uploaded
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updated_at: 2023-06-10T21:30:47Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-regex/releases/assets/112189144
node_id: RA_kwDOIX3Uzc4Gr97Y
name: sqlite-regex-v0.2.3-alpha.9-loadable-linux-x86_64.tar.gz
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T21:30:47Z
updated_at: 2023-06-10T21:30:47Z
browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-ulid/releases/assets/112189319
node_id: RA_kwDOIpo-2s4Gr9-H
name: sqlite-ulid-v0.2.1-alpha.14-loadable-windows-x86_64.zip
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T21:33:21Z
updated_at: 2023-06-10T21:33:21Z
browser_download_url:
  https://github.com/asg017/sqlite-ulid/releases/download/v0.2.1-alpha.14/sqlite-ulid-v0.2.1-alpha.14-loadable-windows-x86_64.zip


url: https://api.github.com/repos/asg017/sqlite-ulid/releases/assets/112189320
node_id: RA_kwDOIpo-2s4Gr9-I
name: sqlite-ulid-v0.2.1-alpha.14-loadable-macos-x86_64.tar.gz
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T21:33:21Z
updated_at: 2023-06-10T21:33:21Z
browser_download_url:
  https://github.com/asg017/sqlite-ulid/releases/download/v0.2.1-alpha.14/sqlite-ulid-v0.2.1-alpha.14-loadable-macos-x86_64.tar.gz


url: https://api.github.com/repos/asg017/sqlite-ulid/releases/assets/112189321
node_id: RA_kwDOIpo-2s4Gr9-J
name: sqlite-ulid-v0.2.1-alpha.14-loadable-macos-aarch64.tar.gz
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T21:33:21Z
updated_at: 2023-06-10T21:33:21Z
browser_download_url:
  https://github.com/asg017/sqlite-ulid/releases/download/v0.2.1-alpha.14/sqlite-ulid-v0.2.1-alpha.14-loadable-macos-aarch64.tar.gz


url: https://api.github.com/repos/asg017/sqlite-ulid/releases/assets/112189322
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name: sqlite-ulid-v0.2.1-alpha.14-loadable-linux-x86_64.tar.gz
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T21:33:21Z
updated_at: 2023-06-10T21:33:21Z
browser_download_url:
  https://github.com/asg017/sqlite-ulid/releases/download/v0.2.1-alpha.14/sqlite-ulid-v0.2.1-alpha.14-loadable-linux-x86_64.tar.gz


url: https://api.github.com/repos/asg017/sqlite-ulid/releases/assets/112189336
node_id: RA_kwDOIpo-2s4Gr9-Y
name: deno-windows-x86_64.ulid0.dll
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T21:33:26Z
updated_at: 2023-06-10T21:33:26Z
browser_download_url:
  https://github.com/asg017/sqlite-ulid/releases/download/v0.2.1-alpha.14/deno-windows-x86_64.ulid0.dll


url: https://api.github.com/repos/asg017/sqlite-ulid/releases/assets/112189337
node_id: RA_kwDOIpo-2s4Gr9-Z
name: deno-darwin-aarch64.ulid0.dylib
content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T21:33:26Z
updated_at: 2023-06-10T21:33:26Z
browser_download_url:
  https://github.com/asg017/sqlite-ulid/releases/download/v0.2.1-alpha.14/deno-darwin-aarch64.ulid0.dylib


url: https://api.github.com/repos/asg017/sqlite-ulid/releases/assets/112189339
node_id: RA_kwDOIpo-2s4Gr9-b
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content_type: application/json; charset=utf-8
state: uploaded
created_at: 2023-06-10T21:33:26Z
updated_at: 2023-06-10T21:33:26Z
browser_download_url:
  https://github.com/asg017/sqlite-ulid/releases/download/v0.2.1-alpha.14/deno-darwin-x86_64.ulid0.dylib


url: https://api.github.com/repos/asg017/sqlite-jsonschema/releases/assets/112189542
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created_at: 2023-06-10T21:36:11Z
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browser_download_url:
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browser_download_url:
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url: https://api.github.com/repos/asg017/sqlite-jsonschema/releases/assets/112189544
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state: uploaded
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browser_download_url:
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browser_download_url:
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browser_download_url:
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# pypi_packages

name: airtable-export
summary: Export Airtable data to files on disk
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/airtable-export
license: Apache License, Version 2.0
package_url: https://pypi.org/project/airtable-export/
project_url: https://pypi.org/project/airtable-export/
release_url: https://pypi.org/project/airtable-export/0.7.1/
version: 0.7.1
description:
  # airtable-export

  [![PyPI](https://img.shields.io/pypi/v/airtable-export.svg)](https://pypi.org/project/airtable-export/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/airtable-export?include_prereleases&label=changelog)](https://github.com/simonw/airtable-export/releases)
  [![Tests](https://github.com/simonw/airtable-export/workflows/Test/badge.svg)](https://github.com/simonw/airtable-export/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/airtable-export/blob/master/LICENSE)

  Export Airtable data to files on disk

  ## Installation

  Install this tool using `pip`:

      $ pip install airtable-export

  ## Usage

  You will need to know the following information:

  - Your Airtable base ID - this is a string starting with `app...`
  - Your Airtable API key - this is a string starting with `key...`
  - The names of each of the tables that you wish to export

  You can export all of your data to a folder called `export/` by running the following:

      airtable-export export base_id table1 table2 --key=key

  This example would create two files: `export/table1.yml` and `export/table2.yml`.

  Rather than passing the API key using the `--key` option you can set it as an environment variable called `AIRTABLE_KEY`.

  ## Export options

  By default the tool exports your data as YAML.

  You can also export as JSON or as [newline delimited JSON](http://ndjson.org/) using the `--json` or `--ndjson` options:

      airtable-export export base_id table1 table2 --key=key --ndjson

  You can pass multiple format options at once. This command will create a `.json`, `.yml` and `.ndjson` file for each exported table:

      airtable-export export base_id table1 table2 \
          --key=key --ndjson --yaml --json

  ### SQLite database export

  You can export tables to a SQLite database file using the `--sqlite database.db` option:

      airtable-export export base_id table1 table2 \
          --key=key --sqlite database.db

  This can be combined with other format options. If you only specify `--sqlite` the export directory argument will be ignored.

  The SQLite database will have a table created for each table you export. Those tables will have a primary key column called `airtable_id`.

  If you run this command against an existing SQLite database records with matching primary keys will be over-written by new records from the export.

  ## Request options

  By default the tool uses [python-httpx](https://www.python-httpx.org)'s default configurations.

  You can override the `user-agent` using the `--user-agent` option:

      airtable-export export base_id table1 table2 --key=key --user-agent "Airtable Export Robot"

  You can override the [timeout during a network read operation](https://www.python-httpx.org/advanced/#fine-tuning-the-configuration) using the `--http-read-timeout` option. If not set, this defaults to 5s.

      airtable-export export base_id table1 table2 --key=key --http-read-timeout 60

  ## Running this using GitHub Actions

  [GitHub Actions](https://github.com/features/actions) is GitHub's workflow automation product. You can use it to run `airtable-export` in order to back up your Airtable data to a GitHub repository. Doing this gives you a visible commit history of changes you make to your Airtable data - like [this one](https://github.com/natbat/rockybeaches/commits/main/airtable).

  To run this for your own Airtable database you'll first need to add the following secrets to your GitHub repository:

  <dl>
    <dt>AIRTABLE_BASE_ID</dt>
    <dd>The base ID, a string beginning `app...`</dd>
    <dt>AIRTABLE_KEY</dt>
    <dd>Your Airtable API key</dd>
    <dt>AIRTABLE_TABLES</dt>
    <dd>A space separated list of the Airtable tables that you want to backup. If any of these contain spaces you will need to enclose them in single quotes, e.g. <samp>'My table with spaces in the name' OtherTableWithNoSpaces</samp></dd>
  </dl>

  Once you have set those secrets, add the following as a file called `.github/workflows/backup-airtable.yml`:
  ```yaml
  name: Backup Airtable

  on:
    workflow_dispatch:
    schedule:
    - cron: '32 0 * * *'

  jobs:
    build:
      runs-on: ubuntu-latest
      steps:
      - name: Check out repo
        uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: 3.8
      - uses: actions/cache@v2
        name: Configure pip caching
        with:
          path: ~/.cache/pip
          key: ${{ runner.os }}-pip-
          restore-keys: |
            ${{ runner.os }}-pip-
      - name: Install airtable-export
        run: |
          pip install airtable-export
      - name: Backup Airtable to backups/
        env:
          AIRTABLE_BASE_ID: ${{ secrets.AIRTABLE_BASE_ID }}
          AIRTABLE_KEY: ${{ secrets.AIRTABLE_KEY }}
          AIRTABLE_TABLES: ${{ secrets.AIRTABLE_TABLES }}
        run: |-
          airtable-export backups $AIRTABLE_BASE_ID $AIRTABLE_TABLES -v
      - name: Commit and push if it changed
        run: |-
          git config user.name "Automated"
          git config user.email "actions@users.noreply.github.com"
          git add -A
          timestamp=$(date -u)
          git commit -m "Latest data: ${timestamp}" || exit 0
          git push
  ```
  This will run once a day (at 32 minutes past midnight UTC) and will also run if you manually click the "Run workflow" button, see [GitHub Actions: Manual triggers with workflow_dispatch](https://github.blog/changelog/2020-07-06-github-actions-manual-triggers-with-workflow_dispatch/).

  ## Development

  To contribute to this tool, first checkout the code. Then create a new virtual environment:

      cd airtable-export
      python -mvenv venv
      source venv/bin/activate

  Or if you are using `pipenv`:

      pipenv shell

  Now install the dependencies and tests:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/airtable-export/actions", "Changelog": "https://github.com/simonw/airtable-export/releases", "Homepage": "https://github.com/simonw/airtable-export", "Issues": "https://github.com/simonw/airtable-export/issues"}

requires_dist:
  ["click", "PyYAML", "httpx", "sqlite-utils", "pytest ; extra == 'test'", "pytest-mock ; extra == 'test'"]


name: apple-notes-to-sqlite
summary: Export Apple Notes to SQLite
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/dogsheep/apple-notes-to-sqlite
license: Apache License, Version 2.0
package_url: https://pypi.org/project/apple-notes-to-sqlite/
project_url: https://pypi.org/project/apple-notes-to-sqlite/
release_url: https://pypi.org/project/apple-notes-to-sqlite/0.1/
requires_dist: ["click", "sqlite-utils", "pytest ; extra == 'test'", "pytest-subprocess ; extra == 'test'"]
requires_python: >=3.7
version: 0.1
description:
  # apple-notes-to-sqlite

  [![PyPI](https://img.shields.io/pypi/v/apple-notes-to-sqlite.svg)](https://pypi.org/project/apple-notes-to-sqlite/)
  [![Changelog](https://img.shields.io/github/v/release/dogsheep/apple-notes-to-sqlite?include_prereleases&label=changelog)](https://github.com/dogsheep/apple-notes-to-sqlite/releases)
  [![Tests](https://github.com/dogsheep/apple-notes-to-sqlite/workflows/Test/badge.svg)](https://github.com/dogsheep/apple-notes-to-sqlite/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/dogsheep/apple-notes-to-sqlite/blob/master/LICENSE)

  Export Apple Notes to SQLite

  ## Installation

  Install this tool using `pip`:

      pip install apple-notes-to-sqlite

  ## Usage

  For help, run:

      apple-notes-to-sqlite --help

  You can also use:

      python -m apple_notes_to_sqlite --help

  To save your notes to a SQLite database called `notes.db` run:

      apple-notes-to-sqlite notes.db

  A progress bar will be shown.

  You can stop it after a specified number of notes using `--stop-after`:

      apple-notes-to-sqlite notes.db --stop-after 10

  ## Development

  To contribute to this tool, first checkout the code. Then create a new virtual environment:

      cd apple-notes-to-sqlite
      python -m venv venv
      source venv/bin/activate

  Now install the dependencies and test dependencies:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/dogsheep/apple-notes-to-sqlite/actions", "Changelog": "https://github.com/dogsheep/apple-notes-to-sqlite/releases", "Homepage": "https://github.com/dogsheep/apple-notes-to-sqlite", "Issues": "https://github.com/dogsheep/apple-notes-to-sqlite/issues"}


name: csv-diff
summary: Python CLI tool and library for diffing CSV and JSON files
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/csv-diff
license: Apache License, Version 2.0
package_url: https://pypi.org/project/csv-diff/
project_url: https://pypi.org/project/csv-diff/
project_urls: {"Homepage": "https://github.com/simonw/csv-diff"}
provides_extra: ["test"]
release_url: https://pypi.org/project/csv-diff/1.2/
requires_dist: ["click", "dictdiffer", "pytest; extra == \"test\""]
version: 1.2
classifiers:
  ["Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: End Users/Desktop", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7"]

description:
  # csv-diff

  [![PyPI](https://img.shields.io/pypi/v/csv-diff.svg)](https://pypi.org/project/csv-diff/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/csv-diff?include_prereleases&label=changelog)](https://github.com/simonw/csv-diff/releases)
  [![Tests](https://github.com/simonw/csv-diff/workflows/Test/badge.svg)](https://github.com/simonw/csv-diff/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/csv-diff/blob/main/LICENSE)

  Tool for viewing the difference between two CSV, TSV or JSON files. See [Generating a commit log for San Francisco’s official list of trees](https://simonwillison.net/2019/Mar/13/tree-history/) (and the [sf-tree-history repo commit log](https://github.com/simonw/sf-tree-history/commits)) for background information on this project.

  ## Installation

      pip install csv-diff

  ## Usage

  Consider two CSV files:

  `one.csv`

      id,name,age
      1,Cleo,4
      2,Pancakes,2

  `two.csv`

      id,name,age
      1,Cleo,5
      3,Bailey,1

  `csv-diff` can show a human-readable summary of differences between the files:

      $ csv-diff one.csv two.csv --key=id
      1 row changed, 1 row added, 1 row removed

      1 row changed

        Row 1
          age: "4" => "5"

      1 row added

        id: 3
        name: Bailey
        age: 1

      1 row removed

        id: 2
        name: Pancakes
        age: 2

  The `--key=id` option means that the `id` column should be treated as the unique key, to identify which records have changed.

  The tool will automatically detect if your files are comma- or tab-separated. You can over-ride this automatic detection and force the tool to use a specific format using `--format=tsv` or `--format=csv`.

  You can also feed it JSON files, provided they are a JSON array of objects where each object has the same keys. Use `--format=json` if your input files are JSON.

  Use `--show-unchanged` to include full details of the unchanged values for rows with at least one change in the diff output:

      % csv-diff one.csv two.csv --key=id --show-unchanged
      1 row changed

        id: 1
          age: "4" => "5"

          Unchanged:
            name: "Cleo"

  ### JSON output

  You can use the `--json` option to get a machine-readable difference:

      $ csv-diff one.csv two.csv --key=id --json
      {
          "added": [
              {
                  "id": "3",
                  "name": "Bailey",
                  "age": "1"
              }
          ],
          "removed": [
              {
                  "id": "2",
                  "name": "Pancakes",
                  "age": "2"
              }
          ],
          "changed": [
              {
                  "key": "1",
                  "changes": {
                      "age": [
                          "4",
                          "5"
                      ]
                  }
              }
          ],
          "columns_added": [],
          "columns_removed": []
      }

  ### Adding templated extras

  You can specify additional keys to be displayed in the human-readable format using the `--extra` option:

      --extra name "Python format string with {id} for variables"

  For example, to output a link to `https://news.ycombinator.com/latest?id={id}` for each item with an ID, you could use this:

  ```bash
  csv-diff one.csv two.csv --key=id \
    --extra latest "https://news.ycombinator.com/latest?id={id}"
  ```
  These extras display something like this:
  ```
  1 row changed

    id: 41459472
      points: "24" => "25"
      numComments: "5" => "6"
    extras:
      latest: https://news.ycombinator.com/latest?id=41459472
  ```

  ## As a Python library

  You can also import the Python library into your own code like so:

      from csv_diff import load_csv, compare
      diff = compare(
          load_csv(open("one.csv"), key="id"),
          load_csv(open("two.csv"), key="id")
      )

  `diff` will now contain the same data structure as the output in the `--json` example above.

  If the columns in the CSV have changed, those added or removed columns will be ignored when calculating changes made to specific rows.

  ## As a Docker container

  ### Build the image

      $ docker build -t csvdiff .

  ### Run the container

      $ docker run --rm -v $(pwd):/files csvdiff

  Suppose current directory contains two csv files : one.csv two.csv

      $ docker run --rm -v $(pwd):/files csvdiff one.csv two.csv
      
  ## Alternatives

  - [csvdiff](https://github.com/aswinkarthik/csvdiff) is a "fast diff tool for comparing CSV files" - you may get better results from this than from `csv-diff` against larger files.


name: csvs-to-sqlite
summary: Convert CSV files into a SQLite database
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/csvs-to-sqlite
license: Apache License, Version 2.0
license_files: ["LICENSE"]
package_url: https://pypi.org/project/csvs-to-sqlite/
project_url: https://pypi.org/project/csvs-to-sqlite/
project_urls: {"Homepage": "https://github.com/simonw/csvs-to-sqlite"}
provides_extra: ["test"]
release_url: https://pypi.org/project/csvs-to-sqlite/1.3.1/
version: 1.3.1
classifiers:
  ["Intended Audience :: Developers", "Intended Audience :: End Users/Desktop", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Topic :: Database"]

description:
  # csvs-to-sqlite

  [![PyPI](https://img.shields.io/pypi/v/csvs-to-sqlite.svg)](https://pypi.org/project/csvs-to-sqlite/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/csvs-to-sqlite?include_prereleases&label=changelog)](https://github.com/simonw/csvs-to-sqlite/releases)
  [![Tests](https://github.com/simonw/csvs-to-sqlite/workflows/Test/badge.svg)](https://github.com/simonw/csvs-to-sqlite/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/csvs-to-sqlite/blob/main/LICENSE)

  Convert CSV files into a SQLite database. Browse and publish that SQLite database with [Datasette](https://github.com/simonw/datasette).

  > [!NOTE]
  > This tool is **infrequently maintained**. I suggest [using sqlite-utils](https://sqlite-utils.datasette.io/en/stable/cli.html#inserting-csv-or-tsv-data) for importing CSV and TSV to SQLite instead for most cases.

  Basic usage:
  ```bash
  csvs-to-sqlite myfile.csv mydatabase.db
  ```
  This will create a new SQLite database called `mydatabase.db` containing a
  single table, `myfile`, containing the CSV content.

  You can provide multiple CSV files:
  ```
  csvs-to-sqlite one.csv two.csv bundle.db
  ```
  The `bundle.db` database will contain two tables, `one` and `two`.

  This means you can use wildcards:
  ```bash
  csvs-to-sqlite ~/Downloads/*.csv my-downloads.db
  ```
  If you pass a path to one or more directories, the script will recursively
  search those directories for CSV files and create tables for each one.
  ```bash
  csvs-to-sqlite ~/path/to/directory all-my-csvs.db
  ```
  ## Handling TSV (tab-separated values)

  You can use the `-s` option to specify a different delimiter. If you want
  to use a tab character you'll need to apply shell escaping like so:
  ```bash
  csvs-to-sqlite my-file.tsv my-file.db -s $'\t'
  ```
  ## Refactoring columns into separate lookup tables

  Let's say you have a CSV file that looks like this:
  ```csv
  county,precinct,office,district,party,candidate,votes
  Clark,1,President,,REP,John R. Kasich,5
  Clark,2,President,,REP,John R. Kasich,0
  Clark,3,President,,REP,John R. Kasich,7
  ```
  ([Real example taken from the Open Elections project](https://github.com/openelections/openelections-data-sd/blob/master/2016/20160607__sd__primary__clark__precinct.csv))

  You can now convert selected columns into separate lookup tables using the new
  `--extract-column` option (shortname: `-c`) - for example:
  ```bash
  csvs-to-sqlite openelections-data-*/*.csv \
      -c county:County:name \
      -c precinct:Precinct:name \
      -c office -c district -c party -c candidate \
      openelections.db
  ```
  The format is as follows:
  ```bash
  column_name:optional_table_name:optional_table_value_column_name
  ```
  If you just specify the column name e.g. `-c office`, the following table will
  be created:
  ```sql
  CREATE TABLE "office" (
      "id" INTEGER PRIMARY KEY,
      "value" TEXT
  );
  ```
  If you specify all three options, e.g. `-c precinct:Precinct:name` the table
  will look like this:
  ```sql
  CREATE TABLE "Precinct" (
      "id" INTEGER PRIMARY KEY,
      "name" TEXT
  );
  ```
  The original tables will be created like this:
  ```sql
  CREATE TABLE "ca__primary__san_francisco__precinct" (
      "county" INTEGER,
      "precinct" INTEGER,
      "office" INTEGER,
      "district" INTEGER,
      "party" INTEGER,
      "candidate" INTEGER,
      "votes" INTEGER,
      FOREIGN KEY (county) REFERENCES County(id),
      FOREIGN KEY (party) REFERENCES party(id),
      FOREIGN KEY (precinct) REFERENCES Precinct(id),
      FOREIGN KEY (office) REFERENCES office(id),
      FOREIGN KEY (candidate) REFERENCES candidate(id)
  );
  ```
  They will be populated with IDs that reference the new derived tables.

  ## Installation

  ```bash
  pip install csvs-to-sqlite
  ```

  `csvs-to-sqlite` now requires Python 3. If you are running Python 2 you can install the last version to support Python 2:
  ```bash
  pip install csvs-to-sqlite==0.9.2
  ```

  ## csvs-to-sqlite --help

  <!-- [[[cog
  import cog
  from csvs_to_sqlite import cli
  from click.testing import CliRunner
  runner = CliRunner()
  result = runner.invoke(cli.cli, ["--help"])
  help = result.output.replace("Usage: cli", "Usage: csvs-to-sqlite")
  cog.out(
      "```\n{}\n```".format(help)
  )
  ]]] -->
  ```
  Usage: csvs-to-sqlite [OPTIONS] PATHS... DBNAME

    PATHS: paths to individual .csv files or to directories containing .csvs

    DBNAME: name of the SQLite database file to create

  Options:
    -s, --separator TEXT            Field separator in input .csv
    -q, --quoting INTEGER           Control field quoting behavior per csv.QUOTE_*
                                    constants. Use one of QUOTE_MINIMAL (0),
                                    QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or
                                    QUOTE_NONE (3).
    --skip-errors                   Skip lines with too many fields instead of
                                    stopping the import
    --replace-tables                Replace tables if they already exist
    -t, --table TEXT                Table to use (instead of using CSV filename)
    -c, --extract-column TEXT       One or more columns to 'extract' into a
                                    separate lookup table. If you pass a simple
                                    column name that column will be replaced with
                                    integer foreign key references to a new table
                                    of that name. You can customize the name of
                                    the table like so:     state:States:state_name
                                    
                                    This will pull unique values from the 'state'
                                    column and use them to populate a new 'States'
                                    table, with an id column primary key and a
                                    state_name column containing the strings from
                                    the original column.
    -d, --date TEXT                 One or more columns to parse into ISO
                                    formatted dates
    -dt, --datetime TEXT            One or more columns to parse into ISO
                                    formatted datetimes
    -df, --datetime-format TEXT     One or more custom date format strings to try
                                    when parsing dates/datetimes
    -pk, --primary-key TEXT         One or more columns to use as the primary key
    -f, --fts TEXT                  One or more columns to use to populate a full-
                                    text index
    -i, --index TEXT                Add index on this column (or a compound index
                                    with -i col1,col2)
    --shape TEXT                    Custom shape for the DB table - format is
                                    csvcol:dbcol(TYPE),...
    --filename-column TEXT          Add a column with this name and populate with
                                    CSV file name
    --fixed-column <TEXT TEXT>...   Populate column with a fixed string
    --fixed-column-int <TEXT INTEGER>...
                                    Populate column with a fixed integer
    --fixed-column-float <TEXT FLOAT>...
                                    Populate column with a fixed float
    --no-index-fks                  Skip adding index to foreign key columns
                                    created using --extract-column (default is to
                                    add them)
    --no-fulltext-fks               Skip adding full-text index on values
                                    extracted using --extract-column (default is
                                    to add them)
    --just-strings                  Import all columns as text strings by default
                                    (and, if specified, still obey --shape,
                                    --date/datetime, and --datetime-format)
    --version                       Show the version and exit.
    --help                          Show this message and exit.

