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Document

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nitinjain999
document

Generate, format, and validate code documentation — docstrings, JSDoc, OpenAPI/Swagger specs, documentation sites, and developer guides.

Overview

Publishernitinjain999
Repositoryplatform-skills
Skill namedocument
Stars
71
Forks
14
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 nitinjain999 on GitHub. Read the source before you install it.

Installation

Install the Document 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/nitinjain999/platform-skills/tree/main/plugins/platform-skills/skills/document
  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/nitinjain999/platform-skills.git /tmp/platform-skills
mkdir -p .claude/skills
cp -r /tmp/platform-skills/plugins/platform-skills/skills/document .claude/skills/document
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Document 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 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 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.

Generate or improve technical documentation for a codebase or API.

Mode: docstrings

Add or improve inline documentation for functions, classes, and modules.

Steps:

  1. Ask for: language, preferred style (Google / NumPy / Sphinx for Python; JSDoc for TypeScript/JavaScript)
  2. Identify all public functions and classes missing documentation
  3. For each, document: purpose, all parameters with types, return value, exceptions raised, and at least one example
  4. Validate examples compile and run:
    • Python: python -m doctest file.py or pytest --doctest-modules
    • TypeScript: tsc --noEmit
  5. Generate coverage report: interrogate --fail-under=80 src/ (Python) or npx typedoc-coverage (TypeScript)

Rules:

  • Do NOT document obvious getters/setters verbosely
  • Do NOT repeat the function signature in prose
  • Do NOT document private/internal methods unless they have complex invariants

Reference: references/documentation.md → Python Docstrings, TypeScript JSDoc

Mode: openapi

Generate or improve an OpenAPI 3.1 specification for a REST API.

Steps:

  1. Ask for: framework (FastAPI / Django / NestJS / Express / raw spec), existing routes or codebase to analyse
  2. Map all endpoints: method, path, request body schema, response schemas per status code
  3. Extract shared schemas into components/schemas
  4. Extract shared responses (400, 401, 404, 500) into components/responses
  5. Add operationId for every operation
  6. Add security scheme and apply globally or per-operation
  7. Validate: npx @redocly/cli lint openapi.yaml
  8. Generate HTML preview: npx @redocly/cli build-docs openapi.yaml

For FastAPI: document with Pydantic model Field(description=...) and docstrings on route functions — auto-generates /docs. For NestJS: use @ApiProperty, @ApiOperation, @ApiResponse decorators.

Reference: references/documentation.md → OpenAPI 3.1, FastAPI, NestJS

Mode: site

Set up a documentation site for a project.

Steps:

  1. Ask for: project type (Python library / TypeScript SDK / REST API / platform runbook)
  2. Recommend appropriate generator:
    • Python projects → MkDocs + mkdocstrings + Material theme
    • TypeScript projects → TypeDoc + typedoc-material-theme
    • API portals → Redocly or Stoplight
    • General docs → Docusaurus
  3. Generate site configuration file
  4. Define nav structure: Getting Started, API Reference, Guides, Changelog
  5. Wire docstring auto-generation from source code
  6. Add search plugin
  7. Provide serve and build commands

Reference: references/documentation.md → Documentation Sites

Mode: guide

Write a getting started guide or tutorial.

Structure every guide as:

  1. Prerequisites — exact versions, accounts, or permissions needed
  2. Installation — copy-paste commands, not prose descriptions
  3. Quick start — the simplest working example (< 10 lines)
  4. Next steps — links to deeper topics

Rules:

  • All code examples must be tested and runnable
  • Use realistic values, not <YOUR_VALUE> placeholders where avoidable
  • One concept per section — do not combine installation with configuration
  • Include expected output for each command

Reference: references/documentation.md → Getting Started Guide Structure

Frequently asked questions

What does the Document AI skill do?

Generate, format, and validate code documentation — docstrings, JSDoc, OpenAPI/Swagger specs, documentation sites, and developer guides.

Why use Document on TypingMind?

Because you install it once and use it with any model. Document 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 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nitinjain999/platform-skills/tree/main/plugins/platform-skills/skills/document. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Document?

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?

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

Is the Document AI skill free?

Yes. It is published on GitHub by nitinjain999 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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