Code Documentation Skill

Overview

This skill generates professional, comprehensive documentation for software projects, codebases, libraries, and APIs. It follows industry best practices from projects like React, Django, Stripe, and Kubernetes to produce documentation that is accurate, well-structured, and useful for both new contributors and experienced developers.

The output ranges from single-file READMEs to multi-document developer guides, always matched to the project's complexity and the user's needs.

Core Capabilities

• Generate comprehensive README.md files with badges, installation, usage, and API reference
• Create API reference documentation from source code analysis
• Produce architecture and design documentation with diagrams
• Write developer onboarding and contribution guides
• Generate changelogs from commit history or release notes
• Create inline code documentation following language-specific conventions
• Support JSDoc, docstrings, GoDoc, Javadoc, and Rustdoc formats
• Adapt documentation style to the project's language and ecosystem

When to Use This Skill

Always load this skill when:

• User asks to "document", "create docs", or "write documentation" for any code
• User requests a README, API reference, or developer guide
• User shares a codebase or repository and wants documentation generated
• User asks to improve or update existing documentation
• User needs architecture documentation, including diagrams
• User requests a changelog or migration guide

Documentation Workflow

Phase 1: Codebase Analysis

Before writing any documentation, thoroughly understand the codebase.

Step 1.1: Project Discovery

Identify the project fundamentals:

package.json

pyproject.toml

go.mod

Cargo.toml

Makefile

CMakeLists.txt

webpack.config.js

build.gradle

Step 1.2: Code Structure Analysis

Use sandbox tools to explore the codebase:

# Get directory structure
ls /mnt/user-data/uploads/project-dir/

# Read key files
read_file /mnt/user-data/uploads/project-dir/package.json
read_file /mnt/user-data/uploads/project-dir/pyproject.toml

# Search for public API surfaces
grep -r "export " /mnt/user-data/uploads/project-dir/src/
grep -r "def " /mnt/user-data/uploads/project-dir/src/ --include="*.py"
grep -r "func " /mnt/user-data/uploads/project-dir/ --include="*.go"

Step 1.3: Identify Documentation Scope

Based on analysis, determine what documentation to produce:

Phase 2: Documentation Generation

Step 2.1: README Generation

Every project needs a README. Follow this structure:

# Project Name

[One-line project description — what it does and why it matters]

[![Badge](link)](#) [![Badge](link)](#)

## Features

- [Key feature 1 — brief description]
- [Key feature 2 — brief description]
- [Key feature 3 — brief description]

## Quick Start

### Prerequisites

- [Prerequisite 1 with version requirement]
- [Prerequisite 2 with version requirement]

### Installation

[Installation commands with copy-paste-ready code blocks]

### Basic Usage

[Minimal working example that demonstrates core functionality]

## Documentation

- [Link to full API reference if separate]
- [Link to architecture docs if separate]
- [Link to examples directory if applicable]

## API Reference

[Inline API reference for smaller projects OR link to generated docs]

## Configuration

[Environment variables, config files, or runtime options]

## Examples

[2-3 practical examples covering common use cases]

## Development

### Setup

[How to set up a development environment]

### Testing

[How to run tests]

### Building

[How to build the project]

## Contributing

[Contribution guidelines or link to CONTRIBUTING.md]

## License

[License information]

Step 2.2: API Reference Generation

For each public API surface, document:

Function / Method Documentation:

### `functionName(param1, param2, options?)`

Brief description of what this function does.

**Parameters:**

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `param1` | `string` | Yes | — | Description of param1 |
| `param2` | `number` | Yes | — | Description of param2 |
| `options` | `Object` | No | `{}` | Configuration options |
| `options.timeout` | `number` | No | `5000` | Timeout in milliseconds |

**Returns:** `Promise<Result>` — Description of return value

**Throws:**
- `ValidationError` — When param1 is empty
- `TimeoutError` — When the operation exceeds the timeout

**Example:**

\`\`\`javascript
const result = await functionName("hello", 42, { timeout: 10000 });
console.log(result.data);
\`\`\`

Class Documentation:

### `ClassName`

Brief description of the class and its purpose.

**Constructor:**

\`\`\`javascript
new ClassName(config)
\`\`\`

| Parameter | Type | Description |
|-----------|------|-------------|
| `config.option1` | `string` | Description |
| `config.option2` | `boolean` | Description |

**Methods:**

- [`method1()`](#method1) — Brief description
- [`method2(param)`](#method2) — Brief description

**Properties:**

| Property | Type | Description |
|----------|------|-------------|
| `property1` | `string` | Description |
| `property2` | `number` | Read-only. Description |

Step 2.3: Architecture Documentation

For medium-to-large projects, include architecture documentation:

# Architecture Overview

## System Diagram

[Include a Mermaid diagram showing the high-level architecture]