  ```
  <!-- [[[end]]] -->

dynamic:
  ["Author", "Classifier", "Description", "Description-Content-Type", "Home-Page", "License", "License-File", "Provides-Extra", "Requires-Dist", "Summary"]

requires_dist:
  ["click>=7.0", "dateparser>=1.0", "pandas>=1.0", "py-lru-cache~=0.1.4", "six", "pytest; extra == \"test\"", "cogapp; extra == \"test\""]


name: datasette-acl
summary: Advanced permission management for Datasette
classifiers: ["Framework :: Datasette"]
author: Simon Willison
description_content_type: text/markdown
dynamic: ["License-File"]
license_expression: Apache-2.0
license_files: ["LICENSE"]
package_url: https://pypi.org/project/datasette-acl/
project_url: https://pypi.org/project/datasette-acl/
release_url: https://pypi.org/project/datasette-acl/0.5a1/
requires_dist: ["datasette>=1.0a20", "sqlite-migrate==0.1b0"]
requires_python: >=3.10
version: 0.5a1
description:
  # datasette-acl

  [![PyPI](https://img.shields.io/pypi/v/datasette-acl.svg)](https://pypi.org/project/datasette-acl/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-acl?include_prereleases&label=changelog)](https://github.com/datasette/datasette-acl/releases)
  [![Tests](https://github.com/datasette/datasette-acl/actions/workflows/test.yml/badge.svg)](https://github.com/datasette/datasette-acl/actions/workflows/test.yml)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-acl/blob/main/LICENSE)

  Advanced permission management for Datasette. **Highly experimental**.

  ## Installation

  Install this plugin in the same environment as Datasette. This plugin requires Datasette 1.0a15 or higher.
  ```bash
  datasette install datasette-acl
  ```
  ## Usage

  This plugin is under active development. It currently only supports configuring [permissions](https://docs.datasette.io/en/latest/authentication.html#permissions) for individual tables, controlling the following:

  - `insert-row`
  - `delete-row`
  - `update-row`
  - `alter-table`
  - `drop-table`

  Permissions are saved in the internal database. This means you should run Datasette with the `--internal path/to/internal.db` option, otherwise your permissions will be reset every time you restart Datasette.

  A JSON HTTP API for reading and managing per-resource grants programmatically is documented in [docs/json-api.md](docs/json-api.md).

  ### Managing permissions for a table

  The interface for configuring table permissions lives at `/database-name/table-name/-/acl`. It can be accessed from the table actions menu on the table page.

  Permission can be granted for each of the above table actions. They can be assigned to both groups and individual users, who can be added using their `actor["id"]`.

  An audit log tracks which permissions were added and removed, displayed at the bottom of the table permissions page.

  ### Custom resource types

  `datasette-acl` is not limited to tables. It can store and resolve grants for *any* resource type defined by a plugin - documents, lists, workbooks, comment spaces, kanban boards and so on.

  There is no dedicated hook for this. A plugin makes its resource type manageable by `datasette-acl` simply by registering actions whose `resource_class` is a [Resource](https://docs.datasette.io/en/latest/internals.html) subclass:

  ```python
  from datasette import hookimpl
  from datasette.permissions import Action, Resource


  class PaperDoc(Resource):
      name = "paper-doc"


  @hookimpl
  def register_actions(datasette):
      return [
          Action(name="paper-view", description="View a doc", resource_class=PaperDoc),
          Action(name="paper-edit", description="Edit a doc", resource_class=PaperDoc),
      ]
  ```

  `datasette-acl` discovers resource types from the actions registered across all plugins (`datasette.actions`). Both the action set offered on the table permissions page and the resource types managed by the generic admin page are derived dynamically from this - nothing is hardcoded.

  A generic admin page for any resource type lives at:

  ```
  /-/acl/resource/<resource-type>/<parent>/<child>
  ```

  For example `/-/acl/resource/paper-doc/workspace-1/doc-42`. The `<child>` segment is optional for parent-only resource types (`/-/acl/resource/<resource-type>/<parent>`). The page presents the same group/user grant interface and audit log as the table page, with checkboxes for exactly the actions registered for that resource type. Access to this admin page is gated on the `datasette-acl` permission described below.

  Grants made here flow through Datasette's permission system: once granted, `await datasette.allowed(actor=..., action="paper-edit", resource=PaperDoc("workspace-1", "doc-42"))` returns `True`.

  ### Controlling who can edit permissions

  Users with the new `datasette-acl` permission will have the ability to access a UI for setting permissions for users and groups on a table.

  To configure the root user to have this permission, add the following to your Datasette configuration:

  ```yaml
  permissions:
    datasette-acl:
      id: root
  ```
  Alternatively you can start Datasette running like this:
  ```bash
  datasette mydata.db --root --internal internal.db \
    -s permissions.datasette-acl.id root
  ```

  ### User groups

  Users can be assigned to groups, and those groups can then be used to quickly assign permissions to all of those users at once.

  To manage your groups, visit `/-/acl/groups` or use the "Manage user groups" item in the Datasette application menu.

  Add users to a group by typing in their actor ID. Remove them using the remove user button.

  The page for each group includes an audit log showing changes made to that group's list of members.

  When you delete a group its members will all be removed and it will be marked as deleted. Creating a group with the same name will reuse that group's record and display its existing audit log, but will not re-add the members that were removed.

  ### Dynamic groups

  You may wish to define permission rules against groups of actors based on their actor attributes, without needing to manually add those actors to a group. This can be achieved by defining a dynamic group in the `datasette-acl` configuration.

  Dynamic groups are defined in terms of [allow blocks](https://docs.datasette.io/en/stable/authentication.html#defining-permissions-with-allow-blocks). The following configuration defines two dynamic groups - one called `admin` that contains all users with `"is_admin": true` in their attributes, and another called `sales` that explicitly includes users with `"sales"` as one of the values in their `departments` array.

  ```yaml
  plugins:
    datasette-acl:
      dynamic-groups:
        admin:
          is_admin: true
        sales:
          departments: ["sales"]
  ```

  Any time an actor has their permissions checked they will be dynamically added to or removed from these groups based on the current value of their actor attributes.

  Dynamic groups are displayed in the list of groups, but their members cannot be manually added or removed.

  ### Table creator permissions

  If you allow regular users to create tables in Datasette, you may want them to maintain a level of "ownership" over those tables, such that other users are unable to modify those tables without the creator's permission.

  The `table-creator-permissions` plugin setting can be used to automatically configure permissions for the actor who created a table.

  Enable that like this:
  ```yaml
  plugins:
    datasette-acl:
      table-creator-permissions:
      - alter-table
      - drop-table
      - insert-row
      - update-row
      - delete-row
  ```
  ### Configuring autocomplete against actor IDs

  By default, users of this plugin can assign permissions to any actor ID by entering that ID, whether or not that ID corresponds to a user that exists elsewhere in the current Datasette configuration.

  If you are running this plugin in an environment with a fixed, known list of actor IDs you can implement a plugin using the `datasette_acl_valid_actors(datasette)` plugin hook which returns an iterable sequence of string actor IDs or `{"id": "actor-id", "display": "Actor Name"}` dictionaries

  These will then be used for both validation and autocomplete, ensuring users do not attach actor IDs that are not in that list.

  Example plugin implementation:
  ```python
  from datasette import hookimpl

  @hookimpl
  def datasette_acl_valid_actors(datasette):
      return ["paulo", "rohan", "simon"]
  ```
  This function can also return an async inner function, for making async calls. This example uses the `[{"id": "actor-id", "display": "Actor Name"}]` format:
  ```python
  from datasette import hookimpl

  @hookimpl
  def datasette_acl_valid_actors(datasette):
      async def inner():
          db = datasette.get_internal_database()
          return (await db.execute("select id, username as display from users")).dicts()
      return inner
  ```

  ### Python API for managing grants

  `datasette_acl.grants` provides async helpers so other plugins can read and modify grants without writing raw SQL against the ACL tables. Each call resolves the `(resource_type, parent, child)` resource, writes the `acl` rows, and appends audit entries attributed to `by_actor`.

  Provide exactly one principal (`actor_id=` or `group_id=`), and for `grant()` exactly one of `role=` or `actions=`.

  ```python
  from datasette_acl.grants import grant, revoke, update_role, list_grants
  ```

  `grant(...)` — give a principal access, by raw actions or by a role (idempotent). Returns the actions now held. Role names come from the `datasette_acl_roles` hook:

  ```python
  await grant(datasette, "table", "mydb", "mytable", actor_id="alice", actions=["insert-row"])
  await grant(datasette, "table", "mydb", "mytable", group_id=3, actions=["insert-row"], by_actor="root")
  ```

  `update_role(...)` — atomically swap a principal's actions to exactly those of a registered `role`. Returns the new actions:

  ```python
  await update_role(datasette, "doc", "doc1", actor_id="alice", role="Viewer")
  ```

  `revoke(...)` — remove all of a principal's grants on a resource. Returns the actions that were removed:

  ```python
  await revoke(datasette, "table", "mydb", "mytable", actor_id="alice")
  ```

  `list_grants(...)` — list current grants on a resource as dicts (`{"principal", "actor_id", "group_id", "group_name", "actions"}`):

  ```python
  grants = await list_grants(datasette, "table", "mydb", "mytable")
  ```

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:
  ```bash
  cd datasette-acl
  python -m venv venv
  source venv/bin/activate
  ```
  Now install the dependencies and test dependencies:
  ```bash
  pip install -e '.[test]'
  ```
  To run the tests:
  ```bash
  python -m pytest
  ```

  ### Tips for local development

  Here's how to run the plugin with all of its features enabled.

  First, grab a test database:
  ```bash
  wget https://latest.datasette.io/fixtures.db
  ```
  Install the [datasette-unsafe-actor-debug](https://github.com/datasette/datasette-unsafe-actor-debug) plugin, so you can use the `http://127.0.0.1:8001/-/unsafe-actor` page to quickly imitate any actor for testing purposes:
  ```bash
  datasette install datasette-unsafe-actor-debug
  ```
  And [datasette-visible-internal-db](https://github.com/datasette/datasette-visible-internal-db) to make it easy to see what's going on in the internal database:
  ```bash
  datasette install datasette-visible-internal-db
  ```
  Then start Datasette like this:
  ```bash
  datasette fixtures.db --internal internal.db \
    -s permissions.datasette-acl.id root \
    -s plugins.datasette-unsafe-actor-debug.enabled 1 \
    -s plugins.datasette-acl.table-creator-permissions '["insert-row", "update-row"]' \
    -s plugins.datasette-acl.dynamic-groups.staff.is_staff true \
    --root \
    --secret 1 \
    --reload
  ```
  This configures Datasette to provide a URL for you to sign in as root, which will give you access to the permission editing tool.

  It ensures that any user who creates a table (which you can test using the `/-/api` API explorer tool) will be granted initial `insert-row` and `update-row` permissions.

  It sets up a dynamic group such that any actor with `{"is_staff": true}` in their JSON will be treated as a member of that group.

  `--reload` means Datasette will reload on any code changes to the plugin, and `--secret 1` ensures your Datasette authentication cookies will continue to work across server restarts.

project_urls:
  {"CI": "https://github.com/datasette/datasette-acl/actions", "Changelog": "https://github.com/datasette/datasette-acl/releases", "Homepage": "https://github.com/datasette/datasette-acl", "Issues": "https://github.com/datasette/datasette-acl/issues"}


name: datasette-agent-charts
summary: Observable Plot charts for Datasette Agent
classifiers: ["Framework :: Datasette"]
author: Simon Willison
description_content_type: text/markdown
dynamic: ["License-File"]
license_expression: Apache-2.0
license_files: ["LICENSE"]
package_url: https://pypi.org/project/datasette-agent-charts/
project_url: https://pypi.org/project/datasette-agent-charts/
release_url: https://pypi.org/project/datasette-agent-charts/0.1a2/
requires_dist: ["datasette>=1.0a28", "llm>=0.32a2", "datasette-agent>=0.1a3"]
requires_python: >=3.10
version: 0.1a2
description:
  # datasette-agent-charts

  [![PyPI](https://img.shields.io/pypi/v/datasette-agent-charts.svg)](https://pypi.org/project/datasette-agent-charts/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-agent-charts?include_prereleases&label=changelog)](https://github.com/datasette/datasette-agent-charts/releases)
  [![Tests](https://github.com/datasette/datasette-agent-charts/actions/workflows/test.yml/badge.svg)](https://github.com/datasette/datasette-agent-charts/actions/workflows/test.yml)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-agent-charts/blob/main/LICENSE)

  Observable Plot charts for [Datasette agent](https://github.com/datasette/datasette-agent).

  ## Installation

  Install this plugin in the same environment as Datasette.
  ```bash
  datasette install datasette-agent-charts
  ```
  ## Usage

  This plugin adds a `render_chart` tool to [Datasette Agent](https://github.com/datasette/datasette-agent) that can generate charts from SQL query results using [Observable Plot](https://observablehq.com/plot/).

  Try prompts like this one, adapted to your available tables:

  > `Draw a bar chart of downloads over time`

  ## Tool definition

  The `render_chart` tool accepts the following parameters:

  - **database** — the database to query
  - **sql** — SQL query whose results become chart data
  - **chart_type** — one of `barX`, `barY`, `line`, `dot`, `areaY`, `waffleY`
  - **x** — column name for the x axis
  - **y** — column name for the y axis
  - **color** — (optional) column name for color encoding
  - **title** — (optional) chart title
  - **x_label** — (optional) x axis label
  - **y_label** — (optional) y axis label

  ## Development

  To set up this plugin locally, first checkout the code. You can confirm it is available like this:
  ```bash
  cd datasette-agent-charts
  # Confirm the plugin is visible
  uv run datasette plugins
  ```
  To run the tests:
  ```bash
  uv run pytest
  ```

project_urls:
  {"CI": "https://github.com/datasette/datasette-agent-charts/actions", "Changelog": "https://github.com/datasette/datasette-agent-charts/releases", "Homepage": "https://github.com/datasette/datasette-agent-charts", "Issues": "https://github.com/datasette/datasette-agent-charts/issues"}


name: datasette-agent-openai-imagegen
summary: Generate images in Datasette Agent
classifiers: ["Framework :: Datasette"]
author: Simon Willison
description_content_type: text/markdown
dynamic: ["License-File"]
license_expression: Apache-2.0
license_files: ["LICENSE"]
package_url: https://pypi.org/project/datasette-agent-openai-imagegen/
project_url: https://pypi.org/project/datasette-agent-openai-imagegen/
release_url: https://pypi.org/project/datasette-agent-openai-imagegen/0.1a1/
requires_dist: ["datasette>=1.0a28", "llm>=0.32a2", "datasette-agent>=0.1a0", "openai"]
requires_python: >=3.10
version: 0.1a1
description:
  # datasette-agent-openai-imagegen

  [![PyPI](https://img.shields.io/pypi/v/datasette-agent-openai-imagegen.svg)](https://pypi.org/project/datasette-agent-openai-imagegen/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-agent-openai-imagegen?include_prereleases&label=changelog)](https://github.com/datasette/datasette-agent-openai-imagegen/releases)
  [![Tests](https://github.com/datasette/datasette-agent-openai-imagegen/actions/workflows/test.yml/badge.svg)](https://github.com/datasette/datasette-agent-openai-imagegen/actions/workflows/test.yml)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-agent-openai-imagegen/blob/main/LICENSE)

  Generate images in [Datasette agent](https://github.com/datasette/datasette-agent).

  ## Installation

  Install this plugin in the same environment as Datasette.
  ```bash
  datasette install datasette-agent-openai-imagegen
  ```
  ## Usage

  Once installed Datasette Agent will have a new tool for generating images, provided an OpenAI API key has been configured for `datasette-llm`.

  The tool will default to using the faster, cheaper [gpt-image-1-mini](https://developers.openai.com/api/docs/models/gpt-image-1-mini) but can be told to use the higher quality and more expensive [gpt-image-2](https://developers.openai.com/api/docs/models/gpt-image-2) instead.

  ## Development

  To set up this plugin locally, first checkout the code. You can confirm it is available like this:
  ```bash
  cd datasette-agent-openai-imagegen
  # Confirm the plugin is visible
  uv run datasette plugins
  ```
  To run the tests:
  ```bash
  uv run pytest
  ```

project_urls:
  {"CI": "https://github.com/datasette/datasette-agent-openai-imagegen/actions", "Changelog": "https://github.com/datasette/datasette-agent-openai-imagegen/releases", "Homepage": "https://github.com/datasette/datasette-agent-openai-imagegen", "Issues": "https://github.com/datasette/datasette-agent-openai-imagegen/issues"}


name: datasette-agent-sprites
summary: Datasette Agent tools for working with Fly Sprites
classifiers: ["Framework :: Datasette"]
author: Simon Willison
description_content_type: text/markdown
dynamic: ["License-File"]
license_expression: Apache-2.0
license_files: ["LICENSE"]
package_url: https://pypi.org/project/datasette-agent-sprites/
project_url: https://pypi.org/project/datasette-agent-sprites/
release_url: https://pypi.org/project/datasette-agent-sprites/0.1a0/
requires_python: >=3.10
version: 0.1a0
description:
  # datasette-agent-sprites

  [![PyPI](https://img.shields.io/pypi/v/datasette-agent-sprites.svg)](https://pypi.org/project/datasette-agent-sprites/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-agent-sprites?include_prereleases&label=changelog)](https://github.com/datasette/datasette-agent-sprites/releases)
  [![Tests](https://github.com/datasette/datasette-agent-sprites/actions/workflows/test.yml/badge.svg)](https://github.com/datasette/datasette-agent-sprites/actions/workflows/test.yml)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-agent-sprites/blob/main/LICENSE)

  Datasette Agent tools for working with [Fly Sprites](https://sprites.dev) - persistent, hardware-isolated Linux environments with a REST API.

  ## Installation

  Install this plugin in the same environment as Datasette.
  ```bash
  datasette install datasette-agent-sprites
  ```

  ## Configuration

  This plugin uses [datasette-secrets](https://github.com/datasette/datasette-secrets) to manage the Sprites API key. You can get a token from <https://sprites.dev/account>.

  There are two ways to configure it:

  **Using an environment variable:**

  ```bash
  export DATASETTE_SECRETS_SPRITES_API_KEY="your-token-here"
  ```
  Or if you have `datasette-secrets` configured with an encrytion key:

  **Using the datasette-secrets UI:** Navigate to `/-/secrets` in your Datasette instance and set the `SPRITES_API_KEY` secret.

  ## Tools

  This plugin provides the following tools to the Datasette Agent:

  | Tool | Description |
  |------|-------------|
  | **list_sprites** | List all available Sprites. Optionally filter by name prefix. |
  | **create_sprite** | Create a new Sprite with a given name. It starts cold and warms on first use. |
  | **get_sprite** | Get details and status of a specific Sprite. |
  | **exec_on_sprite** | Execute a shell command on a Sprite via `bash -lc`. Returns stdout, stderr, and exit code. |
  | **delete_sprite** | Permanently delete a Sprite and all its data. |
  | **read_sprite_file** | Read a file from a Sprite's filesystem. Images are displayed inline; text files are returned directly. |
  | **write_sprite_file** | Write text content to a file on a Sprite. Creates parent directories automatically. |

  ## Development

  To set up this plugin locally, first checkout the code. You can confirm it is available like this:
  ```bash
  cd datasette-agent-sprites
  # Confirm the plugin is visible
  uv run datasette plugins
  ```
  To run the tests:
  ```bash
  uv run pytest
  ```

project_urls:
  {"CI": "https://github.com/datasette/datasette-agent-sprites/actions", "Changelog": "https://github.com/datasette/datasette-agent-sprites/releases", "Homepage": "https://github.com/datasette/datasette-agent-sprites", "Issues": "https://github.com/datasette/datasette-agent-sprites/issues"}

requires_dist:
  ["datasette>=1.0a28", "datasette-agent>=0.1a3", "llm>=0.32a2", "datasette-secrets>=0.3a0", "sprites-py"]


name: datasette-agent
summary: An LLM-powered agent assistant for Datasette
classifiers: ["Framework :: Datasette"]
author: Simon Willison
description_content_type: text/markdown
dynamic: ["License-File"]
license_expression: Apache-2.0
license_files: ["LICENSE"]
package_url: https://pypi.org/project/datasette-agent/
project_url: https://pypi.org/project/datasette-agent/
release_url: https://pypi.org/project/datasette-agent/0.2a0/
requires_dist: ["datasette>=1.0a30", "datasette-llm>=0.1a7", "llm>=0.32a3", "python-ulid"]
requires_python: >=3.10
version: 0.2a0
description:
  # datasette-agent

  [![PyPI](https://img.shields.io/pypi/v/datasette-agent.svg)](https://pypi.org/project/datasette-agent/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-agent?include_prereleases&label=changelog)](https://github.com/datasette/datasette-agent/releases)
  [![Tests](https://github.com/datasette/datasette-agent/actions/workflows/test.yml/badge.svg)](https://github.com/datasette/datasette-agent/actions/workflows/test.yml)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-agent/blob/main/LICENSE)

  An LLM-powered agent assistant for Datasette

  See [Datasette Agent, an extensible AI assistant for Datasette](https://datasette.io/blog/2026/datasette-agent/) for more about this project, including tips on running it on your own machine.

  ## Installation

  Install this plugin in the same environment as Datasette.
  ```bash
  datasette install datasette-agent
  ```
  ## Usage

  Visit `/-/agent` to start a conversation with the chat assistant.

  The agent uses [datasette-llm](https://github.com/datasette/datasette-llm) to call language models. Configure a default model for it before visiting `/-/agent`, for example in `datasette.yml`:

  ```yaml
  plugins:
    datasette-llm:
      default_model: gpt-5.4-mini
  ```

  The "Explore with AI agent" entries that appear in the database and table action menus launch a background agent that explores the selected database or table and writes a report. Reports live under `/-/agent/explore/`.

  Visit `/-/agent/background` to launch background agents directly. Each one is given a goal and runs toward it without further input. The listing includes a Stop button for cancelling agents that are still running.

  ### Saving queries

  The agent has a built-in `save_query` tool that saves SQL it has written as a [Datasette stored query](https://docs.datasette.io/en/latest/sql_queries.html). The query can be read-only or write SQL - Datasette analyzes it to decide which, and named `:parameters` become form fields on the saved query page.

  Saving always requires human approval: the agent shows you the full SQL plus the proposed name, database and visibility, and nothing is stored until you click Yes. Validation and persistence run through Datasette's own `/-/queries/analyze` and `/-/queries/store` endpoints as the requesting actor, so the actor needs `execute-sql` and `store-query` on the target database (plus the relevant row permissions for write queries) - the same rules as the query creation web UI. Saved queries default to private.

  ### Permissions

  This plugin registers three independent permissions:

  - `datasette-agent` — required to use the chat assistant under `/-/agent`.
  - `datasette-agent-explore` — required to see the "Explore with AI agent" entries in the database/table action menus and to use the explorer routes under `/-/agent/explore/`.
  - `datasette-agent-background` — required to use the `spawn_background_agent` and `check_background_agent` tools from chat, and to access the `/-/agent/background` page and `/-/agent/api/background/*` endpoints. The background-agent endpoints require both `datasette-agent` and `datasette-agent-background`.

  The three permissions are independent: an actor may hold any subset. The `--root` user holds all of them.

  ## Registering additional tools from plugins

  Other Datasette plugins can register additional tools for the agent using the `register_agent_tools` plugin hook.

  ### Defining a tool

  Create a Datasette plugin that implements the `register_agent_tools` hook, returning a list of `AgentTool` instances:

  ```python
  from datasette import hookimpl
  from datasette_agent.tools import AgentTool


  @hookimpl
  def register_agent_tools(datasette):
      return [
          AgentTool(
              name="my_tool",
              description="Description of what this tool does, used by the LLM to decide when to call it.",
              input_schema={
                  "type": "object",
                  "properties": {
                      "query": {
                          "type": "string",
                          "description": "The query to run",
                      },
                      "style": {
                          "type": "string",
                          "enum": ["brief", "detailed"],
                          "description": "Output style",
                      },
                  },
                  "required": ["query"],
              },
              fn=my_tool_handler,
              # Optional: name a Datasette permission action that gates this tool.
              # required_permission="myplugin-write",
          ),
      ]
  ```

  ### Gating a tool with a permission

  `AgentTool` accepts an optional `required_permission: str | None` field. When set, the agent harness calls `datasette.allowed(action=required_permission, actor=actor)` for the current actor before sending the tool list to the LLM. If the actor lacks the permission, the tool is filtered out of the list — the model never sees it and cannot call it. There is no runtime "permission denied" branch your `fn` needs to handle.

