Agent skill

Generating Documentation

by ancoleman in ancoleman/ai-design-components

Generate comprehensive technical documentation including API docs (OpenAPI/Swagger), code documentation (TypeDoc/Sphinx), documentation sites (Docusaurus/MkDocs), Architecture Decision Records…

MITAuto-check passedDevelopment

Install Generating Documentation

skills CLI
$ npx skills add ancoleman/ai-design-components --skill generating-documentation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ancoleman/ai-design-components generating-documentation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ancoleman/ai-design-components.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/generating-documentation .claude/skills/generating-documentation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
generating-documentation
GitHub stars
526
Token cost
~3k tokens
SKILL.md length
694 words
Files
12 (incl. references)
Skills in repo
75
Repo updated
First seen
Licence
MIT

At a glance

Generate comprehensive technical documentation including API docs (OpenAPI/Swagger), code documentation (TypeDoc/Sphinx), documentation sites (Docusaurus/MkDocs), Architecture Decision Records…

  • Works in 4 steps: Write OpenAPI spec → Review with stakeholders → Generate server stubs → …
  • Documenting APIs
  • SKILL.md covers When to Use This Skill, Documentation Layers Overview, Quick Decision Framework and API Documentation Quick Start, plus 12 more sections
  • Runs TypeScript scripts from its folder; calls npm, npx and pip; needs GITHUB_TOKEN

What it does

Generating Documentation is an agent skill from ancoleman/ai-design-components. Generate comprehensive technical documentation including API docs (OpenAPI/Swagger), code documentation (TypeDoc/Sphinx), documentation sites (Docusaurus/MkDocs), Architecture Decision Records (ADRs), and diagrams (Mermaid/PlantUML). Use when documenting APIs, libraries, systems architecture, or building developer-facing documentation sites.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including reference files (for example `examples/adr/0001-database-selection.md`, `examples/typescript/tsdoc-examples.ts` and `outputs.yaml`).

It sits in Development, covering Technical documentation, OpenAPI specifications and Diagrams. It works with OpenAPI, Mermaid, Python and TypeScript. The repository describes itself as: Comprehensive UI/UX and Backend component design skills for AI-assisted development with Claude. The licence is MIT.

When your agent uses it

  • Documenting APIs
  • Systems architecture
  • Building developer-facing documentation sites

Example prompts

  • “/generating-documentation”

Requirements

  • Python 3
  • Node.js
  • A credential in GITHUB_TOKEN

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Write OpenAPI spec
  2. Review with stakeholders
  3. Generate server stubs
  4. Implement handlers

What it can do on your machine

Read from SKILL.md and the folder at commit 76551b7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (TypeScript), which the agent can run.

    Shell commands in SKILL.md call:

    • npm
    • npx
    • pip
    • make

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, npx and pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • GITHUB_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Generating Documentation loads about 3k tokens when it runs, and up to ~21k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 694 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~92
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~21k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ancoleman/ai-design-components at commit 76551b7, republished under its MIT licence (© ancoleman). 694 words, ~3,044 tokens.

Download SKILL.mdSave it as .claude/skills/generating-documentation/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
generating-documentation
description
Generate comprehensive technical documentation including API docs (OpenAPI/Swagger), code documentation (TypeDoc/Sphinx), documentation sites (Docusaurus/MkDocs), Architecture Decision Records (ADRs), and diagrams (Mermaid/PlantUML). Use when documenting APIs, libraries, systems architecture, or building developer-facing documentation sites.

Documentation Generation

Generate comprehensive technical documentation across multiple layers: API documentation, code documentation, documentation sites, architecture decisions, and system diagrams.

When to Use This Skill

Use this skill when:

  • Documenting REST or GraphQL APIs with OpenAPI specifications
  • Creating code documentation for libraries (TypeScript, Python, Go, Rust)
  • Building documentation sites for projects or products
  • Recording architectural decisions (ADRs) for system design choices
  • Generating diagrams to visualize system architecture or data flows
  • Setting up automated documentation pipelines in CI/CD

Documentation Layers Overview

Technical documentation operates at five distinct layers:

Layer 1: API Documentation - OpenAPI specs for REST/GraphQL APIs (Swagger UI, Redoc, Scalar) Layer 2: Code Documentation - Generated from code comments (TypeDoc, Sphinx, godoc, rustdoc) Layer 3: Documentation Sites - Comprehensive guides and tutorials (Docusaurus, MkDocs) Layer 4: Architecture Decisions - ADRs using MADR template format Layer 5: Diagrams - Visual architecture (Mermaid, PlantUML, D2)

See references/api-documentation.md, references/code-documentation.md, and references/documentation-sites.md for detailed guides.

