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Document Api Endpoint

OrganizationPopular
getsentry
document-api-endpoint

Document and type a Sentry API endpoint. Write or fix @extend_schema decorators, specify response TypedDicts, type request parameters, correct type drift between the declared schema and the runtime response, and validate the generated spec. Use when asked to "document an endpoint", "add OpenAPI docs", "add/fix @extend_schema", "type an endpoint response", "fix the response type", "fix type drift", "reuse a response type", "split an overloaded endpoint", "specify the response schema", "add a TypedDict response", "migrate a legacy api-docs path", "fix a parameter type", or "make an endpoint public" / "promote an endpoint" (promotion is one section here).

Overview

Publishergetsentry
Repositoryskills
Skill namedocument-api-endpoint
Stars
1K
Forks
53
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by getsentry on GitHub. Read the source before you install it.

Installation

Install the Document Api Endpoint AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/getsentry/skills/tree/main/skills/document-api-endpoint
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/getsentry/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/document-api-endpoint .claude/skills/document-api-endpoint
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Document Api Endpoint in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Document Api Endpoint on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Document Api Endpoint is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Document & Type a Sentry API Endpoint

Add or fix OpenAPI docs for a Sentry endpoint with drf-spectacular. Full reference is at https://develop.sentry.dev/backend/api/public/, the most useful section to you will be https://develop.sentry.dev/backend/api/public/#5-method-decorator. This skill captures the non-obvious lessons on top of it. Most of the work is making the declared schema match what the endpoint actually returns. Before documenting, identify which endpoint class serves the route and what it does; the MCP tool that calls it is usually the fastest way to confirm its behavior. Promoting a PRIVATE/EXPERIMENTAL endpoint to PUBLIC is one application (see below).

Workflow

  1. Class-level @extend_schema(tags=[...]) — use the closest existing OPENAPI_TAGS entry.
  2. Method-level @extend_schema(operation_id=..., parameters=[...], responses={...}, examples=...).
  3. Reuse src/sentry/apidocs/parameters.py and examples/*.py; ensure owner = ApiOwner.<TEAM> is set.
  4. If a legacy api-docs/paths/**/*.json covers the path, remove it (see lesson 4).
  5. Validate, then verify against the live endpoint (lesson 1).

Lessons

1. Carefully compare what the code does vs declared types

Ideally, hit the live endpoint with a real token and diff the keys and types against your TypedDict. Serializers are sometimes inaccurate. Look out for counts coming back as floats instead of integers, IDs declared int emitted as strings, nested types declaring the wrong number of fields. Correct the declared type to match runtime.

bash
curl -s -H "Authorization: Bearer $TOKEN" "https://us.sentry.io/api/0/<endpoint>" | jq 'keys'

2. Reuse the canonical response type

Match the codebase's XxxResponseOptional(TypedDict, total=False) mixin (main class declares required fields). Nullable-vs-absent: T | None = key always present, value may be null; NotRequired[T] = key only set under a condition (e.g. an expand query param). Reuse the existing canonical type instead of re-declaring a second or third copy in a *_types.py. If there's no clean canonical type to reuse (e.g. a payload proxied from another service like vroom/profiling), type it dict[str, Any] rather than inventing a new mirror, and confirm the shape from the owning service's repo, not just the serializer.

3. Infer the type. Avoid cast and # type: ignore

When a serializer returns a base type plus extra fields, refactor the producing code so the response type is inferred rather than forced.

4. Legacy doc migration is all-or-nothing per path

Delete the api-docs/paths/**/*.json file AND its $ref in api-docs/openapi.json. drf-spectacular's APPEND_PATHS does not merge HTTP methods, so once any method on a path uses @extend_schema, all legacy methods on that path vanish — migrate every method on the path in one commit.

Promoting to PUBLIC

Do the workflow above, then on the concrete endpoint only (leave siblings PRIVATE):

  • Bump publish_status[<METHOD>] → PUBLIC and set owner = ApiOwner.<TEAM>.
  • Remove the method from API_OWNERSHIP_ALLOWLIST_DONT_MODIFY in the same change as the flip.
  • If the endpoint is redundant or being renamed, delete or deprecate the old version in its own change first, then stack the publish on top.
  • Note in the PR if scopes widen (event:read → event:{admin,read,write}) — that's drf-spectacular regenerating from permission_classes, documentation-only.

The change reaches the @sentry/api SDK / MCP only after sentry-api-schema regenerates downstream.

Validate

bash
make build-api-docs
pnpm run validate-api-examples
.venv/bin/pytest -q --reuse-db tests/apidocs/endpoints/<area>/test_<name>.py
.venv/bin/prek run -q --files <changed paths>

Frequently asked questions

What does the Document Api Endpoint AI skill do?

Document and type a Sentry API endpoint. Write or fix @extend_schema decorators, specify response TypedDicts, type request parameters, correct type drift between the declared schema and the runtime response, and validate the generated spec. Use when asked to "document an endpoint", "add OpenAPI docs", "add/fix @extend_schema", "type an endpoint response", "fix the response type", "fix type drift", "reuse a response type", "split an overloaded endpoint", "specify the response schema", "add a TypedDict response", "migrate a legacy api-docs path", "fix a parameter type", or "make an endpoint p...

Why use Document Api Endpoint on TypingMind?

Because you install it once and use it with any model. Document Api Endpoint is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Document Api Endpoint in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/getsentry/skills/tree/main/skills/document-api-endpoint. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Document Api Endpoint?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Document Api Endpoint?

As many as you like. As long as a model supports skills, you can use Document Api Endpoint with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Document Api Endpoint AI skill free?

Yes. It is published on GitHub by getsentry under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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