  Your plugin is responsible for registering the action via Datasette's `register_actions` plugin hook:

  ```python
  from datasette import hookimpl
  from datasette.permissions import Action
  from datasette_agent.tools import AgentTool


  @hookimpl
  def register_actions():
      return [
          Action(
              name="myplugin-write",
              description="Allow my plugin's write tools",
          ),
      ]


  @hookimpl
  def register_agent_tools(datasette):
      return [
          AgentTool(
              name="my_write_tool",
              description="Writes things",
              input_schema={"type": "object", "properties": {}},
              fn=my_write_handler,
              required_permission="myplugin-write",
          ),
      ]
  ```

  For a working example see this plugin's own `spawn_background_agent` and `check_background_agent` tools, which use `required_permission="datasette-agent-background"`.

  ### Tool handler function

  Each tool's `fn` must be an async function that accepts `datasette` and `actor` as keyword arguments, plus any parameters defined in `input_schema`. It must return a JSON string:

  ```python
  import json


  async def my_tool_handler(datasette, actor, query, style=None):
      # Do work here...
      return json.dumps({
          "result": "Tool output that the LLM will see",
      })
  ```

  To render rich HTML inline in the chat UI, include an `_html` key in the returned JSON. Any top-level key whose name starts with `_` is removed before the tool result is sent to the LLM, so the HTML is shown to the user but not passed back to the model:

  ```python
  return json.dumps({
      "_html": '<div class="my-widget">Rich content here</div>',
      "summary": "Widget rendered successfully",
  })
  ```

  ### Asking the user questions from a tool

  A tool can pause mid-execution and ask the human user a question. Declare a `context` parameter on your handler and call `await context.ask_user(...)`:

  ```python
  import json


  async def edit_files(datasette, actor, context, path):
      ok = await context.ask_user(
          "Is it OK to edit files in {}?".format(path)
      )
      if not ok:
          return json.dumps({"cancelled": True})
      mode = await context.ask_user(
          "How should I apply this?", options=["dry-run", "apply"]
      )
      note = await context.ask_user("Any notes?", free_text=True)
      # ... do the work ...
      return json.dumps({"edited": path, "mode": mode, "note": note})
  ```

  Three kinds of question are supported:

  - `await context.ask_user("Approve?")` - yes/no, returns a `bool`
  - `await context.ask_user("Which?", options=["a", "b"])` - multiple choice, returns the selected `str`
  - `await context.ask_user("Describe it", free_text=True)` - freeform, returns a `str`

  Pass `html=` to display trusted HTML above the question - use this to show the user exactly what they are approving, for example the full SQL of a query inside a `<pre>` tag. Escape any interpolated content yourself (e.g. with `html.escape()`); the string is rendered as-is in the chat UI.

  When `ask_user()` has no answer yet it suspends the agent turn: the question is rendered as a form in the chat UI, and persisted to the internal database so it survives a server restart - the form re-renders when the conversation page is reloaded. Once the user answers, the tool function is **re-executed from the top**; previously answered questions return their stored answers immediately and execution proceeds past the `ask_user()` call. Because of this replay model you should call `ask_user()` *before* performing side effects.

  The `context` object also exposes `context.actor`, `context.conversation_id`, `context.tool_name`, `context.arguments` and `context.tool_call_id`.

  In contexts with no human watching - background agents and `datasette agent chat` on the CLI - `ask_user()` raises `QuestionsNotSupported`, which surfaces to the model as a tool error so it can proceed without input. Tools that only declare `datasette` and `actor` are unaffected by all of this.

  ## Rendering custom HTML from tools

  Tool plugins can render rich HTML inline in the chat UI by returning a JSON object with an `_html` key. The HTML is rendered directly in the conversation. The remaining keys are returned to the LLM as the tool result, with any key whose name starts with `_` removed first.

  Example tool implementation:

  ```python
  import json

  async def _render_widget(datasette, actor, database, sql):
      html = (
          '<script src="/-/static-plugins/my-plugin/widget.js" type="module"></script>\n'
          '<my-widget>\n'
          f'<script type="application/json">{json.dumps({"database": database, "sql": sql})}</script>\n'
          '</my-widget>'
      )
      return json.dumps({
          "_html": html,
          "database": database,
          "sql": sql,
          "summary": "Widget rendered successfully",
      })
  ```

  The `_html` value is inserted into the chat as raw HTML, so it can include custom elements, scripts, and styles. The other keys (`database`, `sql`, and `summary` in this example) are what the LLM receives as the tool result.

  If your plugin runs SQL and displays the results in HTML, add a link below the rendered output using Datasette Agent's built-in SQL link styling:

  ```html
  <p class="agent-sql-edit-link"><a href="/data/-/query?sql=select+1">View SQL query</a></p>
  ```

  ### Example plugins

  - [datasette-agent-charts](https://github.com/datasette/datasette-agent-charts) - renders charts from SQL query results using Observable Plot
  - [datasette-agent-openai-imagegen](https://github.com/datasette/datasette-agent-openai-imagegen) - generates images using OpenAI's image generation API

  ## CLI commands

  ### Interactive chat

  Start an interactive chat session with the agent from the command line:

  ```bash
  datasette agent chat mydata.db
  ```

  You can pass multiple database files, use `:memory:` for an in-memory database, specify a model, or send a single prompt:

  ```bash
  datasette agent chat mydata.db -m gpt-5.4-mini
  datasette agent chat mydata.db -m gpt-5.4-mini -p "List all tables"
  ```

  Options:

  - `-p`, `--prompt` — Send a single prompt and exit (non-interactive mode)
  - `-m`, `--model` — LLM model to use

  ### Listing available tools

  To see all registered agent tools, grouped by plugin:

  ```bash
  datasette agent tools
  ```

  Output:

  ```
  agent:
    list_databases_and_tables
      List all available databases and their tables
    describe_table
      Get column names, types, and foreign keys for a table
    sql_query
      Execute a read-only SQL query against a database
  ```

  Add `--json` for machine-readable output:

  ```bash
  datasette agent tools --json
  ```

  ## Development

  To set up this plugin locally, first checkout the code. Run the tests like this:
  ```bash
  cd datasette-agent
  uv run pytest
  ```
  To run the development server with a persistent internal database and GPT-5.5 as the model:
  ```bash
  uv run datasette --internal internal.db \
    --root --secret 1 \
    -s plugins.datasette-llm.default_model gpt-5.5
  ```
  Add extra database files to that command to enable the agent to query them.

  ## Credits

  This plugin vendors [streaming-markdown](https://github.com/thetarnav/streaming-markdown) by Damian Tarnawski, MIT licensed.

project_urls:
  {"CI": "https://github.com/datasette/datasette-agent/actions", "Changelog": "https://github.com/datasette/datasette-agent/releases", "Homepage": "https://github.com/datasette/datasette-agent", "Issues": "https://github.com/datasette/datasette-agent/issues"}


name: datasette-alerts
summary: Setup alerts on specfic new records.
classifiers: ["Framework :: Datasette"]
author: Alex Garcia
description_content_type: text/markdown
dynamic: ["License-File"]
license_expression: Apache-2.0
license_files: ["LICENSE"]
package_url: https://pypi.org/project/datasette-alerts/
project_url: https://pypi.org/project/datasette-alerts/
release_url: https://pypi.org/project/datasette-alerts/0.0.1a10/
requires_python: >=3.10
version: 0.0.1a10
description:
  # datasette-alerts

  [![PyPI](https://img.shields.io/pypi/v/datasette-alerts.svg)](https://pypi.org/project/datasette-alerts/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-alerts?include_prereleases&label=changelog)](https://github.com/datasette/datasette-alerts/releases)
  [![Tests](https://github.com/datasette/datasette-alerts/actions/workflows/test.yml/badge.svg)](https://github.com/datasette/datasette-alerts/actions/workflows/test.yml)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-alerts/blob/main/LICENSE)

  Alerts and notifications for Datasette. Supports row-based alerts (new rows in a table) and custom alert types defined by plugins.

  ## How It Works

  datasette-alerts uses [datasette-cron](https://github.com/datasette/datasette-cron) for scheduling. When an alert is created, a cron task is registered that periodically checks for new data and sends notifications through configured destinations.

  **Built-in alert types:**
  - **Cursor alerts** — poll a table for rows newer than a timestamp cursor
  - **Trigger alerts** — SQLite INSERT trigger queues new rows for processing

  **Custom alert types** — plugins can register their own alert logic via the `datasette_alerts_register_alert_types` hook.

  ## Notifier Plugins

  | Plugin | Description |
  | ------ | ----------- |
  | [`datasette-alerts-slack`](https://github.com/datasette/datasette-alerts-slack) | Send Slack messages |
  | [`datasette-alerts-discord`](https://github.com/datasette/datasette-alerts-discord) | Send Discord messages |
  | [`datasette-alerts-ntfy`](https://github.com/datasette/datasette-alerts-ntfy) | Send ntfy.sh notifications |
  | [`datasette-alerts-desktop`](https://github.com/datasette/datasette-alerts-desktop) | Local desktop notifications |

  ## Writing a Notifier Plugin

  Notifier plugins implement the `datasette_alerts_register_notifiers` hook and return subclasses of `Notifier`.

  ```python
  from datasette import hookimpl
  from datasette_alerts import Notifier, Message
  from wtforms import Form, StringField


  @hookimpl
  def datasette_alerts_register_notifiers(datasette):
      return [MyNotifier()]


  class MyNotifier(Notifier):
      slug = "my-notifier"
      name = "My Notifier"
      description = "Send alerts somewhere"

      async def get_config_form(self):
          class ConfigForm(Form):
              webhook_url = StringField("Webhook URL")
          return ConfigForm

      async def send(self, config: dict, message: Message):
          url = config["webhook_url"]
          # deliver message.text (and optionally message.subject)
  ```

  ### Notifier API

  #### `Notifier` (abstract base class)

  | Property / Method | Description |
  | ----------------- | ----------- |
  | `slug` | Unique identifier (required) |
  | `name` | Display name (required) |
  | `description` | Short description |
  | `icon` | SVG string for the UI |
  | `get_config_form()` | Return a WTForms `Form` class for destination config |
  | `get_config_element()` | Return a `ConfigElement` for web component config UI |
  | `send(config, message)` | Deliver a `Message` to the destination described by `config` |

  #### `Message`

  ```python
  Message(text: str, *, subject: str | None = None)
  ```

  ## Writing a Custom Alert Type

  Custom alert types let plugins define their own checking logic. datasette-alerts handles the scheduling, notification delivery, and logging.

  ### Minimal Example

  ```python
  from datasette import hookimpl
  from datasette_alerts import AlertType, Message


  class MyAlertType(AlertType):
      slug = "my-check"
      name = "My Custom Check"
      description = "Checks something and alerts when it finds results"

      async def check(self, datasette, alert_config, database_name, last_check_at):
          db = datasette.get_database(database_name)
          # Your custom logic here — query tables, check conditions, etc.
          result = await db.execute("SELECT * FROM my_queue WHERE status = 'pending'")
          
          messages = []
          ids = []
          for row in result.rows:
              ids.append(row[0])
              messages.append(Message(f"New item: {row[1]}"))
          
          # Mark as processed
          if ids:
              placeholders = ",".join("?" for _ in ids)
              await db.execute_write(
                  f"UPDATE my_queue SET status = 'done' WHERE id IN ({placeholders})",
                  ids,
              )
          
          return messages


  @hookimpl
  def datasette_alerts_register_alert_types(datasette):
      return [MyAlertType()]
  ```

  ### AlertType API

  #### `AlertType` (abstract base class)

  | Property / Method | Description |
  | ----------------- | ----------- |
  | `slug` | Unique identifier (required) |
  | `name` | Display name (required) |
  | `description` | Short description |
  | `icon` | SVG string for the UI |
  | `check(datasette, alert_config, database_name, last_check_at)` | Run check logic, return `list[Message]` |
  | `get_config_element()` | Optional web component for alert configuration UI |
  | `get_config_form()` | Optional WTForms form for configuration |

  #### `check()` Method

  ```python
  async def check(
      self,
      datasette,           # Datasette instance
      alert_config: dict,  # from the alert's custom_config column
      database_name: str,  # which database this alert is scoped to
      last_check_at: str | None,  # ISO timestamp of last check, or None on first run
  ) -> list[Message]:
  ```

  Return a list of `Message` objects to send. Empty list means nothing to report. The handler calls `check()` on the schedule defined by the alert's frequency, sends any returned messages through the alert's subscriptions, and updates `last_check_at`.

  ### How Custom Alerts Get Scheduled

  When a custom alert is created (via the API or a plugin's own routes):

  1. A row is inserted into `datasette_alerts_alerts` with `alert_type="custom:{slug}"`
  2. A cron task is registered with `datasette-cron` at the alert's frequency
  3. Each tick, the `custom_alert_handler` looks up the `AlertType` by slug via the plugin hook
  4. Calls `check()` with the alert's config and database
  5. Sends any returned messages to all subscriptions
  6. Updates `last_check_at` and logs the check

  ### Creating Custom Alerts Programmatically

  ```python
  from datasette_alerts.internal_db import InternalDB, NewAlertRouteParameters, NewSubscription

  internal_db = InternalDB(datasette.get_internal_database())

  params = NewAlertRouteParameters(
      database_name="mydb",
      alert_type="custom:my-check",
      frequency="+1 second",
      custom_config={"key": "value"},
      subscriptions=[
          NewSubscription(destination_id="dest-id", meta={"aggregate": True})
      ],
  )
  alert_id = await internal_db.new_alert(params)

  # Register the cron task
  from datasette_alerts import _register_cron_task_for_alert
  from types import SimpleNamespace

  await _register_cron_task_for_alert(datasette, SimpleNamespace(
      id=alert_id,
      alert_type="custom:my-check",
      frequency="+1 second",
  ))
  ```

  ## Public API

  ### `send_to_destination()`

  Send a message through a configured destination without creating an alert:

  ```python
  from datasette_alerts import send_to_destination, Message

  await send_to_destination(datasette, destination_id, Message("Hello!"))
  ```

  Raises `DestinationNotFound` or `NotifierNotFound` on errors.

  ### `trigger_alert_check()`

  Trigger an immediate check for an alert, outside its normal schedule:

  ```python
  from datasette_alerts import trigger_alert_check

  await trigger_alert_check(datasette, alert_id)
  ```

  ### API Endpoint: List Alert Types

  ```
  GET /-/{database}/datasette-alerts/api/alert-types
  ```

  Returns registered custom alert types with their slug, name, description, and config element info.

  ## Data Models

  Query results from `InternalDB` return typed dataclasses:

  ```python
  from datasette_alerts.models import AlertRecord, AlertForCheck, AlertDetail
  ```

  ### `AlertRecord`

  Returned by `get_all_alerts()`. Fields: `id`, `database_name`, `table_name`, `id_columns`, `timestamp_column`, `frequency`, `alert_type`, `custom_config`, `last_check_at`.

  ### `AlertForCheck`

  Returned by `get_alert_for_check()`. Same as `AlertRecord` plus `cursor`.

  ### `AlertDetail`

  Returned by `get_alert_detail()`. Full alert info including nested `subscriptions: list[SubscriptionDetail]` and `logs: list[AlertLogEntry]`, plus `custom_config`, `last_check_at`, `next_deadline`, `alert_created_at`.

  ### `AlertCleanupInfo`

  Returned by `delete_alert()`. Fields: `alert_type`, `database_name`, `table_name`.

  ## Config: WTForms vs Web Components

  There are two ways to provide a destination (or alert type) configuration UI:

  #### WTForms (simple)

  Override `get_config_form()` and return a WTForms `Form` class.

  #### `ConfigElement` (rich UI)

  Return a `ConfigElement` that declares a custom HTML element:

  ```python
  from datasette_alerts import ConfigElement

  def get_config_element(self):
      return ConfigElement(
          tag="my-config-form",
          scripts=["/-/my-plugin/config.js"],
      )
  ```

  Web component contract:
  - **Inputs**: `config` property, `datasette-base-url` attribute, `database-name` attribute
  - **Output**: `config-change` CustomEvent with `detail: { config: {...}, valid: boolean }`

  ## Sidebar Integration

  If [`datasette-sidebar`](https://github.com/datasette/datasette-sidebar) is installed, datasette-alerts automatically registers itself as a sidebar app.

  ## Development

  ```bash
  just dev           # start dev server
  just test          # run tests (uv run pytest)
  just format        # format code (backend + frontend)
  just check         # lint + type check (backend + frontend)
  just frontend      # build frontend
  just frontend-dev  # start Vite dev server
  ```

project_urls:
  {"CI": "https://github.com/datasette/datasette-alerts/actions", "Changelog": "https://github.com/datasette/datasette-alerts/releases", "Homepage": "https://github.com/datasette/datasette-alerts", "Issues": "https://github.com/datasette/datasette-alerts/issues"}

requires_dist:
  ["datasette>=1.0a28", "datasette-cron>=0.0.1a2", "datasette-plugin-router>=0.0.1a2", "datasette-vite>=0.0.1a4", "pydantic>=2", "python-ulid>=3", "sqlite-migrate>=0.1b0", "sqlite-utils>=3.38", "wtforms>=3.2.1"]


name: datasette-allow-permissions-debug
summary: Always allow access to /-/permissions, for debugging
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-allow-permissions-debug
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-allow-permissions-debug/
project_url: https://pypi.org/project/datasette-allow-permissions-debug/
release_url: https://pypi.org/project/datasette-allow-permissions-debug/0.1/
version: 0.1
description:
  # datasette-allow-permissions-debug

  [![PyPI](https://img.shields.io/pypi/v/datasette-allow-permissions-debug.svg)](https://pypi.org/project/datasette-allow-permissions-debug/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-allow-permissions-debug?label=changelog)](https://github.com/simonw/datasette-allow-permissions-debug/releases)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-allow-permissions-debug/blob/master/LICENSE)

  Always allow access to `/-/permissions`, for debugging.

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ pip install datasette-allow-permissions-debug

  ## Usage

  Once installed, all access to `/-/permissions` will be allowed even if you are not signed in.

project_urls:
  {"CI": "https://github.com/simonw/datasette-allow-permissions-debug/actions", "Changelog": "https://github.com/simonw/datasette-allow-permissions-debug/releases", "Homepage": "https://github.com/simonw/datasette-allow-permissions-debug", "Issues": "https://github.com/simonw/datasette-allow-permissions-debug/issues"}

requires_dist:
  ["datasette", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "httpx ; extra == 'test'"]


name: datasette-app-support
summary: Part of https://github.com/simonw/datasette-app
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-app-support
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-app-support/
project_url: https://pypi.org/project/datasette-app-support/
release_url: https://pypi.org/project/datasette-app-support/0.11.8/
requires_python: >=3.6
version: 0.11.8
description:
  # datasette-app-support

  [![PyPI](https://img.shields.io/pypi/v/datasette-app-support.svg)](https://pypi.org/project/datasette-app-support/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-app-support?include_prereleases&label=changelog)](https://github.com/simonw/datasette-app-support/releases)
  [![Tests](https://github.com/simonw/datasette-app-support/workflows/Test/badge.svg)](https://github.com/simonw/datasette-app-support/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-app-support/blob/main/LICENSE)

  Part of https://github.com/simonw/datasette-app

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ datasette install datasette-app-support

  Using this outside of the context of `Datasette.app` probably won't work.

  ## API endpoints

  This plugin exposes APIs that are called by the Electron wrapper.

  All plugins are protected by authentication: they need to be called with a `Authorization: Bearer xxx` token here the `xxx` matches the value of the `DATASETTE_API_TOKEN` environment variable.

  ### /-/auth-app-user

  ```
  POST /-/auth-app-user
  {"redirect": "/-/metadata"}
  ```
  If a valid `Authorization` header is passed, sets a signed cookie identifying the user as `{"id": "admin"}` and redirects them to the specified page.

  ### /-/open-database-file

  ```
  POST /-/open-database-file
  {"path": "/path/to/file.db"}
  ```
  Attaches a new database file to the running Datasette instance - used by the "Open Database..." menu option.

  Returns HTTP 200 status with `{"ok": True, "path": "/file"}` if it works, 400 with an `"error"` JSON string message if it fails.

  ### /-/new-empty-database-file

  ```
  POST /-/new-empty-database-file
  {"path": "/path/to/file.db"}
  ```
  Creates a brand new empty SQLite database file at the specified path and attaches it to the Datasette instance. Used by the "Create Empty Database..." menu option.

  Returns HTTP 200 status with `{"ok": True, "path": "/file"}` if it works, 400 with an `"error"` JSON string message if it fails.

  ### /-/open-csv-file

  ```
  POST /-/open-csv-file
  {"path": "/path/to/file.csv"}
  ```
  Imports a CSV or TSV file into the default `/temporary` in-memory database. Used by the "Open CSV..." menu option.

  Returns HTTP 200 status with `{"ok": True, "path": "/temporary/table"}` if it works, 400 or 500 with an `"error"` JSON string message if it fails.

  ### /-/import-csv-file

  ```
  POST /-/import-csv-file
  {"path": "/path/to/file.csv", "database": "database_name"}
  ```
  Permanently imports a CSV or TSV file into the specified database. Used by the "Import CSV..." menu option.

  Returns HTTP 200 status with `{"ok": True, "path": "/database_name/table"}` if it works, 400 or 500 with an `"error"` JSON string message if it fails.

  ### /-/open-csv-from-url

  ```
  POST /-/open-csv-from-url
  {"url": "https://example.com/file.csv", "table_name": "My_suggested_table_name"}
  ```
  Imports a CSV file into the default `/temporary` in-memory database. Used by the "Open CSV from URL..." menu option. `table_name` is optional - if omitted the name to use will be derived from the URL.

  Returns HTTP 200 status with `{"ok": True, "path": "/temporary/table"}` if it works, 400 or 500 with an `"error"` JSON string message if it fails.

  ### /-/dump-temporary-to-file

  ```
  POST /-/dump-temporary-to-file
  {"path": "/path/to/backup.db"}
  ```
  Dumps the contents of the `temporary` in-memory database to the specified file. This is intended to be used to take a temporary backup when the Datasette server is restarted after a plugin has been installed, see [datasette-app/issues/42](https://github.com/simonw/datasette-app/issues/42).

  Returns HTTP 200 status with `{"ok": True, "path": "/path/to/backup.db"}` if it works, 400 or 500 with an `"error"` JSON string message if it fails.

  ### /-/restore-temporary-to-file

  ```
  POST /-/restore-temporary-from-file
  {"path": "/path/to/backup.db"}
  ```
  Restores the `temporary` in-memory database to the contents of the specified file.

  Returns HTTP 200 status with `{"ok": True, "path": "/path/to/backup.db"}` if it works, 400 or 500 with an `"error"` JSON string message if it fails.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-app-support
      python3 -mvenv venv
      source venv/bin/activate

  Or if you are using `pipenv`:

      pipenv shell

  Now install the dependencies and test dependencies:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-app-support/actions", "Changelog": "https://github.com/simonw/datasette-app-support/releases", "Homepage": "https://github.com/simonw/datasette-app-support", "Issues": "https://github.com/simonw/datasette-app-support/issues"}

requires_dist:
  ["datasette (>=0.59)", "sqlite-utils", "packaging", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "black ; extra == 'test'", "pytest-httpx ; extra == 'test'"]


name: datasette-atom
summary: Datasette plugin that adds a .atom output format
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-atom
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-atom/
project_url: https://pypi.org/project/datasette-atom/
release_url: https://pypi.org/project/datasette-atom/0.9/
version: 0.9
description:
  # datasette-atom

  [![PyPI](https://img.shields.io/pypi/v/datasette-atom.svg)](https://pypi.org/project/datasette-atom/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-atom?include_prereleases&label=changelog)](https://github.com/simonw/datasette-atom/releases)
  [![Tests](https://github.com/simonw/datasette-atom/workflows/Test/badge.svg)](https://github.com/simonw/datasette-atom/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-atom/blob/main/LICENSE)

  Datasette plugin that adds support for generating [Atom feeds](https://validator.w3.org/feed/docs/atom.html) with the results of a SQL query.

  ## Installation

  Install this plugin in the same environment as Datasette to enable the `.atom` output extension.

      $ pip install datasette-atom

  ## Usage

  To create an Atom feed you need to define a custom SQL query that returns a required set of columns:

  * `atom_id` - a unique ID for each row. [This article](https://web.archive.org/web/20080211143232/http://diveintomark.org/archives/2004/05/28/howto-atom-id) has suggestions about ways to create these IDs.
  * `atom_title` - a title for that row.
  * `atom_updated` - an [RFC 3339](http://www.faqs.org/rfcs/rfc3339.html) timestamp representing the last time the entry was modified in a significant way. This can usually be the time that the row was created.

