How to Automate Documentation With OpenClaw Skills

Documentation falls behind because writing it is tedious and maintaining it is worse. The code changes, the docs stay the same, and eventually nobody trusts them. OpenClaw skills can automate the parts of documentation that are mechanical — extracting API signatures, generating README sections, writing changelogs from commit history, and adding JSDoc annotations — so you can focus on the explanatory writing that requires a human.

This guide covers how to set up OpenClaw skills for documentation automation across your entire project. We will walk through API docs, README generation, changelog automation, inline code documentation, and architecture docs.

Installing Documentation Skills

The OpenClaw Bazaar skills directory has several documentation skills. Install the ones that match your workflow:

# Core documentation skills
openclaw skill install doc-generator
openclaw skill install api-doc-writer
openclaw skill install changelog-automation

# Inline documentation
openclaw skill install jsdoc-annotator       # JavaScript/TypeScript
openclaw skill install tsdoc-annotator       # TypeScript-specific TSDoc
openclaw skill install python-docstrings     # Python

# Architecture documentation
openclaw skill install architecture-doc-writer

Each skill handles a different layer of documentation. Together, they give your agent the ability to generate and maintain docs across your entire codebase.

API Documentation

API documentation is the highest-value target for automation. Endpoints, request parameters, response shapes, and error codes can all be extracted from code. The api-doc-writer skill teaches your agent to generate API docs from your route handlers.

Point the agent at your API routes:

# Ask your agent:
# "Generate API documentation for all routes in src/routes/"

The agent analyzes your route handlers and produces structured documentation:

// Given this route handler:
// src/routes/users.ts

import { z } from "zod";

const CreateUserSchema = z.object({
  email: z.string().email(),
  name: z.string().min(1).max(100),
  role: z.enum(["admin", "member", "viewer"]).default("member"),
});

app.post("/api/v1/users", validate(CreateUserSchema), async (req, res) => {
  const user = await userService.create(req.body);
  res.status(201).json({ data: user });
});

app.get("/api/v1/users/:id", async (req, res) => {
  const user = await userService.findById(req.params.id);
  if (!user) return res.status(404).json({ error: "User not found" });
  res.json({ data: user });
});

The agent generates:

## POST /api/v1/users

Create a new user account.

### Request Body

| Field   | Type     | Required | Default    | Description           |
|---------|----------|----------|------------|-----------------------|
| email   | string   | Yes      | —          | Valid email address   |
| name    | string   | Yes      | —          | 1-100 characters      |
| role    | enum     | No       | "member"   | admin, member, viewer |

### Response

**201 Created**

\`\`\`json
{
  "data": {
    "id": "uuid",
    "email": "user@example.com",
    "name": "Jane Doe",
    "role": "member",
    "createdAt": "2026-03-29T00:00:00Z"
  }
}
\`\`\`

### Errors

| Status | Description       |
|--------|-------------------|
| 400    | Validation failed |
| 409    | Email already exists |

---

## GET /api/v1/users/:id

Retrieve a user by ID.

### Parameters

| Parameter | Type   | Location | Description |
|-----------|--------|----------|-------------|
| id        | string | path     | User UUID   |

### Response

**200 OK**

\`\`\`json
{
  "data": {
    "id": "uuid",
    "email": "user@example.com",
    "name": "Jane Doe",
    "role": "member",
    "createdAt": "2026-03-29T00:00:00Z"
  }
}
\`\`\`

### Errors

| Status | Description    |
|--------|----------------|
| 404    | User not found |

The agent extracts validation rules from your Zod schemas, infers response shapes from your service layer, and documents error cases from your error handling code. If you use OpenAPI or Swagger, the api-doc-writer skill can also generate or update your OpenAPI spec file.

README Generation

READMEs need specific sections that stay in sync with the actual project. The doc-generator skill automates this by reading your project configuration and generating accurate README content:

# Ask your agent:
# "Update the README with current installation steps and available scripts"

The agent reads your package.json, scans your project structure, and generates:

## Getting Started

### Prerequisites

- Node.js >= 20.0.0
- PostgreSQL >= 15
- Redis >= 7

### Installation

\`\`\`bash
git clone https://github.com/your-org/your-project.git
cd your-project
npm install
cp .env.example .env
\`\`\`

### Environment Variables

| Variable          | Required | Description               |
|-------------------|----------|---------------------------|
| DATABASE_URL      | Yes      | PostgreSQL connection URL  |
| REDIS_URL         | Yes      | Redis connection URL       |
| JWT_SECRET        | Yes      | Secret for JWT signing     |
| PORT              | No       | Server port (default 3000) |

### Available Scripts

| Script           | Description                          |
|------------------|--------------------------------------|
| \`npm run dev\`   | Start development server with watch  |
| \`npm run build\` | Build for production                 |
| \`npm run test\`  | Run test suite                       |
| \`npm run lint\`  | Run ESLint                           |
| \`npm run migrate\`| Run database migrations             |

The agent pulls prerequisites from your engines field and Docker configs, environment variables from your .env.example file, and scripts directly from package.json. When these sources change, running the agent again produces updated documentation.