Quick Decision Framework

Which Documentation Layer?
API for external consumers?
  → Layer 1: API Documentation (OpenAPI + Swagger UI/Redoc)

Code for maintainers?
  → Layer 2: Code Documentation (TypeDoc/Sphinx/godoc/rustdoc)

Comprehensive guides?
  → Layer 3: Documentation Site (Docusaurus/MkDocs)

Architectural decision?
  → Layer 4: ADR (MADR template)

Visual system design?
  → Layer 5: Diagrams (Mermaid/PlantUML/D2)
Tool Selection Matrix
NeedPrimary ToolBest For
Doc SiteDocusaurusFeature-rich React sites
Doc SiteMkDocs MaterialSimple Python docs
API Docs (Interactive)Swagger UITesting
API Docs (Read-Only)RedocProfessional design
TypeScriptTypeDocAll TS projects
PythonSphinxAll Python projects
GogodocBuilt-in
RustrustdocBuilt-in
DiagramsMermaidAll-purpose

API Documentation Quick Start

Create OpenAPI specification:

yaml
openapi: 3.1.0
info:
  title: User API
  version: 1.0.0

servers:
  - url: https://api.example.com/v1

paths:
  /users/{userId}:
    get:
      summary: Get a user
      parameters:
        - name: userId
          in: path
          required: true
          schema:
            type: string
      responses:
        '200':
          description: Success
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/User'

components:
  schemas:
    User:
      type: object
      required: [id, email, name]
      properties:
        id:
          type: string
        email:
          type: string
          format: email
        name:
          type: string

  securitySchemes:
    bearerAuth:
      type: http
      scheme: bearer
      bearerFormat: JWT

security:
  - bearerAuth: []

Render with Swagger UI, Redoc, or Scalar. See references/api-documentation.md for complete examples and templates/openapi-template.yaml for starter template.

Code Documentation Quick Start

TypeScript
typescript
/**
 * Calculate the sum of two numbers.
 *
 * @param a - The first number
 * @param b - The second number
 * @returns The sum of a and b
 *
 * @example
 * ```typescript
 * const result = add(2, 3);
 * console.log(result); // 5
 * ```
 */
export function add(a: number, b: number): number {
  return a + b;
}

Generate docs:

bash
npm install -D typedoc
npx typedoc --entryPoints src/index.ts --out docs
Python
python
def calculate_total(items: list[dict], tax_rate: float = 0.0) -> float:
    """Calculate the total price including tax.

    Args:
        items: List of items with 'price' and 'quantity' keys.
        tax_rate: Tax rate as decimal (e.g., 0.1 for 10%).

    Returns:
        Total price including tax.

    Example:
        >>> items = [{'price': 10, 'quantity': 2}]
        >>> calculate_total(items, tax_rate=0.1)
        22.0
    """
    subtotal = sum(item['price'] * item['quantity'] for item in items)
    return subtotal * (1 + tax_rate)

Generate docs:

bash
pip install sphinx sphinx-rtd-theme
sphinx-quickstart docs
cd docs && make html

See references/code-documentation.md for Go and Rust examples.

Documentation Site Quick Start

Docusaurus
bash
npx create-docusaurus@latest my-website classic
cd my-website
npm start

Basic config:

javascript
// docusaurus.config.js
module.exports = {
  title: 'My Project',
  url: 'https://docs.example.com',
  themeConfig: {
    navbar: {
      items: [
        {type: 'doc', docId: 'intro', label: 'Docs'},
      ],
    },
  },
  presets: [
    ['@docusaurus/preset-classic', {
      docs: {
        sidebarPath: require.resolve('./sidebars.js'),
      },
    }],
  ],
};
MkDocs
bash
pip install mkdocs mkdocs-material
mkdocs new my-project
mkdocs serve

Basic config:

yaml
# mkdocs.yml
site_name: My Project
theme:
  name: material
  features:
    - navigation.tabs
    - search.suggest

plugins:
  - search

nav:
  - Home: index.md
  - Getting Started: getting-started.md

See references/documentation-sites.md for versioning and deployment.

Architecture Decision Records

Use MADR template for recording decisions:

markdown
# Use PostgreSQL for Primary Database

* Status: accepted
* Deciders: Engineering Team, CTO
* Date: 2025-01-15

## Context and Problem Statement

Application requires relational database with complex queries,
ACID transactions, JSON support, and full-text search.

## Decision Drivers

* Data integrity (ACID compliance)
* Performance (10K+ queries/second)
* Cost (open-source preferred)
* Features (JSONB, full-text search)

## Considered Options

* PostgreSQL
* MySQL
* Amazon Aurora

## Decision Outcome

Chosen "PostgreSQL" for best balance of features and cost.