  The following columns are optional:

  * `atom_content` - content that should be shown in the feed. This will be treated as a regular string, so any embedded HTML tags will be escaped when they are displayed.
  * `atom_content_html` - content that should be shown in the feed. This will be treated as an HTML string, and will be sanitized using [Bleach](https://github.com/mozilla/bleach) to ensure it does not have any malicious code in it before being returned as part of a `<content type="html">` Atom element. If both are provided, this will be used in place of `atom_content`.
  * `atom_link` - a URL that should be used as the link that the feed entry points to.
  * `atom_author_name` - the name of the author of the entry. If you provide this you can also provide `atom_author_uri` and `atom_author_email` with a URL and e-mail address for that author.

  A query that returns these columns can then be returned as an Atom feed by adding the `.atom` extension.

  ## Example

  Here is an example SQL query which generates an Atom feed for new entries on [www.niche-museums.com](https://www.niche-museums.com/):

  ```sql
  select
    'tag:niche-museums.com,' || substr(created, 0, 11) || ':' || id as atom_id,
    name as atom_title,
    created as atom_updated,
    'https://www.niche-museums.com/browse/museums/' || id as atom_link,
    coalesce(
      '<img src="' || photo_url || '?w=800&amp;h=400&amp;fit=crop&amp;auto=compress">',
      ''
    ) || '<p>' || description || '</p>' as atom_content_html
  from
    museums
  order by
    created desc
  limit
    15
  ```

  You can try this query by [pasting it in here](https://www.niche-museums.com/browse) - then click the `.atom` link to see it as an Atom feed.

  ## Using a canned query

  Datasette's [canned query mechanism](https://docs.datasette.io/en/stable/sql_queries.html#canned-queries) is a useful way to configure feeds. If a canned query definition has a `title` that will be used as the title of the Atom feed.

  Here's an example, defined using a `metadata.yaml` file:

  ```yaml
  databases:
    browse:
      queries:
        feed:
          title: Niche Museums
          sql: |-
            select
              'tag:niche-museums.com,' || substr(created, 0, 11) || ':' || id as atom_id,
              name as atom_title,
              created as atom_updated,
              'https://www.niche-museums.com/browse/museums/' || id as atom_link,
              coalesce(
                '<img src="' || photo_url || '?w=800&amp;h=400&amp;fit=crop&amp;auto=compress">',
                ''
              ) || '<p>' || description || '</p>' as atom_content_html
            from
              museums
            order by
              created desc
            limit
              15
  ```
  ## Disabling HTML filtering

  The HTML allow-list used by Bleach for the `atom_content_html` column can be found in the `clean(html)` function at the bottom of [datasette_atom/__init__.py](https://github.com/simonw/datasette-atom/blob/main/datasette_atom/__init__.py).

  You can disable Bleach entirely for Atom feeds generated using a canned query. You should only do this if you are certain that no user-provided HTML could be included in that value.

  Here's how to do that in `metadata.json`:

  ```json
  {
    "plugins": {
      "datasette-atom": {
        "allow_unsafe_html_in_canned_queries": true
      }
    }
  }
  ```
  Setting this to `true` will disable Bleach filtering for all canned queries across all databases.

  You can disable Bleach filtering just for a specific list of canned queries like so:

  ```json
  {
    "plugins": {
      "datasette-atom": {
        "allow_unsafe_html_in_canned_queries": {
          "museums": ["latest", "moderation"]
        }
      }
    }
  }
  ```
  This will disable Bleach just for the canned queries called `latest` and `moderation` in the `museums.db` database.

project_urls:
  {"CI": "https://github.com/simonw/datasette-atom/actions", "Changelog": "https://github.com/simonw/datasette-atom/releases", "Homepage": "https://github.com/simonw/datasette-atom", "Issues": "https://github.com/simonw/datasette-atom/issues"}

requires_dist:
  ["datasette (>=0.49)", "feedgen", "bleach", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "httpx ; extra == 'test'"]


name: datasette-auth-existing-cookies
summary: Datasette plugin that authenticates users based on existing domain cookies
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-auth-existing-cookies
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-auth-existing-cookies/
project_url: https://pypi.org/project/datasette-auth-existing-cookies/
project_urls: {"Homepage": "https://github.com/simonw/datasette-auth-existing-cookies"}
release_url: https://pypi.org/project/datasette-auth-existing-cookies/0.7/
requires_python: >=3.6
version: 0.7
description:
  # datasette-auth-existing-cookies

  [![PyPI](https://img.shields.io/pypi/v/datasette-auth-existing-cookies.svg)](https://pypi.org/project/datasette-auth-existing-cookies/)
  [![CircleCI](https://circleci.com/gh/simonw/datasette-auth-existing-cookies.svg?style=svg)](https://circleci.com/gh/simonw/datasette-auth-existing-cookies)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://google.com/simonw/datasette-auth-existing-cookies/blob/master/LICENSE)

  Datasette plugin that authenticates users based on existing domain cookies.

  ## When to use this

  This plugin allows you to build custom authentication for Datasette when you are hosting a Datasette instance on the same domain as another, authenticated website.

  Consider a website on `www.example.com` which supports user authentication.

  You could run Datasette on `data.example.com` in a way that lets it see cookies that were set for the `.example.com` domain.

  Using this plugin, you could build an API endpoint at `www.example.com/user-for-cookies` which returns a JSON object representing the currently signed-in user, based on their cookies.

  The plugin can protect any hits to any `data.example.com` pages by passing their cookies through to that API and seeing if the user should be logged in or not.

  You can also use subclassing to decode existing cookies using some other mechanism.

  ## Configuration

  This plugin requires some configuration in the Datasette [metadata.json file](https://datasette.readthedocs.io/en/stable/plugins.html#plugin-configuration).

  It needs to know the following:

  * Which domain cookies should it be paying attention to? If you are authenticating against Dango this is probably `["sessionid"]`.
  * What's an API it can send the incoming cookies to that will decipher them into some user information?
  * Where should it redirect the user if they need to sign in?

  Example configuration setting all three of these values looks like this:

  ```json
  {
      "plugins": {
          "datasette-auth-existing-cookies": {
              "api_url": "http://www.example.com/user-from-cookies",
              "auth_redirect_url": "http://www.example.com/login",
              "original_cookies": ["sessionid"]
          }
      }
  }
  ```

  With this configuration the user's current `sessionid` cookie will be passed to the API URL, as a regular cookie header.

  You can use the `"headers_to_forward"` configuration option to specify a list of additional headers from the request that should be forwarded on to the `api_url` as querystring parameters. For example, if you add this to the above configuration:

  ```json
              "headers_to_forward": ["host", "x-forwarded-for"]
  ```

  Then a hit to `https://data.example.com/` would make the following API call:

      http://www.example.com/user-from-cookies?host=data.example.com&x-forwarded-for=64.18.15.255

  The API URL should then return either an empty JSON object if the user is not currently signed in:

  ```json
  {}
  ```

  Or a JSON object representing the user if they ARE signed in:

  ```json
  {
      "id": 123,
      "username": "simonw"
  }
  ```

  This object can contain any keys that you like - the information will be stored in a new signed cookie and made available to Datasette code as the `"auth"` dictionary on the ASGI `scope`.

  I suggest including at least an `id` and a `username`.

  ## Templates

  You probably want your user's to be able to see that they are signed in. The plugin makes the `auth` data from above directly available within Datasette's templates. You could use a custom `base.html` template (see [template documentation](https://datasette.readthedocs.io/en/stable/custom_templates.html#custom-templates)) that looks like this:

  ```html+django
  {% extends "default:base.html" %}

  {% block extra_head %}
  <style type="text/css">
  .hd .logout {
      float: right;
      text-align: right;
      padding-left: 1em;
  }
  </style>
  {% endblock %}

  {% block nav %}
      {{ super() }}
      {% if auth and auth.username %}
          <p class="logout">
              <strong>{{ auth.username }}</strong> &middot; <a href="https://www.example.com/logout">Log out</a>
          </p>
      {% endif %}
  {% endblock %}
  ```

  ## Other options

  - `require_auth`. This defaults to `True`. You can set it to `False` if you want unauthenticated users to be able to view the Datasette instance.
  - `cookie_secret`. You can use this to set the signing secret that will be used for the cookie set by this plugin (you should use [secret configuration values](https://datasette.readthedocs.io/en/stable/plugins.html#secret-configuration-values) for this). If you do not set a secret the plugin will create one on first run and store it in an appropriate state directory based on your operating system (the `user_state_dir` according to [appdirs](https://pypi.org/project/appdirs/)).
  - `cookie_ttl`. The plugin sets its own cookie to avoid hitting the backend API for every incoming request. By default it still hits the API at most every 10 seconds, in case the user has signed out on the main site. You can raise or lower the timeout using this setting.
  - `trust_x_forwarded_proto`. If you are running behind a proxy that adds HTTPS support for you, you may find that the plugin incorrectly constructs `?next=` URLs with the incorrect scheme. If you know your proxy sends the `x-forwarded-proto` header (you can investigate this with the [datasette-debug-asgi](https://github.com/simonw/datasette-debug-asgi) plugin) setting the `trust_x_forwarded_proto` option to True will cause the plugin to trust that header.
  - `next_secret`. See below.

  ## Login redirect mechanism

  If the user does not have a valid authentication cookie they will be redirected to an existing login page.

  This page is specified using the `auth_redirect_url` setting.

  Given the above example configuration, the URL that the user should be sent to after they log in will be specified as the `?next=` parameter to that page, for example:

      http://www.example.com/login?next=http://foo.example.com/

  It is up to you to program the login endpoint such that it is not vulnerable to an [Unvalidated redirect vulnerability](https://cheatsheetseries.owasp.org/cheatsheets/Unvalidated_Redirects_and_Forwards_Cheat_Sheet.html).

  One way to do this is by verifying that the URL passed to `?next=` is a URL that belongs to a trusted website. Django's own login view [does this](https://github.com/django/django/blob/50cf183d219face91822c75fa0a15fe2fe3cb32d/django/contrib/auth/views.py#L69-L80) by verifying that the URL hostname is on an approved list.

  Another way to do this is to use the `next_secret` configuration parameter to set a signing secret for that URL. This signing secret will be used to construct a `?next_sig=` signed token using the Python [itsdangerous](https://pythonhosted.org/itsdangerous/) module, like this:

      ?next_sig=Imh0dHBzOi8vZGVtby5leGFtcGxlLmNvbS9mb28vYmFyIg.7JdhRCoP7Ow1cRF1ZVengC-qk6c

  You should use Datasette's [secret configuration values](https://datasette.readthedocs.io/en/stable/plugins.html#secret-configuration-values) mechanism to set this secret from an environment variable, like so:

      {
          "plugins": {
              "datasette-auth-existing-cookies": {
                  "api_url": "http://www.example.com/user-from-cookies",
                  "auth_redirect_url": "http://www.example.com/login",
                  "original_cookies": ["sessionid"],
                  "next_secret":  {
                      "$env": "NEXT_SECRET"
                  }
              }
          }
      }

  You can verify this secret in Python code for your own login form like so:

  ```python
  from itsdangerous import URLSafeSerializer, BadSignature

  def verify_next_sig(next_sig):
      signer = URLSafeSerializer(next_secret)
      try:
          decoded = signer.loads(next_sig)
          return True
      except BadSignature:
          return False
  ```

  If you want to roll your own signing mechanism here you can do so by subclassing `ExistingCookiesAuth` and over-riding the `build_auth_redirect(next_url)` method.

  ## Permissions

  If the current user is signed in but should not have permission to access the Datasette instance, you can indicate so by having the API return the following:

  ```json
  {
      "forbidden": "You do not have permission to access this page."
  }
  ```

  The key must be `"forbidden"`. The value can be any string - it will be displayed to the user.

  This is particularly useful when handling multiple different subdomains. You may get an API call to the following:

      http://www.example.com/user-from-cookies?host=a-team.example.com

  You can check if the authenticated user (based on their cookies) has permission to access to the `a-team` Datasette instance, and return a `"forbidden"` JSON object if they should not be able to view it.

  If a user is allowed to access Datasette (because the API returned their user identity as JSON), the plugin will set a cookie on that subdomain granting them access.

  This cookie defaults to expiring after ten seconds. This means that if a user has permission removed for any reason they will still have up to ten seconds in which they will be able to continue accessing Datasette. If this is not acceptable to you the `cookie_ttl` setting can be used to reduce this timeout, at the expense of incurring more frequent API calls to check user permissions.

requires_dist:
  ["appdirs", "httpx", "itsdangerous", "datasette ; extra == 'test'", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "asgiref (~=3.1.2) ; extra == 'test'"]


name: datasette-auth-github
summary: Datasette plugin and ASGI middleware that authenticates users against GitHub
classifiers: []
author: Simon Willison
description_content_type: text/markdown
dynamic: ["License-File"]
license_expression: Apache-2.0
license_files: ["LICENSE"]
package_url: https://pypi.org/project/datasette-auth-github/
project_url: https://pypi.org/project/datasette-auth-github/
provides_extra: ["test"]
release_url: https://pypi.org/project/datasette-auth-github/0.14/
requires_python: >=3.10
version: 0.14
description:
  # datasette-auth-github

  [![PyPI](https://img.shields.io/pypi/v/datasette-auth-github.svg)](https://pypi.org/project/datasette-auth-github/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-auth-github?include_prereleases&label=changelog)](https://github.com/simonw/datasette-auth-github/releases)
  [![Tests](https://github.com/simonw/datasette-auth-github/workflows/Test/badge.svg)](https://github.com/simonw/datasette-auth-github/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-auth-github/blob/main/LICENSE)

  Datasette plugin that authenticates users against GitHub.

  <!-- toc -->

  - [Setup instructions](#setup-instructions)
  - [The authenticated actor](#the-authenticated-actor)
  - [Restricting access to specific users](#restricting-access-to-specific-users)
  - [Restricting access to specific GitHub organizations or teams](#restricting-access-to-specific-github-organizations-or-teams)
  - [What to do if a user is removed from an organization or team](#what-to-do-if-a-user-is-removed-from-an-organization-or-team)

  <!-- tocstop -->

  ## Setup instructions

  * Install the plugin: `datasette install datasette-auth-github`
  * Create a GitHub OAuth app: https://github.com/settings/applications/new
  * Set the Authorization callback URL to `http://127.0.0.1:8001/-/github-auth-callback`
  * Create a `metadata.json` file with the following structure:

  ```json
  {
      "title": "datasette-auth-github demo",
      "plugins": {
          "datasette-auth-github": {
              "client_id": {"$env": "GITHUB_CLIENT_ID"},
              "client_secret": {"$env": "GITHUB_CLIENT_SECRET"}
          }
      }
  }
  ```

  For a GitHub Enterprise server, add `"host": "github.example.com"` to `datasette-auth-github`.

  Now you can start Datasette like this, passing in the secrets as environment variables:

      $ GITHUB_CLIENT_ID=XXX GITHUB_CLIENT_SECRET=YYY datasette \
          fixtures.db -m metadata.json

  Note that hard-coding secrets in `metadata.json` is a bad idea as they will be visible to anyone who can navigate to `/-/metadata`. Instead, we use Datasette's mechanism for [adding secret plugin configuration options](https://docs.datasette.io/en/stable/plugins.html#secret-configuration-values).

  By default anonymous users will still be able to interact with Datasette. If you wish all users to have to sign in with a GitHub account first, add this to your ``metadata.json``:

  ```json
  {
      "allow": {
          "id": "*"
      },
      "plugins": {
          "datasette-auth-github": {
              "...": "..."
          }
      }
  }
  ```
  ## The authenticated actor

  Visit `/-/actor` when signed in to see the shape of the authenticated actor. It should look something like this:

  ```json
  {
      "actor": {
          "id": "github:9599",
          "display": "simonw",
          "gh_id": "9599",
          "gh_name": "Simon Willison",
          "gh_login": "simonw",
          "gh_email": "...",
          "gh_orgs": [
              "dogsheep",
              "datasette-project"
          ],
          "gh_teams": [
              "dogsheep/test"
          ]
      }
  }
  ```

  The `gh_orgs` and `gh_teams` properties will only be present if you used `load_teams` or `load_orgs`, documented below.

  ## Restricting access to specific users

  You can use Datasette's [permissions mechanism](https://docs.datasette.io/en/stable/authentication.html) to specify which user or users are allowed to access your instance. Here's how to restrict access to just GitHub user `simonw`:

  ```json
  {
      "allow": {
          "gh_login": "simonw"
      },
      "plugins": {
          "datasette-auth-github": {
              "...": "..."
          }
      }
  }
  ```

  This `"allow"` block can be positioned at the database, table or query level instead: see [Configuring permissions in metadata.json](https://docs.datasette.io/en/stable/authentication.html#configuring-permissions-in-metadata-json) for details.

  Note that GitHub allows users to change their username, and it is possible for other people to claim old usernames. If you are concerned that your users may change their usernames you can key the allow blocks against GitHub user IDs instead, which do not change:

  ```json
  {
      "allow": {
          "id": "github:9599"
      }
  }
  ```

  ## Restricting access to specific GitHub organizations or teams

  You can also restrict access to users who are members of a specific GitHub organization.

  You'll need to configure the plugin to check if the user is a member of that organization when they first sign in. You can do that using the `"load_orgs"` plugin configuration option.

  Then you can use `"allow": {"gh_orgs": [...]}` to specify which organizations are allowed access.

  ```json
  {
      "plugins": {
          "datasette-auth-github": {
              "...": "...",
              "load_orgs": ["your-organization"]
          }
      },
      "allow": {
          "gh_orgs": "your-organization"
      }
  }
  ```

  If your organization is [arranged into teams](https://help.github.com/en/articles/organizing-members-into-teams) you can restrict access to a specific team like this:

  ```json
  {
      "plugins": {
          "datasette-auth-github": {
              "...": "...",
              "load_teams": [
                  "your-organization/staff",
                  "your-organization/engineering",
              ]
          }
      },
      "allows": {
          "gh_team": "your-organization/engineering"
      }
  }
  ```

  ## What to do if a user is removed from an organization or team

  A user's organization and team memberships are checked once, when they first sign in. Those teams and organizations are then persisted in the user's signed `ds_actor` cookie.

  This means that if a user is removed from an organization or team but still has a Datasette cookie, they will still be able to access that Datasette instance.

  You can remedy this by rotating the `DATASETTE_SECRET` environment variable any time you make changes to your GitHub organization members.

  Changing this value will cause all of your existing users to be signed out, by invalidating their cookies. When they sign back in again their new memberships will be recorded in a new cookie.

  See [Configuring the secret](https://docs.datasette.io/en/stable/settings.html?highlight=secret#configuring-the-secret) in the Datasette documentation for more details.

project_urls:
  {"Changelog": "https://github.com/simonw/datasette-auth-github/releases", "Homepage": "https://github.com/simonw/datasette-auth-github", "Issues": "https://github.com/simonw/datasette-auth-github/issues"}

requires_dist:
  ["datasette", "pytest; extra == \"test\"", "pytest-asyncio; extra == \"test\"", "sqlite-utils; extra == \"test\"", "pytest-httpx; extra == \"test\""]


name: datasette-auth-passwords
summary: Datasette plugin for authenticating access using passwords
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-auth-passwords
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-auth-passwords/
project_url: https://pypi.org/project/datasette-auth-passwords/
provides_extra: ["test"]
release_url: https://pypi.org/project/datasette-auth-passwords/1.1.1/
version: 1.1.1
description:
  # datasette-auth-passwords

  [![PyPI](https://img.shields.io/pypi/v/datasette-auth-passwords.svg)](https://pypi.org/project/datasette-auth-passwords/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-auth-passwords?label=changelog)](https://github.com/simonw/datasette-auth-passwords/releases)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-auth-passwords/blob/master/LICENSE)

  Datasette plugin for authenticating access using passwords

  ## Installation

  Install this plugin in the same environment as Datasette.

  ```bash
  datasette install datasette-auth-passwords
  ```

  ## Demo

  A demo of this plugin is running at https://datasette-auth-passwords-demo.datasette.io/

  The demo is configured to show the `public.db` database to everyone, but the `private.db` database only to logged in users.

  You can log in at https://datasette-auth-passwords-demo.datasette.io/-/login with username `root` and password `password!`.

  ## Usage

  This plugin works based on a list of username/password accounts that are hard-coded into the plugin configuration.

  First, you'll need to create a password hash. There are three ways to do that:

  - Install the plugin, then use the interactive tool located at `/-/password-tool`
  - Use the hosted version of that tool at https://datasette-auth-passwords-demo.datasette.io/-/password-tool
  - Use the `datasette hash-password` command, described below

  Now add the following to your `metadata.json`:

  ```json
  {
      "plugins": {
          "datasette-auth-passwords": {
              "someusername_password_hash": {
                  "$env": "PASSWORD_HASH_1"
              }
          }
      }
  }
  ```

  The password hash can now be specified in an environment variable when you run Datasette. You can do that like so:

      PASSWORD_HASH_1='pbkdf2_sha256$...' \
          datasette -m metadata.json

  Be sure to use single quotes here otherwise the `$` symbols in the password hash may be incorrectly interpreted by your shell.

  You will now be able to log in to your instance using the form at `/-/login` with `someusername` as the username and the password that you used to create your hash as the password.

  You can include as many accounts as you like in the configuration, each with different usernames.

  ### datasette hash-password

  The plugin exposes a new CLI command, `datasette hash-password`. You can run this without arguments to interactively create a new password hash:
  ```bash
  datasette hash-password
  ```
  ```
  Password: 
  Repeat for confirmation: 
  pbkdf2_sha256$260000$1513...
  ```
  Or if you want to use it as part of a script, you can add the `--no-confirm` option to generate a hash directly from a value passed to standard input:
  ```bash
  echo 'my password' | datasette hash-password --no-confirm
  ```
  ```
  pbkdf2_sha256$260000$daa...
  ```
  ### Specifying actors

  By default, a logged in user will result in an [actor block](https://datasette.readthedocs.io/en/stable/authentication.html#actors) that just contains their username:

  ```json
  {
      "id": "someusername"
  }
  ```

  You can customize the actor that will be used for a username by including an `"actors"` configuration block, like this:

  ```json
  {
      "plugins": {
          "datasette-auth-passwords": {
              "someusername_password_hash": {
                  "$env": "PASSWORD_HASH_1"
              },
              "actors": {
                  "someusername": {
                      "id": "someusername",
                      "name": "Some user"
                  }
              }
          }
      }
  }
  ```
  ### HTTP Basic authentication option

  This plugin defaults to implementing login using an HTML form that sets a signed authentication cookie.

  You can alternatively configure it to use [HTTP Basic authentication](https://developer.mozilla.org/en-US/docs/Web/HTTP/Authentication#basic_authentication_scheme) instead.

  Do this by adding `"http_basic_auth": true` to the `datasette-auth-passwords` block in your plugin configuration.

  This option introduces the following behaviour:

  - Account usernames and passwords are configured in the same way as form-based authentication
  - Every page within Datasette - even pages that normally do not use authentication, such as static assets - will display a browser login prompt
  - Users will be unable to log out without closing their browser entirely

  There is a demo of this mode at https://datasette-auth-passwords-http-basic-demo.datasette.io/ - sign in with username `root` and password `password!`

  ### Using with datasette publish

  If you are publishing data using a [datasette publish](https://datasette.readthedocs.io/en/stable/publish.html#datasette-publish) command you can use the `--plugin-secret` option to securely configure your password hashes (see [secret configuration values](https://datasette.readthedocs.io/en/stable/plugins.html#secret-configuration-values)).

  You would run the command something like this:

      datasette publish cloudrun mydatabase.db \
          --install datasette-auth-passwords \
          --plugin-secret datasette-auth-passwords root_password_hash 'pbkdf2_sha256$...' \
          --service datasette-auth-passwords-demo

  This will allow you to log in as username `root` using the password that you used to create the hash.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-auth-passwords
      python3 -mvenv venv
      source venv/bin/activate

  Or if you are using `pipenv`:

      pipenv shell

  Now install the dependencies and tests:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-auth-passwords/actions", "Changelog": "https://github.com/simonw/datasette-auth-passwords/releases", "Homepage": "https://github.com/simonw/datasette-auth-passwords", "Issues": "https://github.com/simonw/datasette-auth-passwords/issues"}

requires_dist:
  ["datasette>=0.59", "pytest; extra == \"test\"", "pytest-asyncio; extra == \"test\"", "httpx; extra == \"test\""]


name: datasette-auth-tokens
summary: Datasette plugin for authenticating access using API tokens
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-auth-tokens
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-auth-tokens/
project_url: https://pypi.org/project/datasette-auth-tokens/
release_url: https://pypi.org/project/datasette-auth-tokens/0.3/
version: 0.3
description:
  # datasette-auth-tokens

  [![PyPI](https://img.shields.io/pypi/v/datasette-auth-tokens.svg)](https://pypi.org/project/datasette-auth-tokens/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-auth-tokens?include_prereleases&label=changelog)](https://github.com/simonw/datasette-auth-tokens/releases)
  [![Tests](https://github.com/simonw/datasette-auth-tokens/workflows/Test/badge.svg)](https://github.com/simonw/datasette-auth-tokens/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-auth-tokens/blob/main/LICENSE)

  Datasette plugin for authenticating access using API tokens

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ pip install datasette-auth-tokens

  ## Hard-coded tokens

  Read about Datasette's [authentication and permissions system](https://datasette.readthedocs.io/en/latest/authentication.html).