Changelog Automation

Maintaining a changelog manually is tedious. The changelog-automation skill generates changelogs from your Git history, grouping commits by type and formatting them for readability:

# Ask your agent:
# "Generate a changelog for version 2.4.0 from the last release tag"

The agent reads your Git log since the last release tag and produces:

## [2.4.0] - 2026-03-29

### Added
- User notification system with email and in-app delivery (#342)
- Bulk export endpoint for analytics data (#338)
- Rate limiting middleware with Redis backend (#335)

### Changed
- Upgraded PostgreSQL driver to v8.11 for connection pool improvements (#340)
- Refactored authentication middleware to support API key auth (#337)

### Fixed
- Fixed race condition in concurrent webhook processing (#341)
- Fixed incorrect pagination count on filtered queries (#339)
- Fixed memory leak in WebSocket connection handler (#336)

### Security
- Patched dependency vulnerability in xml-parser (CVE-2026-1234) (#343)

The skill uses conventional commit prefixes (feat:, fix:, chore:, etc.) to categorize entries. If your team does not use conventional commits, the agent falls back to analyzing commit messages and PR titles to infer categories.

Configure changelog generation in your project:

# .openclaw/changelog-config.yaml
changelog:
  format: keep-a-changelog
  group_by:
    - Added: ["feat"]
    - Changed: ["refactor", "perf", "chore"]
    - Fixed: ["fix"]
    - Security: ["security"]
  include_pr_links: true
  include_authors: false
  output: CHANGELOG.md

JSDoc and TSDoc Annotations

Inline documentation is the documentation developers actually read — it shows up in their editor as they code. The jsdoc-annotator and tsdoc-annotator skills teach your agent to add comprehensive inline documentation:

# Ask your agent:
# "Add JSDoc annotations to all exported functions in src/services/"

The agent scans your service files and adds annotations:

/**
 * Creates a new user account and sends a verification email.
 *
 * Validates the input data, checks for duplicate email addresses,
 * hashes the password, persists the user record, and triggers
 * an asynchronous verification email via the notification service.
 *
 * @param data - The user creation payload
 * @param data.email - Must be a valid, unique email address
 * @param data.name - Display name, 1-100 characters
 * @param data.role - User role, defaults to "member"
 * @returns The created user object without the password hash
 * @throws {ConflictError} If a user with the same email already exists
 * @throws {ValidationError} If the input data fails schema validation
 *
 * @example
 * \`\`\`typescript
 * const user = await userService.create({
 *   email: "jane@example.com",
 *   name: "Jane Doe",
 *   role: "admin",
 * });
 * \`\`\`
 */
export async function create(data: CreateUserInput): Promise<User> {
  // implementation
}

The agent reads the function implementation to understand what it does, checks the types for parameter and return documentation, identifies thrown errors from the code paths, and generates a usage example. The tsdoc-annotator skill adds TypeScript-specific tags like @typeParam, @sealed, and @virtual where appropriate.

Architecture Documentation

Architecture docs are the hardest to automate because they require understanding intent, not just structure. The architecture-doc-writer skill takes a hybrid approach: it generates the structural parts automatically and prompts you to fill in the decision-making parts.

# Ask your agent:
# "Generate an architecture document for this project"

The agent scans your project and produces a document with auto-generated sections and placeholders:

# Architecture Overview

## System Components

| Component        | Location            | Purpose                          |
|------------------|---------------------|----------------------------------|
| API Server       | src/server.ts       | Express.js HTTP server           |
| Route Handlers   | src/routes/         | Request handling and validation  |
| Services         | src/services/       | Business logic layer             |
| Repositories     | src/repositories/   | Database access layer            |
| Middleware        | src/middleware/      | Auth, logging, rate limiting     |
| Workers          | src/workers/        | Background job processing        |

## Data Flow

\`\`\`
Request → Middleware → Route Handler → Service → Repository → Database
                                         ↓
                                    Worker Queue → Background Jobs
\`\`\`

## Key Design Decisions

<!-- TODO: Document why these decisions were made -->

1. **Repository Pattern** — All database access goes through repository classes.
   *Why:* [Add your reasoning]

2. **Worker Queue** — Background jobs use BullMQ with Redis.
   *Why:* [Add your reasoning]

The structural sections — component listing, data flow diagrams, dependency maps — are generated automatically from code analysis. The decision sections are templated with TODO placeholders so you can add the "why" that only a human knows.

Documentation does not have to be a chore that falls behind with every sprint. With the right OpenClaw skills, the mechanical parts get automated while you focus on the parts that need human explanation. Browse documentation skills in the OpenClaw Bazaar skills directory to find the right set for your project.

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