### Positive Consequences

* Open-source with no licensing costs
* Advanced features (JSONB, full-text search)
* Strong ACID compliance

### Negative Consequences

* Self-hosting requires DevOps investment
* Horizontal scaling requires changes

Copy full template from templates/adr-template.md. See references/adr-guide.md for workflow and examples/adr/0001-database-selection.md for complete example.

Diagrams Quick Start

Create diagrams with Mermaid:

markdown
```mermaid
sequenceDiagram
    User->>Frontend: Click "Login"
    Frontend->>API: POST /auth/login
    API->>Database: Verify credentials
    Database-->>API: User found
    API-->>Frontend: JWT token
    Frontend->>User: Redirect to dashboard
```

Mermaid renders in GitHub, Docusaurus, and MkDocs. See references/diagram-generation.md for PlantUML and D2 examples.

Common Patterns

Design-First vs Code-First APIs

Design-First:

  1. Write OpenAPI spec
  2. Review with stakeholders
  3. Generate server stubs
  4. Implement handlers

Pros: Contract before implementation, parallel development Cons: Spec authoring can be verbose

Code-First:

  1. Implement API with decorators
  2. Generate OpenAPI from code
  3. Publish documentation

Pros: Faster development, spec matches code Cons: Documentation lags behind

Recommendation: Design-first for new APIs, code-first for existing.

Embedding API Docs in Sites

Docusaurus integration:

javascript
// docusaurus.config.js
plugins: [
  ['docusaurus-plugin-openapi-docs', {
    config: {
      api: {
        specPath: 'openapi/api.yaml',
        outputDir: 'docs/api',
      },
    },
  }],
],
themes: ['docusaurus-theme-openapi-docs'],

See references/api-documentation.md for MkDocs integration.

CI/CD Automation
yaml
# .github/workflows/docs.yml
name: Documentation

on:
  push:
    branches: [main]

jobs:
  build-deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4

      - name: Generate API docs
        run: npm run docs:api

      - name: Generate code docs
        run: npm run docs:code

      - name: Build site
        run: npm run docs:build

      - name: Deploy to GitHub Pages
        uses: peaceiris/actions-gh-pages@v3
        with:
          github_token: ${{ secrets.GITHUB_TOKEN }}
          publish_dir: ./build

See references/ci-cd-integration.md for validation and versioning.

When to Write an ADR

Write ADRs for:

✅ Technology selection (database, framework, cloud) ✅ Architecture patterns (microservices, event-driven) ✅ Decisions with trade-offs (pros/cons) ✅ Team alignment needed

Don't write ADRs for:

❌ Trivial decisions (naming, formatting) ❌ Easily reversible (config tweaks) ❌ Implementation details (document in code)

See references/adr-guide.md for workflow and examples.

Show full SKILL.md (255 more words)Show less

Reference Documentation

For detailed guides:

  • references/api-documentation.md - OpenAPI, Swagger UI, Redoc, Scalar, design-first vs code-first
  • references/code-documentation.md - TypeDoc, Sphinx, godoc, rustdoc with examples
  • references/documentation-sites.md - Docusaurus and MkDocs setup, versioning, deployment
  • references/adr-guide.md - MADR template, workflow, when to write ADRs
  • references/diagram-generation.md - Mermaid, PlantUML, D2 syntax and integration
  • references/ci-cd-integration.md - Automation, validation, deployment strategies

Templates

  • templates/adr-template.md - MADR template for Architecture Decision Records
  • templates/openapi-template.yaml - OpenAPI 3.1 specification starter

Examples

  • examples/openapi/ - Complete OpenAPI specifications
  • examples/typescript/ - TypeDoc configuration and TSDoc examples
  • examples/python/ - Sphinx configuration and docstring examples
  • examples/adr/ - Real-world Architecture Decision Records
  • examples/diagrams/ - Mermaid, PlantUML, D2 examples

Tool Recommendations

Based on research (December 2025):

Documentation Sites:

  • Docusaurus - React-based, feature-rich (versioning, i18n, search)
  • MkDocs Material - Python-based, simple, beautiful

API Documentation:

  • Swagger UI - Interactive testing
  • Redoc - Beautiful read-only
  • Scalar - Modern 2025 design

Code Documentation:

  • TypeScript: TypeDoc
  • Python: Sphinx
  • Go: godoc (built-in)
  • Rust: rustdoc (built-in)

Diagrams:

  • Mermaid - Most popular, GitHub-integrated
  • PlantUML - UML standard
  • D2 - Modern, declarative

Integration with Other Skills

  • api-patterns - API implementation and documentation
  • building-ci-pipelines - Automate documentation generation
  • testing-strategies - Document test patterns
  • sdk-design - Generate SDK documentation