  This plugin lets you configure secret API tokens which can be used to make authenticated requests to Datasette.

  First, create a random API token. A useful recipe for doing that is the following:

      $ python -c 'import secrets; print(secrets.token_hex(32))'
      5f9a486dd807de632200b17508c75002bb66ca6fde1993db1de6cbd446362589

  Decide on the actor that this token should represent, for example:

  ```json
  {
      "bot_id": "my-bot"
  }
  ```

  You can then use `"allow"` blocks to provide that token with permission to access specific actions. To enable access to a configured writable SQL query you could use this in your `metadata.json`:

  ```json
  {
      "plugins": {
          "datasette-auth-tokens": {
              "tokens": [
                  {
                      "token": {
                          "$env": "BOT_TOKEN"
                      },
                      "actor": {
                          "bot_id": "my-bot"
                      }
                  }
              ]
          }
      },
      "databases": {
          ":memory:": {
              "queries": {
                  "show_version": {
                      "sql": "select sqlite_version()",
                      "allow": {
                          "bot_id": "my-bot"
                      }
                  }
              }
          }
      }
  }
  ```
  This uses Datasette's [secret configuration values mechanism](https://datasette.readthedocs.io/en/stable/plugins.html#secret-configuration-values) to allow the secret token to be passed as an environment variable.

  Run Datasette like this:

      BOT_TOKEN="this-is-the-secret-token" \
          datasette -m metadata.json

  You can now run authenticated API queries like this:

      $ curl -H 'Authorization: Bearer this-is-the-secret-token' \
        'http://127.0.0.1:8001/:memory:/show_version.json?_shape=array'
      [{"sqlite_version()": "3.31.1"}]

  Additionally you can allow passing the token as a query string parameter, although that's disabled by default given the security implications of URLs with secret tokens included. This may be useful to easily allow embedding data between different services.

  Simply enable it using the `param` config value:

  ```json
  {
      "plugins": {
          "datasette-auth-tokens": {
              "tokens": [
                  {
                      "token": {
                          "$env": "BOT_TOKEN"
                      },
                      "actor": {
                          "bot_id": "my-bot"
                      },
                  }
              ],
              "param": "_auth_token"
          }
      },
      "databases": {
          ":memory:": {
              "queries": {
                  "show_version": {
                      "sql": "select sqlite_version()",
                      "allow": {
                          "bot_id": "my-bot"
                      }
                  }
              }
          }
      }
  }
  ```

  You can now run authenticated API queries like this:

      $ curl http://127.0.0.1:8001/:memory:/show_version.json?_shape=array&_auth_token=this-is-the-secret-token
      [{"sqlite_version()": "3.31.1"}]

  ## Tokens from your database

  As an alternative (or in addition) to the hard-coded list of tokens you can store tokens in a database table and configure the plugin to access them using a SQL query.

  Your query needs to take a `:token_id` parameter and return at least two columns: one called `token_secret` and one called `actor_*` - usually `actor_id`. Further `actor_` prefixed columns can be returned to provide more details for the authenticated actor.

  Here's a simple example of a configuration query:

  ```sql
  select actor_id, actor_name, token_secret from tokens where token_id = :token_id
  ```

  This can run against a table like this one:

  | token_id | token_secret | actor_id | actor_name |
  | -------- | ------------ | -------- | ---------- |
  | 1        | bd3c94f51fcd | 78       | Cleopaws   |
  | 2        | 86681b4d6f66 | 32       | Pancakes   |

  The tokens are formed as the token ID, then a hyphen, then the token secret. For example:

  - `1-bd3c94f51fcd`
  - `2-86681b4d6f66`

  The SQL query will be executed with the portion before the hyphen as the `:token_id` parameter.

  The `token_secret` value returned by the query will be compared to the portion of the token after the hyphen to check if the token is valid.

  Columns with a prefix of `actor_` will be used to populate the actor dictionary. In the above example, a token of `2-86681b4d6f66` will become an actor dictionary of `{"id": 32, "name": "Pancakes"}`.

  To configure this, use a `"query"` block in your plugin configuration like this:

  ```json
  {
      "plugins": {
          "datasette-auth-tokens": {
              "query": {
                  "sql": "select actor_id, actor_name, token_secret from tokens where token_id = :token_id",
                  "database": "tokens"
              }
          }
      },
      "databases": {
          "tokens": {
              "allow": {}
          }
      }
  }
  ```
  The `"sql"` key here contains the SQL query. The `"database"` key has the name of the attached database file that the query should be executed against - in this case it would execute against `tokens.db`.

  ### Securing your tokens

  Anyone with access to your Datasette instance can use it to read the `token_secret` column in your tokens table. This probably isn't what you want!

  To avoid this, you should lock down access to that table. The configuration example above shows how to do this using an `"allow": {}` block. Consult Datasette's [Permissions documentation](https://datasette.readthedocs.io/en/stable/authentication.html#permissions) for more information about how to lock down this kind of access.

project_urls:
  {"CI": "https://github.com/simonw/datasette-auth-tokens/actions", "Changelog": "https://github.com/simonw/datasette-auth-tokens/releases", "Homepage": "https://github.com/simonw/datasette-auth-tokens", "Issues": "https://github.com/simonw/datasette-auth-tokens/issues"}

requires_dist:
  ["datasette (>=0.44)", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "httpx ; extra == 'test'", "sqlite-utils ; extra == 'test'"]


name: datasette-auth0
summary: Datasette plugin that authenticates users using Auth0
classifiers: ["Framework :: Datasette", "License :: OSI Approved :: Apache Software License"]
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-auth0
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-auth0/
project_url: https://pypi.org/project/datasette-auth0/
release_url: https://pypi.org/project/datasette-auth0/0.1/
requires_python: >=3.7
version: 0.1
description:
  # datasette-auth0

  [![PyPI](https://img.shields.io/pypi/v/datasette-auth0.svg)](https://pypi.org/project/datasette-auth0/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-auth0?include_prereleases&label=changelog)](https://github.com/simonw/datasette-auth0/releases)
  [![Tests](https://github.com/simonw/datasette-auth0/workflows/Test/badge.svg)](https://github.com/simonw/datasette-auth0/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-auth0/blob/main/LICENSE)

  Datasette plugin that authenticates users using [Auth0](https://auth0.com/)

  See [Simplest possible OAuth authentication with Auth0](https://til.simonwillison.net/auth0/oauth-with-auth0) for more about how this plugin works.

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ datasette install datasette-auth0

  ## Demo

  You can try this out at [datasette-auth0-demo.datasette.io](https://datasette-auth0-demo.datasette.io/) - click on the top right menu icon and select "Sign in with Auth0".

  ## Initial configuration

  First, create a new application in Auth0. You will need the domain, client ID and client secret for that application.

  The domain should be something like `mysite.us.auth0.com`.

  Add `http://127.0.0.1:8001/-/auth0-callback` to the list of Allowed Callback URLs.

  Then configure these plugin secrets using `metadata.yml`:

  ```yaml
  plugins:
    datasette-auth0:
      domain:
        "$env": AUTH0_DOMAIN
      client_id:
        "$env": AUTH0_CLIENT_ID
      client_secret:
        "$env": AUTH0_CLIENT_SECRET
  ```
  Only the `client_secret` needs to be kept secret, but for consistency I recommend using the `$env` mechanism for all three.

  In development, you can run Datasette and pass in environment variables like this:
  ```
  AUTH0_DOMAIN="your-domain.us.auth0.com" \
  AUTH0_CLIENT_ID="...client-id-goes-here..." \
  AUTH0_CLIENT_SECRET="...secret-goes-here..." \
  datasette -m metadata.yml
  ```

  If you are deploying using `datasette publish` you can pass these using `--plugin-secret`. For example, to deploy using Cloud Run you might run the following:
  ```
  datasette publish cloudrun mydatabase.db \
  --install datasette-auth0 \
  --plugin-secret datasette-auth0 domain "your-domain.us.auth0.com" \
  --plugin-secret datasette-auth0 client_id "your-client-id" \
  --plugin-secret datasette-auth0 client_secret "your-client-secret" \
  --service datasette-auth0-demo
  ```
  Once your Datasette instance is deployed, you will need to add its callback URL to the "Allowed Callback URLs" list in Auth0.

  The callback URL should be something like:

      https://url-to-your-datasette/-/auth0-callback

  ## Usage

  Once installed, a "Sign in with Auth0" menu item will appear in the Datasette main menu.

  You can sign in and then visit the `/-/actor` page to see full details of the `auth0` profile that has been authenticated.

  You can then use [Datasette permissions](https://docs.datasette.io/en/stable/authentication.html#configuring-permissions-in-metadata-json) to grant or deny access to different parts of Datasette based on the authenticated user.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-auth0
      python3 -mvenv venv
      source venv/bin/activate

  Now install the dependencies and test dependencies:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-auth0/actions", "Changelog": "https://github.com/simonw/datasette-auth0/releases", "Homepage": "https://github.com/simonw/datasette-auth0", "Issues": "https://github.com/simonw/datasette-auth0/issues"}

requires_dist:
  ["datasette", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "pytest-httpx ; extra == 'test'"]


name: datasette-backup
summary: Plugin adding backup options to Datasette
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-backup
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-backup/
project_url: https://pypi.org/project/datasette-backup/
release_url: https://pypi.org/project/datasette-backup/0.1/
version: 0.1
description:
  # datasette-backup

  [![PyPI](https://img.shields.io/pypi/v/datasette-backup.svg)](https://pypi.org/project/datasette-backup/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-backup?include_prereleases&label=changelog)](https://github.com/simonw/datasette-backup/releases)
  [![Tests](https://github.com/simonw/datasette-backup/workflows/Test/badge.svg)](https://github.com/simonw/datasette-backup/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-backup/blob/main/LICENSE)

  Plugin adding backup options to Datasette

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ datasette install datasette-backup

  ## Usage

  Once installed, you can download a SQL backup of any of your databases from:

      /-/backup/dbname.sql

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-backup
      python3 -mvenv venv
      source venv/bin/activate

  Or if you are using `pipenv`:

      pipenv shell

  Now install the dependencies and tests:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-backup/actions", "Changelog": "https://github.com/simonw/datasette-backup/releases", "Homepage": "https://github.com/simonw/datasette-backup", "Issues": "https://github.com/simonw/datasette-backup/issues"}

requires_dist:
  ["datasette", "sqlite-dump (>=0.1.1)", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "httpx ; extra == 'test'", "sqlite-utils ; extra == 'test'"]


name: datasette-basemap
summary: A basemap for Datasette and datasette-leaflet
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-basemap
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-basemap/
project_url: https://pypi.org/project/datasette-basemap/
release_url: https://pypi.org/project/datasette-basemap/0.2/
requires_dist: ["datasette", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'"]
requires_python: >=3.6
version: 0.2
description:
  # datasette-basemap

  [![PyPI](https://img.shields.io/pypi/v/datasette-basemap.svg)](https://pypi.org/project/datasette-basemap/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-basemap?include_prereleases&label=changelog)](https://github.com/simonw/datasette-basemap/releases)
  [![Tests](https://github.com/simonw/datasette-basemap/workflows/Test/badge.svg)](https://github.com/simonw/datasette-basemap/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-basemap/blob/main/LICENSE)

  A basemap for Datasette and datasette-leaflet

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ datasette install datasette-basemap

  ## Usage

  This plugin will make a `basemap` database available containing OpenStreetMap tiles in the [mbtiles](https://github.com/mapbox/mbtiles-spec) format. It is designed for use with the [datasette-tiles](https://datasette.io/plugins/datasette-tiles) tile server plugin.

  ## Demo

  You can preview this map at https://datasette-tiles-demo.datasette.io/-/tiles/basemap and browse the database directly at https://datasette-tiles-demo.datasette.io/basemap

  ## License

  The data bundled with this package is © OpenStreetMap contributors, licensed under the [Open Data Commons Open Database License](https://opendatacommons.org/licenses/odbl/). See [this page](https://www.openstreetmap.org/copyright) for more details.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-basemap
      python3 -mvenv venv
      source venv/bin/activate

  Or if you are using `pipenv`:

      pipenv shell

  Now install the dependencies and tests:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-basemap/actions", "Changelog": "https://github.com/simonw/datasette-basemap/releases", "Homepage": "https://github.com/simonw/datasette-basemap", "Issues": "https://github.com/simonw/datasette-basemap/issues"}


name: datasette-block-robots
summary: Datasette plugin that blocks all robots using robots.txt
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-block-robots
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-block-robots/
project_url: https://pypi.org/project/datasette-block-robots/
release_url: https://pypi.org/project/datasette-block-robots/1.1/
version: 1.1
description:
  # datasette-block-robots

  [![PyPI](https://img.shields.io/pypi/v/datasette-block-robots.svg)](https://pypi.org/project/datasette-block-robots/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-block-robots?label=changelog)](https://github.com/simonw/datasette-block-robots/releases)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-block-robots/blob/master/LICENSE)

  Datasette plugin that blocks robots and crawlers using robots.txt

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ pip install datasette-block-robots

  ## Usage

  Having installed the plugin, `/robots.txt` on your Datasette instance will return the following:

      User-agent: *
      Disallow: /

  This will request all robots and crawlers not to visit any of the pages on your site.

  Here's a demo of the plugin in action: https://sqlite-generate-demo.datasette.io/robots.txt

  ## Configuration

  By default the plugin will block all access to the site, using `Disallow: /`.

  If you want the index page to be indexed by search engines without crawling the database, table or row pages themselves, you can use the following:

  ```json
  {
      "plugins": {
          "datasette-block-robots": {
              "allow_only_index": true
          }
      }
  }
  ```
  This will return a `/robots.txt` like so:

      User-agent: *
      Disallow: /db1
      Disallow: /db2

  With a `Disallow` line for every attached database.

  To block access to specific areas of the site using custom paths, add this to your `metadata.json` configuration file:

  ```json
  {
      "plugins": {
          "datasette-block-robots": {
              "disallow": ["/mydatabase/mytable"]
          }
      }
  }
  ```
  This will result in a `/robots.txt` that looks like this:

      User-agent: *
      Disallow: /mydatabase/mytable

  Alternatively you can set the full contents of the `robots.txt` file using the `literal` configuration option. Here's how to do that if you are using YAML rather than JSON and have a `metadata.yml` file:

  ```yaml
  plugins:
      datasette-block-robots:
          literal: |-
              User-agent: *
              Disallow: /
              User-agent: Bingbot
              User-agent: Googlebot
              Disallow:
  ```
  This example would block all crawlers with the exception of Googlebot and Bingbot, which are allowed to crawl the entire site.

  ## Extending this with other plugins

  This plugin adds a new [plugin hook](https://docs.datasette.io/en/stable/plugin_hooks.html) to Datasete called `block_robots_extra_lines()` which can be used by other plugins to add their own additional lines to the `robots.txt` file.

  The hook can optionally accept these parameters:

  - `datasette`: The current [Datasette instance](https://docs.datasette.io/en/stable/internals.html#datasette-class). You can use this to execute SQL queries or read plugin configuration settings.
  - `request`: The [Request object](https://docs.datasette.io/en/stable/internals.html#request-object) representing the incoming request to `/robots.txt`.

  The hook should return a list of strings, each representing a line to be added to the `robots.txt` file.

  It can also return an `async def` function, which will be awaited and used to generate a list of lines. Use this option if you need to make `await` calls inside you hook implementation.

  This example uses the hook to add a `Sitemap: http://example.com/sitemap.xml` line to the `robots.txt` file:

  ```python
  from datasette import hookimpl

  @hookimpl
  def block_robots_extra_lines(datasette, request):
      return [
          "Sitemap: {}".format(datasette.absolute_url(request, "/sitemap.xml")),
      ]
  ```
  This example blocks access to paths based on a database query:

  ```python
  @hookimpl
  def block_robots_extra_lines(datasette):
      async def inner():
          db = datasette.get_database()
          result = await db.execute("select path from mytable")
          return [
              "Disallow: /{}".format(row["path"]) for row in result
          ]
      return inner
  ```
  [datasette-sitemap](https://datasette.io/plugins/datasette-sitemap) is an example of a plugin that uses this hook.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-block-robots
      python3 -mvenv venv
      source venv/bin/activate

  Or if you are using `pipenv`:

      pipenv shell

  Now install the dependencies and tests:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-block-robots/actions", "Changelog": "https://github.com/simonw/datasette-block-robots/releases", "Homepage": "https://github.com/simonw/datasette-block-robots", "Issues": "https://github.com/simonw/datasette-block-robots/issues"}

requires_dist:
  ["datasette (>=0.50)", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "httpx ; extra == 'test'"]


name: datasette-block
summary: Block all access to specific path prefixes
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-block
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-block/
project_url: https://pypi.org/project/datasette-block/
release_url: https://pypi.org/project/datasette-block/0.1.1/
requires_python: >=3.6
version: 0.1.1
description:
  # datasette-block

  [![PyPI](https://img.shields.io/pypi/v/datasette-block.svg)](https://pypi.org/project/datasette-block/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-block?include_prereleases&label=changelog)](https://github.com/simonw/datasette-block/releases)
  [![Tests](https://github.com/simonw/datasette-block/workflows/Test/badge.svg)](https://github.com/simonw/datasette-block/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-block/blob/main/LICENSE)

  Block all access to specific path prefixes

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ datasette install datasette-block

  ## Configuration

  Add the following to `metadata.json` to block specific path prefixes:

  ```json
  {
      "plugins": {
          "datasette-block": {
              "prefixes": ["/all/"]
          }
      }
  }
  ```

  This will cause a 403 error to be returned for any path beginning with `/all/`.

  This blocking happens as an ASGI wrapper around Datasette.

  ## Why would you need this?

  You almost always would not. I use it with `datasette-ripgrep` to block access to static assets for unauthenticated users.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-block
      python3 -mvenv venv
      source venv/bin/activate

  Or if you are using `pipenv`:

      pipenv shell

  Now install the dependencies and tests:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-block/actions", "Changelog": "https://github.com/simonw/datasette-block/releases", "Homepage": "https://github.com/simonw/datasette-block", "Issues": "https://github.com/simonw/datasette-block/issues"}

requires_dist:
  ["datasette", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "asgi-lifespan ; extra == 'test'"]


name: datasette-bplist
summary: Datasette plugin for working with Apple's binary plist format
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-bplist
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-bplist/
project_url: https://pypi.org/project/datasette-bplist/
project_urls: {"Homepage": "https://github.com/simonw/datasette-bplist"}
release_url: https://pypi.org/project/datasette-bplist/0.1/
requires_dist: ["datasette", "bpylist", "pytest ; extra == 'test'"]
version: 0.1
description:
  # datasette-bplist

  [![PyPI](https://img.shields.io/pypi/v/datasette-bplist.svg)](https://pypi.org/project/datasette-bplist/)
  [![CircleCI](https://circleci.com/gh/simonw/datasette-bplist.svg?style=svg)](https://circleci.com/gh/simonw/datasette-bplist)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-bplist/blob/master/LICENSE)

  Datasette plugin for working with Apple's [binary plist](https://en.wikipedia.org/wiki/Property_list) format.

  This plugin adds two features: a display hook and a SQL function.

  The display hook will detect any database values that are encoded using the binary plist format. It will decode them, convert them into JSON and display them pretty-printed in the Datasette UI.

  The SQL function `bplist_to_json(value)` can be used inside a SQL query to convert a binary plist value into a JSON string. This can then be used with SQLite's `json_extract()` function or with the [datasette-jq](https://github.com/simonw/datasette-jq) plugin to further analyze that data as part of a SQL query.

  Install this plugin in the same environment as Datasette to enable this new functionality:

      pip install datasette-bplist

  ## Trying it out

  If you use a Mac you already have plenty of SQLite databases that contain binary plist data.

  One example is the database that powers the Apple Photos app.

  This database tends to be locked, so you will need to create a copy of the database in order to run queries against it:

      cp ~/Pictures/Photos\ Library.photoslibrary/database/photos.db /tmp/photos.db

  The database also makes use of custom SQLite extensions which prevent it from opening in Datasette.

  You can work around this by exporting the data that you want to experiment with into a new SQLite file.

  I recommend trying this plugin against the `RKMaster_dataNote` table, which contains plist-encoded EXIF metadata about the photos you have taken.

  You can export that table into a fresh database like so:

      sqlite3 /tmp/photos.db ".dump RKMaster_dataNote" | sqlite3 /tmp/exif.db

  Now run `datasette /tmp/exif.db` and you can start trying out the plugin.

  ## Using the bplist_to_json() SQL function

  Once you have the `exif.db` demo working, you can try the `bplist_to_json()` SQL function.

  Here's a query that shows the camera lenses you have used the most often to take photos:

      select
          json_extract(
              bplist_to_json(value),
              "$.{Exif}.LensModel"
          ) as lens,
          count(*) as n
      from RKMaster_dataNote
      group by lens
      order by n desc;

  If you have a large number of photos this query can take a long time to execute, so you may need to increase the SQL time limit enforced by Datasette like so:

      $ datasette /tmp/exif.db \
          --config sql_time_limit_ms:10000

  Here's another query, showing the time at which you took every photo in your library which is classified as as screenshot:

      select
          attachedToId,
          json_extract(
              bplist_to_json(value),
              "$.{Exif}.DateTimeOriginal"
          )
      from RKMaster_dataNote
      where
          json_extract(
              bplist_to_json(value),
              "$.{Exif}.UserComment"
          ) = "Screenshot"

  And if you install the [datasette-cluster-map](https://github.com/simonw/datasette-cluster-map) plugin, this query will show you a map of your most recent 1000 photos:

      select
          *, 
          json_extract(
              bplist_to_json(value),
              "$.{GPS}.Latitude"
          ) as latitude,
          -json_extract(
              bplist_to_json(value),
              "$.{GPS}.Longitude"
          ) as longitude,
          json_extract(
              bplist_to_json(value),
              "$.{Exif}.DateTimeOriginal"
          ) as datetime
      from
          RKMaster_dataNote
      where
          latitude is not null
      order by
          attachedToId desc


name: datasette-build
summary: Build a directory full of files into a SQLite database
classifiers: ["Framework :: Datasette", "License :: OSI Approved :: Apache Software License"]
author: Datasette
description_content_type: text/markdown
license: Apache-2.0
package_url: https://pypi.org/project/datasette-build/
project_url: https://pypi.org/project/datasette-build/
release_url: https://pypi.org/project/datasette-build/0.1a0/
requires_python: >=3.8
version: 0.1a0
description:
  # datasette-build

  [![PyPI](https://img.shields.io/pypi/v/datasette-build.svg)](https://pypi.org/project/datasette-build/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-build?include_prereleases&label=changelog)](https://github.com/datasette/datasette-build/releases)
  [![Tests](https://github.com/datasette/datasette-build/actions/workflows/test.yml/badge.svg)](https://github.com/datasette/datasette-build/actions/workflows/test.yml)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-build/blob/main/LICENSE)

  Build a directory full of files into a SQLite database

  > ⚠️ **Early alpha preview**. Everything about this tool is likely to change.

  ## Installation

  Install this tool using `pip` or `pipx`:
  ```bash
  pipx install datasette-build
  ```
  This will provide the `datasette-build` CLI application.

  You can also install it as a Datasette plugin. First [install Datasette](https://docs.datasette.io/en/stable/installation.html), then run:
  ```bash
  datasette install datasette-build
  ```
  This will provide a `datasette build ...` command that works the same as the `datasette-build` CLI application.

  Or you can install it as a plugin for [sqlite-utils](https://sqlite-utils.datasette.io/). With that installed, run this:
  ```bash
  sqlite-utils install datasette-build
  ```
  Now you can access the tool as `sqlite-utils build ...`

  ## Usage

  The `datasette-build` (or `datasette build` or `sqlite-utils build`) command takes two arguments: a path to a SQLite database file and a path to a directory containing files to be loaded into that database:

  ```bash
  datasette-build mydatabase.db myfiles/
  ```
  The `myfiles/` folder can contain a mixture of CSV, TSV and JSON files. Each file will be loaded into a table in the `mydatabase.db` SQLite database.

