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Api Docs Generator

Community
Mathews-Tom
api-docs-generator

Audits and enhances FastAPI and REST API documentation: missing descriptions, response codes, examples, docstrings, Pydantic models, OpenAPI spec. Triggers on: "generate API docs", "document this API", "OpenAPI for", "FastAPI docs", "document endpoints", "swagger docs".

Overview

PublisherMathews-Tom
Repositoryarmory
Skill nameapi-docs-generator
Stars
318
Forks
47
Bundled files
5
LicenseMIT
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Mathews-Tom on GitHub. Read the source before you install it.

Installation

Install the Api Docs Generator 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.
  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/Mathews-Tom/armory.git /tmp/armory
mkdir -p .claude/skills
cp -r /tmp/armory/skills/api-docs-generator .claude/skills/api-docs-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Api Docs Generator 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 Api Docs Generator 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 Api Docs Generator 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.

API Docs Generator

Audits API endpoint documentation for completeness, generates enhanced docstrings with proper parameter descriptions and examples, documents all response codes, and produces Pydantic model examples — bridging the gap between auto-generated OpenAPI specs and genuinely useful API documentation.

Reference Files

FileContentsLoad When
references/fastapi-patterns.mdFastAPI-specific documentation patterns, Path/Query/Body parameter docsFastAPI endpoint
references/example-generation.mdCreating realistic field examples, model_config patternsExample values needed
references/response-codes.mdStandard HTTP response documentation, error response schemasResponse documentation needed
references/openapi-enhancement.mdOpenAPI spec enrichment, tag organization, schema documentationOpenAPI spec review

Prerequisites

  • Access to the API source code (route definitions, models)
  • Framework identification (FastAPI, Flask, Django REST, Express)

Workflow

Phase 1: Analyze Endpoints

  1. Inventory endpoints — List all routes with HTTP method, path, handler function.
  2. Identify models — Request bodies (Pydantic models, dataclasses), response models, query parameters, path parameters.
  3. Map dependencies — Authentication requirements, middleware, shared dependencies.
  4. Read existing docs — Current docstrings, OpenAPI metadata, inline documentation.

Phase 2: Audit Documentation

For each endpoint, check:

CheckWhat to VerifyCommon Gap
Endpoint descriptionHandler has a docstringMissing or "TODO"
Parameter descriptionsEach param has description=Path params undocumented
Request exampleBody model has example= or json_schema_extraNo request example
Response modelresponse_model= specifiedReturns raw dict
Error responses4xx/5xx documented with responses=Only 200 documented
TagsEndpoint assigned to a tag groupUntagged endpoints

Phase 3: Generate Enhancements

  1. Docstrings — Write clear endpoint descriptions that explain purpose, not implementation. Include Raises section for documented errors.
  2. Parameter metadata — Add description, example, ge/le/regex to Path, Query, Body parameters.
  3. Model examples — Add Field(example=...) and model_config with json_schema_extra.
  4. Error responses — Document every possible error status code with response schema.
  5. Tags — Group endpoints by resource or feature area.

Phase 4: Output

Produce a coverage report and enhanced code.

Output Format

## API Documentation Audit

### Coverage Summary
| Metric | Count | Documented | Coverage |
|--------|-------|------------|----------|
| Endpoints | {N} | {M} | {%} |
| Parameters | {N} | {M} | {%} |
| Response codes | {N} | {M} | {%} |
| Models with examples | {N} | {M} | {%} |

### Gaps Identified

| # | Endpoint | Issue | Severity |
|---|----------|-------|----------|
| 1 | `{METHOD} {path}` | {issue} | {High/Medium/Low} |

### Enhanced Code

#### `{METHOD} {path}`

```python
@router.{method}(
    "{path}",
    response_model={ResponseModel},
    summary="{Short summary}",
    responses={{
        404: {{"description": "{Not found description}"}},
        422: {{"description": "Validation error"}},
    }},
    tags=["{tag}"],
)
async def {handler}(
    {param}: {type} = Path(..., description="{description}", example={example}),
) -> {ResponseModel}:
    """
    {Full description of what this endpoint does.}

    {Additional context about behavior, side effects, or important notes.}

    Raises:
        404: {Entity} not found
        403: Insufficient permissions
    """
Model: {ModelName}
python
class {ModelName}(BaseModel):
    {field}: {type} = Field(..., description="{description}", example={example})

    model_config = ConfigDict(
        json_schema_extra={{
            "example": {{
                "{field}": {example_value},
            }}
        }}
    )
text

## Calibration Rules

1. **Describe behavior, not implementation.** "Retrieves the user's profile" is good.
   "Calls `db.query(User).filter_by(id=id).first()`" is implementation leakage.
2. **Realistic examples.** `"alice@example.com"` not `"string"`. `42` not `0`.
   Examples serve as documentation — they should look like real data.
3. **Document every error code.** If the endpoint can return 404, document it. Users
   should never encounter an undocumented error response.
4. **Consistent style.** All endpoints in the same API should use the same documentation
   patterns — same tag naming, same description style, same example format.
5. **Don't duplicate the type system.** If the parameter type is `int`, don't write
   "An integer" as the description. Write what the integer represents: "Unique user
   identifier."

## Error Handling

| Problem | Resolution |
|---------|------------|
| Non-FastAPI framework | Adapt patterns. Document the HTTP contract regardless of framework. |
| No type hints on handlers | Infer types from usage, document uncertainty, suggest adding type hints. |
| Massive API (50+ endpoints) | Prioritize undocumented and public endpoints. Batch output by resource. |
| Generated API (OpenAPI → code) | Document at the spec level, not the generated code level. |
| Authentication varies by endpoint | Document auth requirements per endpoint group. |

## When NOT to Generate

Push back if:
- The API design itself is wrong (bad URL patterns, wrong HTTP methods) — fix the API first
- The user wants SDK generation from OpenAPI — different tool
- The code is a prototype that will change significantly — document after stabilization

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Api Docs Generator AI skill do?

Audits and enhances FastAPI and REST API documentation: missing descriptions, response codes, examples, docstrings, Pydantic models, OpenAPI spec. Triggers on: "generate API docs", "document this API", "OpenAPI for", "FastAPI docs", "document endpoints", "swagger docs".

Why use Api Docs Generator on TypingMind?

Because you install it once and use it with any model. Api Docs Generator 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 Api Docs Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mathews-Tom/armory/tree/main/skills/api-docs-generator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Api Docs Generator?

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 Api Docs Generator?

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

Is the Api Docs Generator AI skill free?

Yes. It is published on GitHub by Mathews-Tom under the MIT 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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