Best Practices

  1. Docs-as-Code - Keep docs in version control
  2. Single Source of Truth - Generate from code/specs
  3. Automation - Generate in CI/CD pipelines
  4. Examples - Include working code examples
  5. Validation - Lint Markdown, validate specs
  6. Versioning - Version docs with releases
  7. Consistency - Use consistent terminology
  8. Maintenance - Update when code changes

Common Pitfalls

Documentation Drift - Docs become outdated → Automate generation, validate in CI/CD

Over-Documentation - Documenting obvious behavior → Focus on "why" not "what"

Fragmented Docs - Information scattered → Single site with clear navigation

No Examples - Theory without practice → Include runnable examples

© ancoleman, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (references) in skills/generating-documentation of ancoleman/ai-design-components.

  • SKILL.md
  • examples/adr/0001-database-selection.md
  • examples/typescript/tsdoc-examples.ts
  • outputs.yaml
  • references/adr-guide.md
  • references/api-documentation.md
  • references/ci-cd-integration.md
  • references/code-documentation.md
  • references/diagram-generation.md
  • references/documentation-sites.md
  • templates/adr-template.md
  • templates/openapi-template.yaml

Open the folder on GitHubat commit 76551b7

Compare with similar skills

Generating Documentation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Generating Documentation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Generating Documentation this skillancoleman/ai-design-components526—~3kAutomated safety check: PassMIT
Code Documenterzebbern/claude-code-guide4.6k—~1kAutomated safety check: PassMIT
Code DocumenterJeffallan/claude-skills12k—~1.5kAutomated safety check: PassMIT
Draw.io Diagram StudioAgents365-ai/drawio-skill10k—~2.4kAutomated safety check: NotesMIT
Design Doc MermaidSpillwaveSolutions/design-doc-mermaid1751 repos~5.6kAutomated safety check: PassNone
Generate Readmedivar-ir/ai-doc-gen765—~996Automated safety check: PassMIT

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Categories

Questions about Generating Documentation

What does Generating Documentation do?

Generate comprehensive technical documentation including API docs (OpenAPI/Swagger), code documentation (TypeDoc/Sphinx), documentation sites (Docusaurus/MkDocs), Architecture Decision Records…. Generating Documentation is an agent skill from ancoleman/ai-design-components. Generate comprehensive technical documentation including API docs (OpenAPI/Swagger), code documentation (TypeDoc/Sphinx), documentation sites (Docusaurus/MkDocs), Architecture Decision Records (ADRs), and diagrams (Mermaid/PlantUML).

When should I use Generating Documentation?

Generating Documentation fits situations like: documenting APIs; systems architecture; building developer-facing documentation sites.

How do I install Generating Documentation in Claude Code?

Run `npx skills add ancoleman/ai-design-components --skill generating-documentation -a claude-code`. Or copy the skill folder (skills/generating-documentation in ancoleman/ai-design-components) into .claude/skills/generating-documentation in your project. Claude Code loads it when a task matches its description.

How do I install Generating Documentation in Codex?

Run `npx skills add ancoleman/ai-design-components --skill generating-documentation -a codex`. Or copy the skill folder (skills/generating-documentation in ancoleman/ai-design-components) into .agents/skills/generating-documentation in your project. Codex loads it when a task matches its description.

Can I use Generating Documentation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ancoleman/ai-design-components --skill generating-documentation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/generating-documentation, .gemini/skills/generating-documentation, .github/skills/generating-documentation and .opencode/skills/generating-documentation in your project.

What does Generating Documentation need to run?

Going by SKILL.md and its folder, Generating Documentation needs TypeScript for the scripts in its folder, the command-line tools its instructions call (npm, npx, pip and make) and credentials named GITHUB_TOKEN. Our summary lists: Python 3; Node.js; A credential in GITHUB_TOKEN.

Does Generating Documentation access the network?

SKILL.md contains no URLs. Its commands use npm, npx and pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Generating Documentation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Generating Documentation use?

Generating Documentation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Generating Documentation use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 18k tokens, read only when the agent opens those files.

What are the alternatives to Generating Documentation?

Skills that share tags, products or a category with Generating Documentation: Code Documenter (zebbern/claude-code-guide, 4.6k stars), Code Documenter (Jeffallan/claude-skills, 12k stars), Draw.io Diagram Studio (Agents365-ai/drawio-skill, 10k stars) and Design Doc Mermaid (SpillwaveSolutions/design-doc-mermaid, 175 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Generating Documentation?

ancoleman (a GitHub user) maintains it in ancoleman/ai-design-components, which has 526 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on December 11, 2025.

Source: ancoleman/ai-design-components on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.