  The database file will be created if it does not already exist.

  Consider a `myfiles/cities.csv` file like this:
  ```csv
  id,name,latitude,longitude,country
  nyc,New York City,40.7128,-74.006,US
  lon,London,51.5074,-0.1278,GB
  tok,Tokyo,35.6895,139.6917,JP
  par,Paris,48.8566,2.3522,FR
  ber,Berlin,52.52,13.405,DE
  syd,Sydney,-33.8688,151.2093,AU
  cai,Cairo,30.0444,31.2357,EG
  rio,Rio de Janeiro,-22.9068,-43.1729,BR
  mos,Moscow,55.7558,37.6173,RU
  mum,Mumbai,19.076,72.8777,IN
  ```
  Since this has a `id` column the primary key for the table will be set to `id`. Without an `id` column the primary key will not be defined.

  A `myfiles/counties.tsv` file could look like this:
  ```tsv
  id	name	population
  US	United States	331002651
  GB	United Kingdom	67886011
  JP	Japan	126476461
  FR	France	65273511
  DE	Germany	83783942
  AU	Australia	25499884
  EG	Egypt	102334404
  BR	Brazil	212559417
  RU	Russia	145934462
  IN	India	1380004385
  ```
  And a `myfiles/museums.json` file like this:
  ```json
  [
    {
      "id": 1,
      "name": "Metropolitan Museum of Art",
      "city_id": "nyc"
    },
    {
      "id": 2,
      "name": "British Museum",
      "city_id": "lon"
    }
  ]
  ```
  Running `datasette-build mydatabase.db myfiles/` will create a SQLite database file containing three tables: `cities`, `counties` and `museums`. The schema will look like this:

  ```sql
  CREATE TABLE [museums] (
     [id] INTEGER PRIMARY KEY,
     [name] TEXT,
     [city_id] TEXT
  );
  CREATE TABLE "cities" (
     [id] TEXT PRIMARY KEY,
     [name] TEXT,
     [latitude] FLOAT,
     [longitude] FLOAT,
     [country] TEXT
  );
  CREATE TABLE "countries" (
     [id] TEXT PRIMARY KEY,
     [name] TEXT,
     [population] INTEGER
  );
  ```

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:
  ```bash
  cd datasette-build
  python3 -m venv venv
  source venv/bin/activate
  ```
  Now install the dependencies and test dependencies:
  ```bash
  pip install -e '.[test]'
  ```
  To run the tests:
  ```bash
  pytest
  ```

project_urls:
  {"CI": "https://github.com/datasette/datasette-build/actions", "Changelog": "https://github.com/datasette/datasette-build/releases", "Homepage": "https://github.com/datasette/datasette-build", "Issues": "https://github.com/datasette/datasette-build/issues"}

requires_dist:
  ["click", "sqlite-utils", "PyYAML", "pluggy", "datasette ; extra == 'datasette'", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'"]


name: datasette-chatgpt-plugin
summary: A Datasette plugin that turns a Datasette instance into a ChatGPT plugin
classifiers: ["Framework :: Datasette", "License :: OSI Approved :: Apache Software License"]
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-chatgpt-plugin
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-chatgpt-plugin/
project_url: https://pypi.org/project/datasette-chatgpt-plugin/
release_url: https://pypi.org/project/datasette-chatgpt-plugin/0.1/
requires_dist: ["datasette", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'"]
requires_python: >=3.7
version: 0.1
description:
  # datasette-chatgpt-plugin

  [![PyPI](https://img.shields.io/pypi/v/datasette-chatgpt-plugin.svg)](https://pypi.org/project/datasette-chatgpt-plugin/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-chatgpt-plugin?include_prereleases&label=changelog)](https://github.com/simonw/datasette-chatgpt-plugin/releases)
  [![Tests](https://github.com/simonw/datasette-chatgpt-plugin/workflows/Test/badge.svg)](https://github.com/simonw/datasette-chatgpt-plugin/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-chatgpt-plugin/blob/main/LICENSE)

  A Datasette plugin that turns a Datasette instance into a ChatGPT plugin - so you can use ChatGPT to ask questions of your data.

  For more on this project, see [Building a ChatGPT plugin to ask questions of data hosted in Datasette](https://simonwillison.net/2023/Mar/24/datasette-chatgpt-plugin/).

  ## Installation

  Install this plugin in the same environment as Datasette, on a deployed instance.

      datasette install datasette-chatgpt-plugin

  Or if you are using `datasette publish` to deploy Datasette:

      datasette publish cloudrun/vercel/fly/heroku data.db \
        --install datasette-chatgpt-plugin

  ## Usage

  Once installed, your Datasette instance will work with the new [ChatGPT plugins](https://openai.com/blog/chatgpt-plugins) system - provided you have access to that preview.

  Click `Plugins -> Plugin store -> Install an unverified plugin` and enter the URL of your Datasette instance.

  ChatGPT will discover the plugin by hitting the `/.well-known/ai-plugin.json` endpoint.

  You can then ask it questions! Some starting examples:

  - Show a list of tables (this is always good to start with as it ensures ChatGPT knows which tables are available)
  - Show the first 10 rows of the `mytable` table

  This plugin currently exposes a single database - the first database attached to your instance.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-chatgpt-plugin
      python3 -m venv venv
      source venv/bin/activate

  Now install the dependencies and test dependencies:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-chatgpt-plugin/actions", "Changelog": "https://github.com/simonw/datasette-chatgpt-plugin/releases", "Homepage": "https://github.com/simonw/datasette-chatgpt-plugin", "Issues": "https://github.com/simonw/datasette-chatgpt-plugin/issues"}


name: datasette-checkbox
summary: Add interactive checkboxes to columns in Datasette
classifiers: ["Framework :: Datasette"]
author: Simon Willison
description_content_type: text/markdown
dynamic: ["License-File"]
license_expression: Apache-2.0
license_files: ["LICENSE"]
package_url: https://pypi.org/project/datasette-checkbox/
project_url: https://pypi.org/project/datasette-checkbox/
provides_extra: ["test"]
release_url: https://pypi.org/project/datasette-checkbox/0.1a4/
requires_dist: ["datasette>=1.0a19", "pytest; extra == \"test\"", "pytest-asyncio; extra == \"test\""]
requires_python: >=3.10
version: 0.1a4
description:
  # datasette-checkbox

  [![PyPI](https://img.shields.io/pypi/v/datasette-checkbox.svg)](https://pypi.org/project/datasette-checkbox/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-checkbox?include_prereleases&label=changelog)](https://github.com/datasette/datasette-checkbox/releases)
  [![Tests](https://github.com/datasette/datasette-checkbox/actions/workflows/test.yml/badge.svg)](https://github.com/datasette/datasette-checkbox/actions/workflows/test.yml)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-checkbox/blob/main/LICENSE)

  Add interactive checkboxes to columns in Datasette. Requires Datasette 1.0 alpha or higher.

  ![Animated demo showing checkboxes in columns for is_done, should_be_deleted and is_happy - checking the checkboxes shows an updated message next to each one which then fades away.](https://raw.githubusercontent.com/datasette/datasette-checkbox/main/datasette-checkbox.gif)

  ## Installation

  Install this plugin in the same environment as Datasette.
  ```bash
  datasette install datasette-checkbox
  ```
  ## Usage

  This plugin adds checkboxes to the table and row pages in Datasette for any column with a name that starts `is_*` or `should_*` or `has_*` and that is of type `integer`.

  Toggling those checkboxes updates the underlying column to a `1` or a `0`.

  The checkbox interface will only be shown for users who have `update-row` permission for the table.

  The easiest way to try this plugin is using the `--root` Datasette option:

  ```bash
  datasette data.db --root
  ```
  Or use the [Datasette permission system](https://docs.datasette.io/en/latest/authentication.html#permissions) to grant `update-row` to specific users.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:
  ```bash
  cd datasette-checkbox
  python3 -m venv venv
  source venv/bin/activate
  ```
  Now install the dependencies and test dependencies:
  ```bash
  pip install -e '.[test]'
  ```
  To run the tests:
  ```bash
  pytest
  ```

project_urls:
  {"CI": "https://github.com/datasette/datasette-checkbox/actions", "Changelog": "https://github.com/datasette/datasette-checkbox/releases", "Homepage": "https://github.com/datasette/datasette-checkbox", "Issues": "https://github.com/datasette/datasette-checkbox/issues"}


name: datasette-chronicle
summary: Use sqlite-chronicle with tables in Datasette
classifiers: ["Framework :: Datasette"]
author: Simon Willison
description_content_type: text/markdown
dynamic: ["License-File"]
license_expression: Apache-2.0
license_files: ["LICENSE"]
package_url: https://pypi.org/project/datasette-chronicle/
project_url: https://pypi.org/project/datasette-chronicle/
provides_extra: ["test"]
release_url: https://pypi.org/project/datasette-chronicle/0.3/
requires_python: >=3.9
version: 0.3
description:
  # datasette-chronicle

  [![PyPI](https://img.shields.io/pypi/v/datasette-chronicle.svg)](https://pypi.org/project/datasette-chronicle/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-chronicle?include_prereleases&label=changelog)](https://github.com/datasette/datasette-chronicle/releases)
  [![Tests](https://github.com/datasette/datasette-chronicle/workflows/Test/badge.svg)](https://github.com/datasette/datasette-chronicle/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-chronicle/blob/main/LICENSE)

  Use [sqlite-chronicle](https://github.com/simonw/sqlite-chronicle) with tables in Datasette

  ## Installation

  Install this plugin in the same environment as Datasette.
  ```bash
  datasette install datasette-chronicle
  ```
  ## Usage

  Once installed, users with the `enable-chronicle` and `disable-chronicle` permissions (granted to the `root` user by default) will have access to new table action menu options for enabling and disabling row version tracking for a table.

  Tables that have had row version tracking enabled will now support a new `?_since=X` query string parameter on the table page, which will filter for just rows that have been inserted or updated since the specified chronicle version.

  See the [sqlite-chronicle documentation](https://github.com/simonw/sqlite-chronicle/blob/main/README.md) for more details on what this is and how it works.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:
  ```bash
  cd datasette-chronicle
  python3 -m venv venv
  source venv/bin/activate
  ```
  Now install the dependencies and test dependencies:
  ```bash
  pip install -e '.[test]'
  ```
  To run the tests:
  ```bash
  pytest
  ```

project_urls:
  {"CI": "https://github.com/datasette/datasette-chronicle/actions", "Changelog": "https://github.com/datasette/datasette-chronicle/releases", "Homepage": "https://github.com/datasette/datasette-chronicle", "Issues": "https://github.com/datasette/datasette-chronicle/issues"}

requires_dist:
  ["datasette", "sqlite-chronicle>=0.4", "pytest; extra == \"test\"", "pytest-asyncio; extra == \"test\"", "sqlite-utils; extra == \"test\""]


name: datasette-clone
summary: Create a local copy of database files from a Datasette instance
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-clone
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-clone/
project_url: https://pypi.org/project/datasette-clone/
project_urls: {"Homepage": "https://github.com/simonw/datasette-clone"}
release_url: https://pypi.org/project/datasette-clone/0.5/
requires_dist: ["requests", "click", "pytest ; extra == 'test'", "requests-mock ; extra == 'test'"]
version: 0.5
description:
  # datasette-clone

  [![PyPI](https://img.shields.io/pypi/v/datasette-clone.svg)](https://pypi.org/project/datasette-clone/)
  [![CircleCI](https://circleci.com/gh/simonw/datasette-clone.svg?style=svg)](https://circleci.com/gh/simonw/datasette-clone)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-clone/blob/master/LICENSE)

  Create a local copy of database files from a Datasette instance.

  See [datasette-clone](https://simonwillison.net/2020/Apr/14/datasette-clone/) on my blog for background on this project.

  ## How to install

      $ pip install datasette-clone

  ## Usage

  This only works against Datasette instances running immutable databases (with the `-i` option). Databases published using the `datasette publish` command should be compatible with this tool.

  To download copies of all `.db` files from an instance, run:

      datasette-clone https://latest.datasette.io

  You can provide an optional second argument to specify a directory:

      datasette-clone https://latest.datasette.io /tmp/here-please

  The command stores its own copy of a `databases.json` manifest and uses it to only download databases that have changed the next time you run the command.

  It also stores a copy of the instance's `metadata.json` to ensure you have a copy of any source and licensing information for the downloaded databases.

  If your instance is protected by an API token, you can use `--token` to provide it:

      datasette-clone https://latest.datasette.io --token=xyz

  For verbose output showing what the tool is doing, use `-v`.


name: datasette-cluster-map
summary: Datasette plugin that shows a map for any data with latitude/longitude columns
classifiers: ["Framework :: Datasette", "License :: OSI Approved :: Apache Software License"]
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-cluster-map
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-cluster-map/
project_url: https://pypi.org/project/datasette-cluster-map/
release_url: https://pypi.org/project/datasette-cluster-map/0.18.2/
requires_python: >=3.8
version: 0.18.2
description:
  # datasette-cluster-map

  [![PyPI](https://img.shields.io/pypi/v/datasette-cluster-map.svg)](https://pypi.org/project/datasette-cluster-map/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-cluster-map?include_prereleases&label=changelog)](https://github.com/simonw/datasette-cluster-map/releases)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-cluster-map/blob/main/LICENSE)

  A [Datasette plugin](https://docs.datasette.io/en/stable/plugins.html) that detects tables with `latitude` and `longitude` columns and then plots them on a map using [Leaflet.markercluster](https://github.com/Leaflet/Leaflet.markercluster).

  More about this project: [Datasette plugins, and building a clustered map visualization](https://simonwillison.net/2018/Apr/20/datasette-plugins/).

  ## Demo

  [global-power-plants.datasettes.com](https://global-power-plants.datasettes.com/global-power-plants/global-power-plants) hosts a demo of this plugin running against a database of 33,000 power plants around the world.

  ![Cluster map demo](https://static.simonwillison.net/static/2020/global-power-plants.png)

  ## Installation

  Run `datasette install datasette-cluster-map` to add this plugin to your Datasette virtual environment. Datasette will automatically load the plugin if it is installed in this way.

  If you are deploying using the `datasette publish` command you can use the `--install` option:

      datasette publish cloudrun mydb.db --install=datasette-cluster-map

  If any of your tables have one of the following pairs of columns a map will be automatically displayed:

  - `latitude` and `longitude`
  - `lat` and `lng`
  - `lat` and `lon`
  - `lat` and `long`
  - `*_latitude` and `*_longitude`
  - `*_lat` and `*_lng` for any of the three variants of `lng`

  ## Configuration

  If your columns are called something else you can configure the column names using [plugin configuration](https://docs.datasette.io/en/stable/plugins.html#plugin-configuration) in a `metadata.json` file. For example, if all of your columns are called `xlat` and `xlng` you can create a `metadata.json` file like this:

  ```json
  {
      "title": "Regular metadata keys can go here too",
      "plugins": {
          "datasette-cluster-map": {
              "latitude_column": "xlat",
              "longitude_column": "xlng"
          }
      }
  }
  ```

  Then run Datasette like this:

      datasette mydata.db -m metadata.json

  This will configure the required column names for every database loaded by that Datasette instance.

  If you want to customize the column names for just one table in one database, you can do something like this:

  ```json
  {
      "databases": {
          "polar-bears": {
              "tables": {
                  "USGS_WC_eartag_deployments_2009-2011": {
                      "plugins": {
                          "datasette-cluster-map": {
                              "latitude_column": "Capture Latitude",
                              "longitude_column": "Capture Longitude"
                          }
                      }
                  }
              }
          }
      }
  }
  ```

  You can also use a custom SQL query to rename those columns to `latitude` and `longitude`, [for example](https://polar-bears.now.sh/polar-bears?sql=select+*%2C%0D%0A++++%22Capture+Latitude%22+as+latitude%2C%0D%0A++++%22Capture+Longitude%22+as+longitude%0D%0Afrom+%5BUSGS_WC_eartag_deployments_2009-2011%5D):

  ```sql
  select *,
      "Capture Latitude" as latitude,
      "Capture Longitude" as longitude
  from [USGS_WC_eartag_deployments_2009-2011]
  ```

  The map defaults to being displayed above the main results table on the page. You can use the `"container"` plugin setting to provide a CSS selector indicating an element that the map should be appended to instead.

  ## Custom tile layers

  You can customize the tile layer used  by the maps using the `tile_layer` and `tile_layer_options` configuration settings. For example, to use the [OpenTopoMap](https://opentopomap.org/) you can use these settings:

  ```json
  {
      "plugins": {
          "datasette-cluster-map": {
              "tile_layer": "https://{s}.tile.opentopomap.org/{z}/{x}/{y}.png",
              "tile_layer_options": {
                  "attribution": "Map data: &copy; <a href='https://www.openstreetmap.org/copyright'>OpenStreetMap</a> contributors, <a href='http://viewfinderpanoramas.org'>SRTM</a> | Map style: &copy; <a href='https://opentopomap.org'>OpenTopoMap</a> (<a href='https://creativecommons.org/licenses/by-sa/3.0/'>CC-BY-SA</a>)",
                  "maxZoom": 17
              }
          }
      }
  }
  ```
  If your tile server provides retina tiles, add `"detectRetina": true` to `tile_layer_options` to enable them.

  The [Leaflet Providers preview list](https://leaflet-extras.github.io/leaflet-providers/preview/index.html) has details of many other tile layers you can use.

  ## Custom marker popups

  The marker popup defaults to displaying the data for the underlying database row.

  You can customize this by including a `popup` column in your results containing JSON that defines a more useful popup.

  The JSON in the popup column should look something like this:

  ```json
  {
      "image": "https://niche-museums.imgix.net/dodgems.heic?w=800&h=400&fit=crop",
      "alt": "Dingles Fairground Heritage Centre",
      "title": "Dingles Fairground Heritage Centre",
      "description": "Home of the National Fairground Collection, Dingles has over 45,000 indoor square feet of vintage fairground rides... and you can go on them! Highlights include the last complete surviving and opera",
      "link": "/browse/museums/26"
  }
  ```

  Each of these columns is optional.

  - `title` is the title to show at the top of the popup
  - `image` is the URL to an image to display in the popup
  - `alt` is the alt attribute to use for that image
  - `description` is a longer string of text to use as a description
  - `link` is a URL that the marker content should link to

  You can use the SQLite `json_object()` function to construct this data dynamically as part of your SQL query. Here's an example:

  ```sql
  select json_object(
    'image', photo_url || '?w=800&h=400&fit=crop',
    'title', name,
    'description', substr(description, 0, 200),
    'link', '/browse/museums/' || id
    ) as popup,
    latitude, longitude from museums
  where id in (26, 27) order by id
  ```

  [Try that example here](https://www.niche-museums.com/browse?sql=select+json_object%28%0D%0A++%27image%27%2C+photo_url+%7C%7C+%27%3Fw%3D800%26h%3D400%26fit%3Dcrop%27%2C%0D%0A++%27title%27%2C+name%2C%0D%0A++%27description%27%2C+substr%28description%2C+0%2C+200%29%2C%0D%0A++%27link%27%2C+%27%2Fbrowse%2Fmuseums%2F%27+%7C%7C+id%0D%0A++%29+as+popup%2C%0D%0A++latitude%2C+longitude+from+museums) or take a look at [this demo built using a SQL view](https://dogsheep-photos.dogsheep.net/public/photos_on_a_map).

  ## How I deployed the demo

      datasette publish cloudrun global-power-plants.db \
          --service global-power-plants \
          --metadata metadata.json \
          --install=datasette-cluster-map \
          --extra-options="--config facet_time_limit_ms:1000"

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-cluster-map
      python3 -mvenv venv
      source venv/bin/activate

  Or if you are using `pipenv`:

      pipenv shell

  Now install the dependencies and tests:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-cluster-map/actions", "Changelog": "https://github.com/simonw/datasette-cluster-map/releases", "Homepage": "https://github.com/simonw/datasette-cluster-map", "Issues": "https://github.com/simonw/datasette-cluster-map/issues"}

requires_dist:
  ["datasette>=0.54", "datasette-leaflet>=0.2.2", "pytest-playwright; extra == \"playwright\"", "pytest; extra == \"test\"", "pytest-asyncio; extra == \"test\"", "httpx; extra == \"test\"", "sqlite-utils; extra == \"test\"", "nest-asyncio; extra == \"test\"", "datasette-test; extra == \"test\""]


name: datasette-codespaces
summary: Conveniences for running Datasette on GitHub Codespaces
classifiers: ["Framework :: Datasette", "License :: OSI Approved :: Apache Software License"]
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-codespaces
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-codespaces/
project_url: https://pypi.org/project/datasette-codespaces/
release_url: https://pypi.org/project/datasette-codespaces/0.1.1/
requires_python: >=3.7
version: 0.1.1
description:
  # datasette-codespaces

  [![PyPI](https://img.shields.io/pypi/v/datasette-codespaces.svg)](https://pypi.org/project/datasette-codespaces/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-codespaces?include_prereleases&label=changelog)](https://github.com/simonw/datasette-codespaces/releases)
  [![Tests](https://github.com/simonw/datasette-codespaces/workflows/Test/badge.svg)](https://github.com/simonw/datasette-codespaces/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-codespaces/blob/main/LICENSE)

   Conveniences for running [Datasette](https://datasette.io/) on [GitHub Codespaces](https://github.com/features/codespaces)

  ## Installation

  Install this plugin in the same environment as Datasette.

      datasette install datasette-codespaces

  ## Tutorial

  [Using Datasette in GitHub Codespaces](https://datasette.io/tutorials/codespaces) provides a detailed tutorial introduction to this plugin.

  ## Usage

  Install this when you are using Datasette inside of GitHub Codespaces. It makes the following changes:

  - All requests will be treated as the `root` actor provided Datasette is running inside Codespaces (detected through the presence of the `CODESPACE_NAME` environment variable). This should still be secure as GitHub restricts access to servers running in the Codespaces environment based on GitHub authentication.
  - The [datasette-x-forwarded-host](https://datasette.io/plugins/datasette-x-forwarded-host) plugin will be installed, ensuring links to other pages within Datasette such as facet navigation work correctly.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-codespaces
      python3 -m venv venv
      source venv/bin/activate

  Now install the dependencies and test dependencies:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-codespaces/actions", "Changelog": "https://github.com/simonw/datasette-codespaces/releases", "Homepage": "https://github.com/simonw/datasette-codespaces", "Issues": "https://github.com/simonw/datasette-codespaces/issues"}

requires_dist:
  ["datasette", "datasette-x-forwarded-host", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'"]


name: datasette-column-inspect
summary: Experimental Datasette plugin for inspecting columns
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-column-inspect
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-column-inspect/
project_url: https://pypi.org/project/datasette-column-inspect/
project_urls: {"Homepage": "https://github.com/simonw/datasette-column-inspect"}
release_url: https://pypi.org/project/datasette-column-inspect/0.2a0/
version: 0.2a0
description:
  # datasette-column-inspect

  [![PyPI](https://img.shields.io/pypi/v/datasette-column-inspect.svg)](https://pypi.org/project/datasette-column-inspect/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-column-inspect?include_prereleases&label=changelog)](https://github.com/simonw/datasette-column-inspect/releases)
  [![Tests](https://github.com/simonw/datasette-column-inspect/workflows/Test/badge.svg)](https://github.com/simonw/datasette-column-inspect/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-column-inspect/blob/main/LICENSE)

  Highly experimental Datasette plugin for inspecting columns.

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ pip install datasette-column-inspect

  ## Usage

  This plugin adds an icon to each column on the table page which opens an inspection side panel.

requires_dist:
  ["datasette", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "httpx ; extra == 'test'", "sqlite-utils ; extra == 'test'"]


name: datasette-column-sum
summary: Sum the values in numeric Datasette columns
classifiers: ["Framework :: Datasette", "License :: OSI Approved :: Apache Software License"]
author: Simon Willison
description_content_type: text/markdown
license: Apache-2.0
package_url: https://pypi.org/project/datasette-column-sum/
project_url: https://pypi.org/project/datasette-column-sum/
release_url: https://pypi.org/project/datasette-column-sum/0.1a0/
requires_python: >=3.8
version: 0.1a0
description:
  # datasette-column-sum

  [![PyPI](https://img.shields.io/pypi/v/datasette-column-sum.svg)](https://pypi.org/project/datasette-column-sum/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-column-sum?include_prereleases&label=changelog)](https://github.com/datasette/datasette-column-sum/releases)
  [![Tests](https://github.com/datasette/datasette-column-sum/actions/workflows/test.yml/badge.svg)](https://github.com/datasette/datasette-column-sum/actions/workflows/test.yml)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-column-sum/blob/main/LICENSE)

  Sum the values in numeric Datasette columns

  ## Installation

  Install this plugin in the same environment as Datasette.
  ```bash
  datasette install datasette-column-sum
  ```
  ## Usage

  Numeric columns (float and integer) will have a new "Sum this column" menu item in their column action menu (accessible via the cog icon).