\`\`\`mermaid
graph TD
    A[Client] --> B[API Gateway]
    B --> C[Service A]
    B --> D[Service B]
    C --> E[(Database)]
    D --> E
\`\`\`

## Component Overview

### Component Name
- **Purpose**: What this component does
- **Location**: `src/components/name/`
- **Dependencies**: What it depends on
- **Public API**: Key exports or interfaces

## Data Flow

[Describe how data flows through the system for key operations]

## Design Decisions

### Decision Title
- **Context**: What situation led to this decision
- **Decision**: What was decided
- **Rationale**: Why this approach was chosen
- **Trade-offs**: What was sacrificed

Step 2.4: Inline Code Documentation

Generate language-appropriate inline documentation:

Python (Docstrings — Google style):

def process_data(input_path: str, options: dict | None = None) -> ProcessResult:
    """Process data from the given file path.

    Reads the input file, applies transformations based on the provided
    options, and returns a structured result object.

    Args:
        input_path: Absolute path to the input data file.
            Supports CSV, JSON, and Parquet formats.
        options: Optional configuration dictionary.
            - "validate" (bool): Enable input validation. Defaults to True.
            - "format" (str): Output format ("json" or "csv"). Defaults to "json".

    Returns:
        A ProcessResult containing the transformed data and metadata.

    Raises:
        FileNotFoundError: If input_path does not exist.
        ValidationError: If validation is enabled and data is malformed.

    Example:
        >>> result = process_data("/data/input.csv", {"validate": True})
        >>> print(result.row_count)
        1500
    """

TypeScript (JSDoc / TSDoc):

/**
 * Fetches user data from the API and transforms it for display.
 *
 * @param userId - The unique identifier of the user
 * @param options - Configuration options for the fetch operation
 * @param options.includeProfile - Whether to include the full profile. Defaults to `false`.
 * @param options.cache - Cache duration in seconds. Set to `0` to disable.
 * @returns The transformed user data ready for rendering
 * @throws {NotFoundError} When the user ID does not exist
 * @throws {NetworkError} When the API is unreachable
 *
 * @example
 * ```ts
 * const user = await fetchUser("usr_123", { includeProfile: true });
 * console.log(user.displayName);
 * ```
 */

Go (GoDoc):

// ProcessData reads the input file at the given path, applies the specified
// transformations, and returns the processed result.
//
// The input path must be an absolute path to a CSV or JSON file.
// If options is nil, default options are used.
//
// ProcessData returns an error if the file does not exist or cannot be parsed.
func ProcessData(inputPath string, options *ProcessOptions) (*Result, error) {

Phase 3: Quality Assurance

Step 3.1: Documentation Completeness Check

Verify the documentation covers:

• What it is — Clear project description that a newcomer can understand
• Why it exists — Problem it solves and value proposition
• How to install — Copy-paste-ready installation commands
• How to use — At least one minimal working example
• API surface — All public functions, classes, and types documented
• Configuration — All environment variables, config files, and options
• Error handling — Common errors and how to resolve them
• Contributing — How to set up dev environment and submit changes

Step 3.2: Quality Standards

Step 3.3: Cross-reference Validation

Ensure:

• All mentioned file paths exist in the project
• All referenced functions and classes exist in the code
• All code examples use the correct function signatures
• Version numbers match the project's actual version
• All links (internal and external) are valid

Documentation Style Guide

Writing Principles

1. Lead with the "why" — Before explaining how something works, explain why it exists
2. Progressive disclosure — Start simple, add complexity gradually
3. Show, don't tell — Prefer code examples over lengthy explanations
4. Active voice — "The function returns X" not "X is returned by the function"
5. Present tense — "The server starts on port 8080" not "The server will start on port 8080"
6. Second person — "You can configure..." not "Users can configure..."

Formatting Rules

• Use ATX-style headers (

  #

  ,

  ##

  ,

  ###

  )
• Use fenced code blocks with language specification (

  ```python

  ,

  ```bash

  )
• Use tables for structured information (parameters, options, configuration)
• Use admonitions for important notes, warnings, and tips
• Keep line length readable (wrap prose at ~80-100 characters in source)
• Use

  code formatting

  for function names, file paths, variable names, and CLI commands

Language-Specific Conventions

///

Output Handling

After generation:

• Save documentation files to

  /mnt/user-data/outputs/

• For multi-file documentation, maintain the project directory structure
• Present generated files to the user using the

  present_files

  tool
• Offer to iterate on specific sections or adjust the level of detail
• Suggest additional documentation that might be valuable

Notes

• Always analyze the actual code before writing documentation — never guess at API signatures or behavior
• When existing documentation exists, preserve its structure unless the user explicitly asks for a rewrite
• For large codebases, prioritize documenting the public API surface and key abstractions first
• Documentation should be written in the same language as the project's existing docs; default to English if none exist
• When generating changelogs, use the Keep a Changelog format
• This skill works well in combination with the

  deep-research

  skill for documenting third-party integrations or dependencies