  Text columns get options to calculate sums for those numeric columns grouped by the text column.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:
  ```bash
  cd datasette-column-sum
  python3 -m venv venv
  source venv/bin/activate
  ```
  Now install the dependencies and test dependencies:
  ```bash
  pip install -e '.[test]'
  ```
  To run the tests:
  ```bash
  pytest
  ```

project_urls:
  {"CI": "https://github.com/datasette/datasette-column-sum/actions", "Changelog": "https://github.com/datasette/datasette-column-sum/releases", "Homepage": "https://github.com/datasette/datasette-column-sum", "Issues": "https://github.com/datasette/datasette-column-sum/issues"}

requires_dist:
  ["datasette >=1.0a12", "pytest-playwright ; extra == 'playwright'", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "datasette-test ; extra == 'test'", "nest-asyncio ; extra == 'test'"]


name: datasette-comments
summary: A Datasette plugin for commenting on tables, rows, and values
classifiers: ["License :: OSI Approved :: Apache Software License"]
author: Alex Garcia
description_content_type: text/markdown
license: Apache-2.0
package_url: https://pypi.org/project/datasette-comments/
project_url: https://pypi.org/project/datasette-comments/
release_url: https://pypi.org/project/datasette-comments/0.1.0/
version: 0.1.0
description:
  # datasette-comments

  [![PyPI](https://img.shields.io/pypi/v/datasette-comments.svg)](https://pypi.org/project/datasette-comments/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-comments?include_prereleases&label=changelog)](https://github.com/datasette/datasette-comments/releases)
  [![Tests](https://github.com/datasette/datasette-comments/workflows/Test/badge.svg)](https://github.com/datasette/datasette-comments/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-comments/blob/main/LICENSE)

  A Datasette plugin for commenting on tables, rows, and values. Work in progress, not ready yet!

  ## Installation

  Install this plugin in the same environment as Datasette.
  ```bash
  datasette install datasette-comments
  ```
  ## Usage

  Once installed, users with the `datasette-comments-access` will be able to view and add comments on rows within their Datasette instance.

  ## Plugin hooks

  This plugin provies the following plugin hook which can be used to customize its behavior:

  ### datasette_comments_users(datasette)

  This hook should return a list of dictionaries, each representing a user that should be made available to the plugin. Each dictionary should have the following keys:

  - `id`: A unique ID of the user, same as the actor ID.
  - `username`: A unique string that is used in searches and @ mentions.
  - `name`: A string of the user's natural name.
  - `profile_photo_url`: Optional URL to the user's profile pic.
  - `email`: Optional email used for gravatar profile photo, if enabled.

  The plugin hook can return a list, or it can return an awaitable function that returns a list.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:
  ```bash
  cd datasette-comments
  python3 -m venv venv
  source venv/bin/activate
  ```
  Now install the dependencies and test dependencies:
  ```bash
  pip install -e '.[test]'
  ```
  And for the JavaScript dependencies (needed to run a JavaScript build):
  ```bash
  npm install
  ```
  To run the tests:
  ```bash
  pytest
  ```

  To rebuild the minified JavaScript after making a change to a `.ts` or `.tsx` file:

  ```bash
  just js
  ```

project_urls:
  {"CI": "https://github.com/datasette/datasette-comments/actions", "Changelog": "https://github.com/datasette/datasette-comments/releases", "Homepage": "https://github.com/datasette/datasette-comments", "Issues": "https://github.com/datasette/datasette-comments/issues"}

requires_dist:
  ["datasette >=1.0a6", "python-ulid", "sqlite-migrate ==0.1b0", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "pytest-playwright ; extra == 'test'", "syrupy ; extra == 'test'"]


name: datasette-configure-asgi
summary: Datasette plugin for configuring arbitrary ASGI middleware
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-configure-asgi
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-configure-asgi/
project_url: https://pypi.org/project/datasette-configure-asgi/
project_urls: {"Homepage": "https://github.com/simonw/datasette-configure-asgi"}
release_url: https://pypi.org/project/datasette-configure-asgi/0.1/
version: 0.1
description:
  # datasette-configure-asgi

  [![PyPI](https://img.shields.io/pypi/v/datasette-configure-asgi.svg)](https://pypi.org/project/datasette-configure-asgi/)
  [![CircleCI](https://circleci.com/gh/simonw/datasette-configure-asgi.svg?style=svg)](https://circleci.com/gh/simonw/datasette-configure-asgi)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-configure-asgi/blob/master/LICENSE)

  Datasette plugin for configuring arbitrary ASGI middleware

  ## Installation

      pip install datasette-configure-asgi

  ## Usage

  This plugin only takes effect if your `metadata.json` file contains relevant top-level plugin configuration in a `"datasette-configure-asgi"` configuration key.

  For example, to wrap your Datasette instance in the `asgi-log-to-sqlite` middleware configured to write logs to `/tmp/log.db` you would use the following:

  ```json
  {
      "plugins": {
          "datasette-configure-asgi": [
              {
                  "class": "asgi_log_to_sqlite.AsgiLogToSqlite",
                  "args": {
                      "file": "/tmp/log.db"
                  }
              }
          ]
      }
  }
  ```

  The `"datasette-configure-asgi"` key should be a list of JSON objects. Each object should have a `"class"` key indicating the class to be used, and an optional `"args"` key providing any necessary arguments to be passed to that class constructor.

  ## Plugin structure

  This plugin can be used to wrap your Datasette instance in any ASGI middleware that conforms to the following structure:

  ```python
  class SomeAsgiMiddleware:
      def __init__(self, app, arg1, arg2):
          self.app = app
          self.arg1 = arg1
          self.arg2 = arg2

      async def __call__(self, scope, receive, send):
          start = time.time()
          await self.app(scope, receive, send)
          end = time.time()
          print("Time taken: {}".format(end - start))
  ```

  So the middleware is a class with a constructor which takes the wrapped application as a first argument, `app`, followed by further named arguments to configure the middleware. It provides an `async def __call__(self, scope, receive, send)` method to implement the middleware's behavior.

requires_dist:
  ["pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "asgiref (==3.1.2) ; extra == 'test'", "datasette ; extra == 'test'"]


name: datasette-configure-fts
summary: Datasette plugin for enabling full-text search against selected table columns
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-configure-fts
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-configure-fts/
project_url: https://pypi.org/project/datasette-configure-fts/
project_urls: {"Homepage": "https://github.com/simonw/datasette-configure-fts"}
provides_extra: ["test"]
release_url: https://pypi.org/project/datasette-configure-fts/1.1.4/
version: 1.1.4
description:
  # datasette-configure-fts

  [![PyPI](https://img.shields.io/pypi/v/datasette-configure-fts.svg)](https://pypi.org/project/datasette-configure-fts/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-configure-fts?include_prereleases&label=changelog)](https://github.com/simonw/datasette-configure-fts/releases)
  [![Tests](https://github.com/simonw/datasette-configure-fts/workflows/Test/badge.svg)](https://github.com/simonw/datasette-configure-fts/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-configure-fts/blob/main/LICENSE)

  Datasette plugin for enabling full-text search against selected table columns

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ datasette install datasette-configure-fts

  ## Usage

  Having installed the plugin, visit `/-/configure-fts` on your Datasette instance to configure FTS for tables on attached writable databases.

  Any time you have permission to configure FTS for a table a menu item will appear in the table actions menu on the table page.

  By default only [the root actor](https://datasette.readthedocs.io/en/stable/authentication.html#using-the-root-actor) can access the page - so you'll need to run Datasette with the `--root` option and click on the link shown in the terminal to sign in and access the page.

  The `configure-fts` permission governs access. You can use permission plugins such as [datasette-permissions-sql](https://github.com/simonw/datasette-permissions-sql) to grant additional access to the write interface.

requires_dist:
  ["datasette>=0.51", "sqlite-utils>=2.10", "pytest; extra == \"test\"", "pytest-asyncio; extra == \"test\"", "httpx; extra == \"test\""]


name: datasette-cookies-for-magic-parameters
summary: UI for setting cookies to populate magic parameters
classifiers: ["Framework :: Datasette", "License :: OSI Approved :: Apache Software License"]
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-cookies-for-magic-parameters
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-cookies-for-magic-parameters/
project_url: https://pypi.org/project/datasette-cookies-for-magic-parameters/
release_url: https://pypi.org/project/datasette-cookies-for-magic-parameters/0.1.2/
requires_dist: ["datasette", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'"]
requires_python: >=3.7
version: 0.1.2
description:
  # datasette-cookies-for-magic-parameters

  [![PyPI](https://img.shields.io/pypi/v/datasette-cookies-for-magic-parameters.svg)](https://pypi.org/project/datasette-cookies-for-magic-parameters/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-cookies-for-magic-parameters?include_prereleases&label=changelog)](https://github.com/simonw/datasette-cookies-for-magic-parameters/releases)
  [![Tests](https://github.com/simonw/datasette-cookies-for-magic-parameters/workflows/Test/badge.svg)](https://github.com/simonw/datasette-cookies-for-magic-parameters/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-cookies-for-magic-parameters/blob/main/LICENSE)

  UI for setting cookies to populate magic parameters

  ## Installation

  Install this plugin in the same environment as Datasette.

      datasette install datasette-cookies-for-magic-parameters

  ## Usage

  This plugin only affects canned queries. If you have a canned query configured like this:

  ```yaml
  databases:
    mydatabase:
      queries:
        api_query:
          sql: select 'Your API key is' || :_cookie_openai_api_token;
  ```
  Then the `/mydatabase/api_query` page will include a new interface for setting the `openapi_api_token` cookie, or unsetting it if it has already been set.

  ## Why use this?

  This plugin was initially developed to use with [datasette-openai](https://github.com/simonw/datasette-openai) - a plugin that provides custom SQL functions that take an API token as one of their parameters.

  Passing these API tokens in a GET query string is unsafe, as they may leak through referrers or other log files.

  Instead, this plugin enables them to be set and passed using a cookie, which is much less likely to be logged.

  This pattern takes advantage of the `:_cookie_x` feature of Datasette's [magic parameters](https://docs.datasette.io/en/stable/sql_queries.html#magic-parameters) mechanism.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-cookies-for-magic-parameters
      python3 -m venv venv
      source venv/bin/activate

  Now install the dependencies and test dependencies:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-cookies-for-magic-parameters/actions", "Changelog": "https://github.com/simonw/datasette-cookies-for-magic-parameters/releases", "Homepage": "https://github.com/simonw/datasette-cookies-for-magic-parameters", "Issues": "https://github.com/simonw/datasette-cookies-for-magic-parameters/issues"}


name: datasette-copy-to-memory
summary: Copy database files into an in-memory database on startup
classifiers: ["Framework :: Datasette", "License :: OSI Approved :: Apache Software License"]
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-copy-to-memory
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-copy-to-memory/
project_url: https://pypi.org/project/datasette-copy-to-memory/
release_url: https://pypi.org/project/datasette-copy-to-memory/0.2/
requires_python: >=3.7
version: 0.2
description:
  # datasette-copy-to-memory

  [![PyPI](https://img.shields.io/pypi/v/datasette-copy-to-memory.svg)](https://pypi.org/project/datasette-copy-to-memory/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-copy-to-memory?include_prereleases&label=changelog)](https://github.com/simonw/datasette-copy-to-memory/releases)
  [![Tests](https://github.com/simonw/datasette-copy-to-memory/workflows/Test/badge.svg)](https://github.com/simonw/datasette-copy-to-memory/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-copy-to-memory/blob/main/LICENSE)

  Copy database files into an in-memory database on startup

  This plugin is **highly experimental**. It currently exists to support Datasette performance research, and is not designed for actual production usage.

  ## Installation

  Install this plugin in the same environment as Datasette.

      datasette install datasette-copy-to-memory

  ## Usage

  On startup, Datasette will create an in-memory named database for each attached database. This database will have the same name but with `_memory` at the end.

  So running this:

      datasette fixtures.db

  Will serve two databases: the original at `/fixtures` and the in-memory copy at `/fixtures_memory`.

  ## Demo

  A demo is running on [latest-with-plugins.datasette.io](https://latest-with-plugins.datasette.io/) - the [/fixtures_memory](https://latest-with-plugins.datasette.io/fixtures_memory) table there is provided by this plugin.

  ## Configuration

  By default every attached database file will be loaded into a `_memory` copy.

  You can use plugin configuration to specify just a subset of the database. For example, to create `github_memory` but not `fixtures_memory` you would use the following `metadata.yml` file:

  ```yaml
  plugins:
    datasette-copy-to-memory:
      databases:
      - github
  ```
  Then start Datasette like this:

      datasette github.db fixtures.db -m metadata.yml

  If you don't want to have a `fixtures` and `fixtures_memory` database, you can use `replace: true` to have the plugin replace the file-backed database with the new in-memory one, reusing the same database name:

  ```yaml
  plugins:
    datasette-copy-to-memory:
      replace: true
  ```
  Then:

      datasette github.db fixtures.db -m metadata.yml

  This will result in both `/github` and `/fixtures` but no `/github_memory` or `/fixtures_memory`.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-copy-to-memory
      python3 -m venv venv
      source venv/bin/activate

  Now install the dependencies and test dependencies:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-copy-to-memory/actions", "Changelog": "https://github.com/simonw/datasette-copy-to-memory/releases", "Homepage": "https://github.com/simonw/datasette-copy-to-memory", "Issues": "https://github.com/simonw/datasette-copy-to-memory/issues"}

requires_dist:
  ["datasette", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "sqlite-utils ; extra == 'test'"]


name: datasette-copyable
summary: Datasette plugin for outputting tables in formats suitable for copy and paste
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-copyable
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-copyable/
project_url: https://pypi.org/project/datasette-copyable/
release_url: https://pypi.org/project/datasette-copyable/0.3.2/
requires_python: >=3.7
version: 0.3.2
description:
  # datasette-copyable

  [![PyPI](https://img.shields.io/pypi/v/datasette-copyable.svg)](https://pypi.org/project/datasette-copyable/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-copyable?include_prereleases&label=changelog)](https://github.com/simonw/datasette-copyable/releases)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-copyable/blob/master/LICENSE)

  Datasette plugin for outputting tables in formats suitable for copy and paste

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ pip install datasette-copyable

  ## Demo

  You can try this plugin on [fivethirtyeight.datasettes.com](https://fivethirtyeight.datasettes.com/) - browse for tables or queries there and look for the "copyable" link. Here's an example for a table of [airline safety data](https://fivethirtyeight.datasettes.com/fivethirtyeight/airline-safety~2Fairline-safety.copyable).

  ## Usage

  This plugin adds a `.copyable` output extension to every table, view and query.

  Navigating to this page will show an interface allowing you to select a format for copying and pasting the demo. The default is TSV, which is suitable for copying into Google Sheets or Excel.

  You can add `?_raw=1` to get back just the raw data.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-copyable
      python3 -mvenv venv
      source venv/bin/activate

  Or if you are using `pipenv`:

      pipenv shell

  Now install the dependencies and tests:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-copyable/actions", "Changelog": "https://github.com/simonw/datasette-copyable/releases", "Homepage": "https://github.com/simonw/datasette-copyable", "Issues": "https://github.com/simonw/datasette-copyable/issues"}

requires_dist:
  ["datasette (>=0.63)", "tabulate", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "httpx ; extra == 'test'", "sqlite-utils ; extra == 'test'"]


name: datasette-cors
summary: Datasette plugin for configuring CORS headers
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-cors
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-cors/
project_url: https://pypi.org/project/datasette-cors/
project_urls: {"Homepage": "https://github.com/simonw/datasette-cors"}
release_url: https://pypi.org/project/datasette-cors/1.0.1/
version: 1.0.1
description:
  # datasette-cors

  [![PyPI](https://img.shields.io/pypi/v/datasette-cors.svg)](https://pypi.org/project/datasette-cors/)
  [![Tests](https://github.com/simonw/datasette-cors/actions/workflows/test.yml/badge.svg)](https://github.com/simonw/datasette-cors/actions/workflows/test.yml)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-cors?include_prereleases&label=changelog)](https://github.com/simonw/datasette-cors/releases)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-cors/blob/main/LICENSE)

  Datasette plugin for configuring CORS headers, based on [asgi-cors](https://github.com/simonw/asgi-cors).

  You can use this plugin to allow JavaScript running on an allowlisted set of domains to make `fetch()` calls to the JSON API provided by your Datasette instance.

  ## Installation
  ```bash
  datasette install datasette-cors
  ```
  ## Configuration

  You need to add some plugin configuration for this plugin to take effect.

  To allowlist specific domains, use this:

  ```json
  {
      "plugins": {
          "datasette-cors": {
              "hosts": ["https://www.example.com"]
          }
      }
  }
  ```
  This affects the `access-control-allow-origin` header.

  You can also allowlist host patterns like this:

  ```json
  {
      "plugins": {
          "datasette-cors": {
              "host_wildcards": ["https://*.example.com"]
          }
      }
  }
  ```

  To allow all origins, use:

  ```json
  {
      "plugins": {
          "datasette-cors": {
              "allow_all": true
          }
      }
  }
  ```
  This sets the `access-control-allow-origin` header to `*`.

  You can specify allowed headers - with the `access-control-allow-headers` header - using the `headers` option:

  ```json
  {
      "plugins": {
          "datasette-cors": {
              "allow_all": true,
              "headers": ["Authorization", "Content-Type"]
          }
      }
  }
  ```

  To allow specific HTTP methods with the `access-control-allow-methods` header, use the `methods` option:

  ```json
  {
      "plugins": {
          "datasette-cors": {
              "allow_all": true,
              "methods": ["GET", "POST", "OPTIONS"]
          }
      }
  }
  ```

  You can set the `access-control-max-age` header using the `max_age` option:

  ```json
  {
      "plugins": {
          "datasette-cors": {
              "allow_all": true,
              "max_age": 3600
          }
      }
  }
  ```

  ## Testing it

  To test this plugin out, run it locally by saving one of the above examples as `metadata.json` and running this:
  ```bash
  datasette -m metadata.json
  ```
  With Datasette 1.0 use `-c config.json` instead, or try this:
  ```bash
  datasette -s plugins.datasette-cors.allow_all true
  ```

  Now visit https://www.example.com/ in your browser, open the browser developer console and paste in the following:

  ```javascript
  fetch("http://127.0.0.1:8001/_memory.json?sql=select+sqlite_version%28%29").then(r => r.json()).then(console.log)
  ```

  If the plugin is working correctly, you will see the JSON response output to the console.

requires_dist:
  ["asgi-cors>=1.0.1", "datasette; extra == \"test\"", "pytest; extra == \"test\"", "pytest-asyncio; extra == \"test\"", "httpx; extra == \"test\""]


name: datasette-create-view
summary: Create a SQL view from a query
classifiers: ["Framework :: Datasette", "License :: OSI Approved :: Apache Software License"]
author: Simon Willison
description_content_type: text/markdown
license: Apache-2.0
package_url: https://pypi.org/project/datasette-create-view/
project_url: https://pypi.org/project/datasette-create-view/
release_url: https://pypi.org/project/datasette-create-view/0.1/
requires_dist: ["datasette >=1.0a12", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'"]
requires_python: >=3.8
version: 0.1
description:
  # datasette-create-view

  [![PyPI](https://img.shields.io/pypi/v/datasette-create-view.svg)](https://pypi.org/project/datasette-create-view/)
  [![Changelog](https://img.shields.io/github/v/release/datasette/datasette-create-view?include_prereleases&label=changelog)](https://github.com/datasette/datasette-create-view/releases)
  [![Tests](https://github.com/datasette/datasette-create-view/actions/workflows/test.yml/badge.svg)](https://github.com/datasette/datasette-create-view/actions/workflows/test.yml)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-create-view/blob/main/LICENSE)

  Create a SQL view from a query

  ## Installation

  Install this plugin in the same environment as Datasette.
  ```bash
  datasette install datasette-create-view
  ```
  ## Usage

  Users with the `create-table` permission will get a new menu item on the query results page offering to save the query as a view.

  This option will only show for SQL queries that do not use `:named` parameters.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:
  ```bash
  cd datasette-create-view
  python3 -m venv venv
  source venv/bin/activate
  ```
  Now install the dependencies and test dependencies:
  ```bash
  pip install -e '.[test]'
  ```
  To run the tests:
  ```bash
  pytest
  ```

project_urls:
  {"CI": "https://github.com/datasette/datasette-create-view/actions", "Changelog": "https://github.com/datasette/datasette-create-view/releases", "Homepage": "https://github.com/datasette/datasette-create-view", "Issues": "https://github.com/datasette/datasette-create-view/issues"}


name: datasette-css-properties
summary: Experimental Datasette output plugin using CSS properties
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-css-properties
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-css-properties/
project_url: https://pypi.org/project/datasette-css-properties/
release_url: https://pypi.org/project/datasette-css-properties/0.2/
requires_dist: ["datasette", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'"]
requires_python: >=3.6
version: 0.2
description:
  # datasette-css-properties

  [![PyPI](https://img.shields.io/pypi/v/datasette-css-properties.svg)](https://pypi.org/project/datasette-css-properties/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-css-properties?include_prereleases&label=changelog)](https://github.com/simonw/datasette-css-properties/releases)
  [![Tests](https://github.com/simonw/datasette-css-properties/workflows/Test/badge.svg)](https://github.com/simonw/datasette-css-properties/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-css-properties/blob/main/LICENSE)

  Extremely experimental Datasette output plugin using CSS properties, inspired by [Custom Properties as State](https://css-tricks.com/custom-properties-as-state/) by Chris Coyier.

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ datasette install datasette-css-properties

  ## Usage

  Once installed, this plugin adds a `.css` output format to every query result. This will return the first row in the query as a valid CSS file, defining each column as a custom property:

  Example: https://latest-with-plugins.datasette.io/fixtures/roadside_attractions.css produces:

  ```css
  :root {
    --pk: '1';
    --name: 'The Mystery Spot';
    --address: '465 Mystery Spot Road, Santa Cruz, CA 95065';
    --latitude: '37.0167';
    --longitude: '-122.0024';
  }
  ```

  If you link this stylesheet to your page you can then do things like this;

  ```html
  <link rel="stylesheet" href="https://latest-with-plugins.datasette.io/fixtures/roadside_attractions.css">
  <style>
  .attraction-name:after { content: var(--name); }
  </style>
  <p class="attraction-name">Attraction name: </p>
  ```

  Values will be quoted as CSS strings by default. If you want to return a "raw" value without the quotes - for example to set a CSS property that is numeric or a color, you can specify that column name using the `?_raw=column-name` parameter. This can be passed multiple times.

  Consider [this example query](https://latest-with-plugins.datasette.io/github?sql=select%0D%0A++%27%23%27+||+substr(sha%2C+0%2C+6)+as+[custom-bg]%0D%0Afrom%0D%0A++commits%0D%0Aorder+by%0D%0A++author_date+desc%0D%0Alimit%0D%0A++1%3B):

  ```sql
  select
    '#' || substr(sha, 0, 6) as [custom-bg]
  from
    commits
  order by
    author_date desc
  limit
    1;
  ```

  This returns the first 6 characters of the most recently authored commit with a `#` prefix. The `.css` [output rendered version](https://latest-with-plugins.datasette.io/github.css?sql=select%0D%0A++%27%23%27+||+substr(sha%2C+0%2C+6)+as+[custom-bg]%0D%0Afrom%0D%0A++commits%0D%0Aorder+by%0D%0A++author_date+desc%0D%0Alimit%0D%0A++1%3B) looks like this:

  ```css
  :root {
    --custom-bg: '#97fb1';
  }
  ```

  Adding `?_raw=custom-bg` to the URL produces [this instead](https://latest-with-plugins.datasette.io/github.css?sql=select%0D%0A++%27%23%27+||+substr(sha%2C+0%2C+6)+as+[custom-bg]%0D%0Afrom%0D%0A++commits%0D%0Aorder+by%0D%0A++author_date+desc%0D%0Alimit%0D%0A++1%3B&_raw=custom-bg):

  ```css
  :root {
    --custom-bg: #97fb1;
  }
  ```

  This can then be used as a color value like so:

  ```css
  h1 {
      background-color: var(--custom-bg);
  }
  ```

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-css-properties
      python3 -mvenv venv
      source venv/bin/activate

  Or if you are using `pipenv`:

      pipenv shell

  Now install the dependencies and tests:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-css-properties/actions", "Changelog": "https://github.com/simonw/datasette-css-properties/releases", "Homepage": "https://github.com/simonw/datasette-css-properties", "Issues": "https://github.com/simonw/datasette-css-properties/issues"}


name: datasette-dashboards
summary: Datasette plugin to create interactive data dashboards
author_email: Romain Clement <git@romain-clement.net>
description_content_type: text/markdown
keywords: dashboards, datasette, vega, visualization
license_expression: Apache-2.0
license_files: ["LICENSE"]
package_url: https://pypi.org/project/datasette-dashboards/
project_url: https://pypi.org/project/datasette-dashboards/
release_url: https://pypi.org/project/datasette-dashboards/0.8.0/
requires_dist: ["datasette", "datasette-leaflet", "datasette-render-markdown"]
requires_python: <3.15,>=3.10
version: 0.8.0
classifiers:
  ["Development Status :: 4 - Beta", "Environment :: Web Environment", "Framework :: Datasette", "Intended Audience :: Developers", "Intended Audience :: End Users/Desktop", "Intended Audience :: Information Technology", "Intended Audience :: Other Audience", "Intended Audience :: Science/Research", "Operating System :: OS Independent", "Programming Language :: JavaScript", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "Programming Language :: Python :: 3.14", "Programming Language :: SQL", "Topic :: Database", "Topic :: Database :: Front-Ends", "Topic :: Scientific/Engineering :: Visualization", "Topic :: Utilities", "Typing :: Typed"]

description:
  # datasette-dashboards

  > Datasette plugin to create interactive data dashboards

  [![PyPI](https://img.shields.io/pypi/v/datasette-dashboards.svg)](https://pypi.org/project/datasette-dashboards/)
  [![CI/CD](https://github.com/rclement/datasette-dashboards/actions/workflows/ci-cd.yml/badge.svg)](https://github.com/rclement/datasette-dashboards/actions/workflows/ci-cd.yml)
  [![Coverage Status](https://img.shields.io/codecov/c/github/rclement/datasette-dashboards)](https://codecov.io/gh/rclement/datasette-dashboards)
  [![License](https://img.shields.io/github/license/rclement/datasette-dashboards)](https://github.com/rclement/datasette-dashboards/blob/master/LICENSE)

  Try out a live demo at [https://datasette-dashboards-demo.vercel.app](https://datasette-dashboards-demo.vercel.app/-/dashboards)

  **WARNING**: this plugin is still experimental and not ready for production.
  Some breaking changes might happen between releases before reaching a stable version.
  Use it at your own risks!

  ![Datasette Dashboards Demo](https://raw.githubusercontent.com/rclement/datasette-dashboards/master/demo/datasette-dashboards-demo.png)

  ## Installation

  Install this plugin in the same environment as Datasette:

  ```bash
  $ datasette install datasette-dashboards
  ```

  ## Usage

  Define dashboards within `metadata.yml` / `metadata.json`:

  ```yaml
  plugins:
    datasette-dashboards:
      my-dashboard:
        title: My Dashboard
        description: Showing some nice metrics
        layout:
          - [analysis-note, events-count]
          - [analysis-note, events-source]
        filters:
          date_start:
            name: Date Start
            type: date
            default: "2021-01-01"
          date_end:
            name: Date End
            type: date
          category:
            name: My Category
            type: select
            options: [Option 1, Option 2, Option 3]
          dynamic_category:
            name: My Dynamic Category
            type: select
            db: jobs
            query: SELECT DISTINCT col1 FROM mytable ORDER BY col1 ASC
          dependent_filter:
            name: My Dependent Filter
            type: select
            db: jobs
            query: SELECT DISTINCT col2 FROM mytable WHERE col2 IS NOT NULL [[ AND col1 = :dynamic_category ]] ORDER BY col2 ASC
        charts:
          analysis-note:
            library: markdown
            settings:
              extensions:
                - attr_list
              extra_attrs:
                h1:
                  - name
            display: |-
              # Analysis notes {: name="notes" }
              > A quick rundown of events statistics and KPIs

          events-count:
            title: Total number of events
            db: jobs
            query: SELECT count(*) as count FROM events
            library: metric
            display:
              field: count
              prefix:
              suffix:

          events-source:
            title: Number of events by source
            db: jobs
            query: SELECT source, count(*) as count FROM events WHERE TRUE [[ AND date >= date(:date_start) ]] [[ AND date <= date(:date_end) ]] GROUP BY source ORDER BY count DESC
            library: vega-lite
            display:
              mark: { type: arc, tooltip: true }
              encoding:
                color: { field: source, type: nominal }
                theta: { field: count, type: quantitative }
  ```

  A new menu entry is now available, pointing at `/-/dashboards` to access all defined dashboards.

  ### Properties

  Dashboard properties:

  | Property      | Type     | Description           |
  | ------------- | -------- | --------------------- |
  | `title`       | `string` | Dashboard title       |
  | `description` | `string` | Dashboard description |
  | `settings`    | `object` | Dashboard settings    |
  | `layout`      | `array`  | Dashboard layout      |
  | `filters`     | `object` | Dashboard filters     |

  Dashboard settings:

  | Property           | Type     | Description                                                    |
  | ------------------ | -------- | -------------------------------------------------------------- |
  | `allow_fullscreen` | `bool`   | Allow dashboard to be toggled in fullscreen  (default `false`) |
  | `autorefresh`      | `number` | Auto-refresh timeout in minutes                                |

  Dashboard filters:

  | Property  | Type               | Description                                      |
  | --------- | ------------------ | ------------------------------------------------ |
  | `name`    | `string`           | Filter display name                              |
  | `type`    | `string`           | Filter type (`text`, `date`, `number`, `select`) |
  | `default` | `string`, `number` | (optional) Filter default value                  |
  | `min`     | `number`           | (optional) Filter minimum value                  |
  | `max`     | `number`           | (optional) Filter maximum value                  |
  | `step`    | `number`           | (optional) Filter stepping value                 |
  | `options` | `list`             | (optional) Select filter options list            |
  | `db`      | `string`           | (optional) Dynamic select filter database        |
  | `query`   | `string`           | (optional) Dynamic select filter query           |

  Common chart properties for all chart types:

  | Property  | Type     | Description                                                                           |
  | --------- | -------- | ------------------------------------------------------------------------------------- |
  | `title`   | `string` | Chart title                                                                           |
  | `db`      | `string` | Database name against which to run the query                                          |
  | `query`   | `string` | SQL query to run and extract data from                                                |
  | `library` | `string` | One of supported libraries: `vega`, `vega-lite`, `markdown`, `metric`, `table`, `map` |
  | `display` | `object` | Chart display specification (depend on the used library)                              |

  To define SQL queries using dashboard filters:

  ```sql
  SELECT * FROM mytable [[ WHERE col >= :my_filter ]]
  ```

  ```sql
  SELECT * FROM mytable WHERE TRUE [[ AND col1 = :my_filter_1 ]] [[ AND col2 = :my_filter_2 ]]
  ```

  **Important notes:**

  - When a `select` filter has more than 100 options, the dropdown list will be automatically converted to a text filter with autocompletion

  #### Vega properties

  Available configuration for `vega` charts:

  | Property  | Type     | Description               |
  | --------- | -------- | ------------------------- |
  | `library` | `string` | Must be set to `vega`     |
  | `display` | `object` | Vega specification object |

  Notes about the `display` property:

  - Requires a valid [Vega specification object](https://vega.github.io/vega/docs/)
  - Some fields are pre-defined: `$schema`, `description`, `autosize`, `data`, `signals`
  - All fields are passed along as-is (overriding pre-defined fields if any)
  - Only `mark` and `encoding` fields are required as the bare-minimum

  #### Vega-Lite properties

  Available configuration for `vega-lite` charts:

  | Property  | Type     | Description                |
  | --------- | -------- | -------------------------- |
  | `library` | `string` | Must be set to `vega-lite` |
  | `display` | `object` | Vega specification object  |

  Notes about the `display` property:

  - Requires a valid [Vega-Lite specification object](https://vega.github.io/vega-lite/docs/)
  - Some fields are pre-defined: `$schema`, `description`, `width`, `view`, `config`, `data`
  - All fields are passed along as-is (overriding pre-defined fields if any)
  - Only `mark` and `encoding` fields are required as the bare-minimum

  #### Markdown properties

  Available configuration for `markdown` chart:

  | Property   | Type     | Description                                       |
  | ---------- | -------- | ------------------------------------------------- |
  | `library`  | `string` | Must be set to `markdown`                         |
  | `display`  | `string` | Multi-line string containing the Markdown content |
  | `settings` | `object` | Markdown settings (see below)                     |

  Note :

  - Some common properties do not apply and can be omitted: `title`, `db`, `query`
  - Markdown rendering is done by [`datasette-render-markdown`](https://datasette.io/plugins/datasette-render-markdown)
  - To configure Markdown rendering, use the `settings` property.

  ##### Markdown settings

  Available configuration for Markdown rendering:

  | Property      | Type     | Description                                          |
  | ------------- | -------- | ---------------------------------------------------- |
  | `extensions`  | `array`  | List of extension entry points to enable             |
  | `extra_tags`  | `array`  | List of extra tags to allow                          |
  | `extra_attrs` | `object` | Mapping of tags to extra attributes allowed for them |

  Note :

  - These arguments are passed to the [`render_markdown()` function](https://github.com/simonw/datasette-render-markdown/blob/main/README.md#markdown-in-templates).
  - The `extra_tags` and `extra_attrs` are passed to [Bleach](https://bleach.readthedocs.io/).
    + `extra_attrs` is indeed required to be given [as a dict](https://bleach.readthedocs.io/en/latest/clean.html#as-a-dict).

  #### Metric properties

  Available configuration for `metric` chart:

  | Property         | Type     | Description                               |
  | ---------------- | -------- | ----------------------------------------- |
  | `library`        | `string` | Must be set to `metric`                   |
  | `display.field`  | `string` | Numerical field to be displayed as metric |
  | `display.prefix` | `string` | Prefix to be displayed before metric      |
  | `display.suffix` | `string` | Prefix to be displayed after metric       |

  Note:

  - The `display.field` must reference a single-numerical value from the SQL query
    (e.g. numerical `number` field in `SELECT count(*) as number FROM events`)

  #### Table properties

  There is no required configured in `display`, so you can either ignored or
  leave it empty for table charts.

  Some advice for a nice table chart:

  - Set proper column names in the `SELECT` clause
  - Limit the number of columns in the `SELECT` clause
  - Limit the number of rows with the `LIMIT` clause
  - Order the rows explicitely with the `ORDER BY` clause
  - Use SQLite string concatenation operator (`||`) to format column data (for instance to include HTML markup!)

  #### Map properties

  Available configuration for `map` chart:

  | Property                    | Type      | Description                                                                         |
  | --------------------------- | --------- | ----------------------------------------------------------------------------------- |
  | `library`                   | `string`  | Must be set to `map`                                                                |
  | `display.latitude_column`   | `string`  | Name of the latitude column (default: `latitude`)                                   |
  | `display.longitude_column`  | `string`  | Name of the latitude column (default: `longitude`)                                  |
  | `display.show_latlng_popup` | `boolean` | Whether or not to display latitude and longitude values in popup (default: `false`) |

  **Warning**: do not try to load more than a thousand rows for a map at the risk of
  slugginess and being unreadable. Make sensible use of the `LIMIT` clause to reduce
  the number of items to display on the map.

  ### Dashboard layout

  The default dashboard layout will present two charts per row (one per row on mobile).
  To make use of custom dashboard layout using [CSS Grid Layout](https://developer.mozilla.org/en-US/docs/Web/CSS/CSS_Grid_Layout),
  define the `layout` array property as a grid / matrix:

  - Each entry represents a row of charts
  - Each column is referring a chart by its property name
  - An empty slot in the grid can be specified using the `.` (_full stop_) placeholder

  **WARNINGS**:
  - All rows must specify the same number of columns
  - All charts must be placed somewhere on the custom layout

  Here is a simple 2x3 grid example with 4 different charts:

  ```yaml
  layout:
    - [chart1, chart2, chart3]
    - [chart1, chart4, chart4]
  ```

  Here is a more subtle example involving an empty spot at the end of the second row:

  ```yaml
  layout:
    - [chart1, chart2, chart3]
    - [chart1, chart4, .]
  ```

  ### Embedding dashboards and charts

  Dashboards can be embedded within an HTML page using an `iframe` element:

  ```html
  <iframe
    src="/-/dashboards/my-dashboard/embed?start_date=2023-01-01&end_date=2023-12-31"
    frameborder="0"
    width="100%"
    height="600"
    allowtransparency
  >
  </iframe>
  ```

  Same goes for charts:

  ```html
  <iframe
    src="/-/dashboards/my-dashboard/my-chart/embed?start_date=2023-01-01&end_date=2023-12-31"
    frameborder="0"
    width="100%"
    height="600"
    allowtransparency
  >
  </iframe>
  ```

  ## Development

  To set up this plugin locally, first checkout the code.
  Then create a new virtual environment and the required dependencies:

  ```bash
  uv sync
  ```

  To run the QA suite:

  ```bash
  uv run ruff check datasette_dashboards tests
  uv run mypy --strict datasette_dashboards tests
  uv run pytest -v --cov=datasette_dashboards --cov=tests --cov-branch --cov-report=term-missing tests
  ```

  ## Updating JS dependencies

  External JS dependencies are tracked and bundled using NPM and `package.json` (`package-lock.json` is not needed here):

  ```bash
  npm install --no-package-lock
  ```

  ## Demo

  With the developmnent environment setup, you can run the demo locally:

  ```bash
  uv run datasette \
    --metadata demo/metadata.yml \
    --template-dir demo/templates \
    demo/jobs.db
  ```

  ## License

  Licensed under Apache License, Version 2.0

  Copyright (c) 2021 - present Romain Clement

project_urls:
  {"Changelog": "https://github.com/rclement/datasette-dashboards/blob/main/CHANGELOG.md", "Issues": "https://github.com/rclement/datasette-dashboards/issues", "Repository": "https://github.com/rclement/datasette-dashboards"}


name: datasette-dateutil
summary: dateutil functions for Datasette
classifiers: []
author: Simon Willison
description_content_type: text/markdown
home_page: https://github.com/simonw/datasette-dateutil
license: Apache License, Version 2.0
package_url: https://pypi.org/project/datasette-dateutil/
project_url: https://pypi.org/project/datasette-dateutil/
release_url: https://pypi.org/project/datasette-dateutil/0.3/
version: 0.3
description:
  # datasette-dateutil

  [![PyPI](https://img.shields.io/pypi/v/datasette-dateutil.svg)](https://pypi.org/project/datasette-dateutil/)
  [![Changelog](https://img.shields.io/github/v/release/simonw/datasette-dateutil?include_prereleases&label=changelog)](https://github.com/simonw/datasette-dateutil/releases)
  [![Tests](https://github.com/simonw/datasette-dateutil/workflows/Test/badge.svg)](https://github.com/simonw/datasette-dateutil/actions?query=workflow%3ATest)
  [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/datasette-dateutil/blob/main/LICENSE)

  dateutil functions for Datasette

  ## Installation

  Install this plugin in the same environment as Datasette.

      $ datasette install datasette-dateutil

  ## Usage

  This function adds custom SQL functions that expose functionality from the [dateutil](https://dateutil.readthedocs.io/) Python library.

  Once installed, the following SQL functions become available:

  ### Parsing date strings

  - `dateutil_parse(text)` - returns an ISO8601 date string parsed from the text, or `null` if the input could not be parsed. `dateutil_parse("10 october 2020 3pm")` returns `2020-10-10T15:00:00`.
  - `dateutil_parse_fuzzy(text)` - same as `dateutil_parse()` but this also works against strings that contain a date somewhere within them - that date will be returned, or `null` if no dates could be found. `dateutil_parse_fuzzy("This is due 10 september")` returns `2020-09-10T00:00:00` (but will start returning the 2021 version of that if the year is 2021).

  The `dateutil_parse()` and `dateutil_parse_fuzzy()` functions both follow the American convention of assuming that `1/2/2020` lists the month first, evaluating this example to the 2nd of January.

  If you want to assume that the day comes first, use these two functions instead:

  - `dateutil_parse_dayfirst(text)`
  - `dateutil_parse_fuzzy_dayfirst(text)`

  Here's a query demonstrating these functions:

  ```sql
  select
    dateutil_parse("10 october 2020 3pm"),
    dateutil_parse_fuzzy("This is due 10 september"),
    dateutil_parse("1/2/2020"),
    dateutil_parse("2020-03-04"),
    dateutil_parse_dayfirst("2020-03-04");
  ```

  [Try that query](https://latest-with-plugins.datasette.io/fixtures?sql=select%0D%0A++dateutil_parse%28%2210+october+2020+3pm%22%29%2C%0D%0A++dateutil_parse_fuzzy%28%22This+is+due+10+september%22%29%2C%0D%0A++dateutil_parse%28%221%2F2%2F2020%22%29%2C%0D%0A++dateutil_parse%28%222020-03-04%22%29%2C%0D%0A++dateutil_parse_dayfirst%28%222020-03-04%22%29%3B)

  ### Optional default dates

  The `dateutil_parse()`, `dateutil_parse_fuzzy()`, `dateutil_parse_dayfirst()` and `dateutil_parse_fuzzy_dayfirst()` functions all accept an optional second argument specifying a "default" datetime to consider if some of the details are missing. For example, the following:
  ```sql
  select dateutil_parse('1st october', '1985-01-01')
  ```
  Will return `1985-10-01T00:00:00` - the missing year is replaced with the year from the default date.

  [Example query demonstrating the default date argument](https://latest-with-plugins.datasette.io/fixtures?sql=with+times+as+%28%0D%0A++select%0D%0A++++datetime%28%27now%27%29+as+t%0D%0A++union%0D%0A++select%0D%0A++++datetime%28%27now%27%2C+%27-1+year%27%29%0D%0A++union%0D%0A++select%0D%0A++++datetime%28%27now%27%2C+%27-3+years%27%29%0D%0A%29%0D%0Aselect+t%2C+dateutil_parse_fuzzy%28%22This+is+due+10+september%22%2C+t%29+from+times)

  ### Calculating Easter

  - `dateutil_easter(year)` - returns the date for Easter in that year, for example `dateutil_easter("2020")` returns `2020-04-12`.

  [Example Easter query](https://latest-with-plugins.datasette.io/fixtures?sql=select%0D%0A++dateutil_easter%282019%29%2C%0D%0A++dateutil_easter%282020%29%2C%0D%0A++dateutil_easter%282021%29)

  ### JSON arrays of dates

  Several functions return JSON arrays of date strings. These can be used with SQLite's `json_each()` function to perform joins against dates from a specific date range or recurrence rule.

  These functions can return up to 10,000 results. They will return an error if more than 10,000 dates would be returned - this is to protect against denial of service attacks.

  - `dateutil_dates_between('1 january 2020', '5 jan 2020')` - given two dates (in any format that can be handled by `dateutil_parse()`) this function returns a JSON string containing the dates between those two days, inclusive. This example returns `["2020-01-01", "2020-01-02", "2020-01-03", "2020-01-04", "2020-01-05"]`.
  - `dateutil_dates_between('1 january 2020', '5 jan 2020', 0)` - set the optional third argument to `0` to specify that you would like this to be exclusive of the last day. This example returns `["2020-01-01", "2020-01-02", "2020-01-03", "2020-01-04"]`.

  [Try these queries](https://latest-with-plugins.datasette.io/fixtures?sql=select%0D%0A++dateutil_dates_between%28%271+january+2020%27%2C+%275+jan+2020%27%29%2C%0D%0A++dateutil_dates_between%28%271+january+2020%27%2C+%275+jan+2020%27%2C+0%29)

  The `dateutil_rrule()` and `dateutil_rrule_date()` functions accept the iCalendar standard ``rrule` format - see [the dateutil documentation](https://dateutil.readthedocs.io/en/stable/rrule.html#rrulestr-examples) for more examples.

  This format lets you specify recurrence rules such as "the next four last mondays of the month".

  - `dateutil_rrule(rrule, optional_dtsart)` - given an rrule returns a JSON array of ISO datetimes. The second argument is optional and will be treated as the start date for the rule.
  - `dateutil_rrule_date(rrule, optional_dtsart)` - same as `dateutil_rrule()` but returns ISO dates.

  Example query:

  ```sql
  select
    dateutil_rrule('FREQ=HOURLY;COUNT=5'),
    dateutil_rrule_date(
      'FREQ=DAILY;COUNT=3',
      '1st jan 2020'
    );
  ```
  [Try the rrule example query](https://latest-with-plugins.datasette.io/fixtures?sql=select%0D%0A++dateutil_rrule('FREQ%3DHOURLY%3BCOUNT%3D5')%2C%0D%0A++dateutil_rrule_date(%0D%0A++++'FREQ%3DDAILY%3BCOUNT%3D3'%2C%0D%0A++++'1st+jan+2020'%0D%0A++)%3B)

  ### Joining data using json_each()

  SQLite's [json_each() function](https://www.sqlite.org/json1.html#jeach) can be used to turn a JSON array of dates into a table that can be joined against other data. Here's a query that returns a table showing every day in January 2019:

  ```sql
  select
    value as date
  from
    json_each(
      dateutil_dates_between('1 Jan 2019', '31 Jan 2019')
    )
  ```
  [Try that query](https://latest-with-plugins.datasette.io/fixtures?sql=select%0D%0A++value+as+date%0D%0Afrom%0D%0A++json_each%28%0D%0A++++dateutil_dates_between%28%271+Jan+2019%27%2C+%2731+Jan+2019%27%29%0D%0A++%29)

  You can run joins against this table by assigning it a name using SQLite's [support for Common Table Expressions (CTEs)](https://sqlite.org/lang_with.html).

  This example query uses `substr(created, 0, 11)` to retrieve the date portion of the `created` column in the [facetable demo table](https://latest-with-plugins.datasette.io/fixtures/facetable), then joins that against the table of days in January to calculate the count of rows created on each day. The `LEFT JOIN` against `days_in_january` ensures that days which had no created records are still returned in the results, with a count of 0.

  ```sql
  with created_dates as (
    select
      substr(created, 0, 11) as date
    from
      facetable
  ),
  days_in_january as (
    select
      value as date
    from
      json_each(
        dateutil_dates_between('1 Jan 2019', '31 Jan 2019')
      )
  )
  select
    days_in_january.date,
    count(created_dates.date) as total
  from
    days_in_january
    left join created_dates on days_in_january.date = created_dates.date
  group by
    days_in_january.date;
  ```
  [Try that query](https://latest-with-plugins.datasette.io/fixtures?sql=with+created_dates+as+%28%0D%0A++select%0D%0A++++substr%28created%2C+0%2C+11%29+as+date%0D%0A++from%0D%0A++++facetable%0D%0A%29%2C%0D%0Adays_in_january+as+%28%0D%0A++select%0D%0A++++value+as+date%0D%0A++from%0D%0A++++json_each%28%0D%0A++++++dateutil_dates_between%28%271+Jan+2019%27%2C+%2731+Jan+2019%27%29%0D%0A++++%29%0D%0A%29%0D%0Aselect%0D%0A++days_in_january.date%2C%0D%0A++count%28created_dates.date%29+as+total%0D%0Afrom%0D%0A++days_in_january%0D%0A++left+join+created_dates+on+days_in_january.date+%3D+created_dates.date%0D%0Agroup+by%0D%0A++days_in_january.date%3B#g.mark=bar&g.x_column=date&g.x_type=ordinal&g.y_column=total&g.y_type=quantitative) with a bar chart rendered using the [datasette-vega](https://github.com/simonw/datasette-vega) plugin.

  ## Development

  To set up this plugin locally, first checkout the code. Then create a new virtual environment:

      cd datasette-dateutil
      python3 -mvenv venv
      source venv/bin/activate

  Or if you are using `pipenv`:

      pipenv shell

  Now install the dependencies and tests:

      pip install -e '.[test]'

  To run the tests:

      pytest

project_urls:
  {"CI": "https://github.com/simonw/datasette-dateutil/actions", "Changelog": "https://github.com/simonw/datasette-dateutil/releases", "Homepage": "https://github.com/simonw/datasette-dateutil", "Issues": "https://github.com/simonw/datasette-dateutil/issues"}

requires_dist:
  ["datasette", "python-dateutil", "pytest ; extra == 'test'", "pytest-asyncio ; extra == 'test'", "httpx ; extra == 'test'"]


