Code Documentation

What it does

When your agent uses it

• The user requests code documentation
• Provides relevant inputs for this workflow

Example prompts

• “/code-documentation”

Requirements

• Python 3
• Node.js
• Docker

Workflow steps

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

1. 1Inventory the Codebase: Walk the project tree and catalog public modules, classes, functions, constants, and type definitions. Note which…
2. 2Determine Documentation Scope: Based on the user's request, decide whether to generate inline docstrings, a standalone API reference, a…
3. 3Analyze Signatures and Behavior: For each symbol, inspect parameter types, return types, default values, raised exceptions, and side…
4. 4Generate Documentation: Write documentation that includes a one-line summary, an extended description when the logic is non-trivial…
5. 5Insert or Update In-Place: For inline documentation (docstrings, JSDoc comments), insert the generated text directly above or inside the…
6. 6Validate and Cross-Reference: Verify that documented parameter names match the actual signature, that referenced types exist, and that…

What it can do on your machine

Read from SKILL.md and the folder at commit 75865a5. 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

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python and markdown).

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

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • github.com

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

• Credentials

  Names no API keys, tokens, secrets or passwords.

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

Context cost

Code Documentation loads about 2.3k tokens when it runs. Until then it costs about 66 tokens; SKILL.md has 706 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~66

When it runs · the whole SKILL.md, loaded when a task matches

~2.3k

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

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 seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 706 words, ~2,300 tokens.

name

code-documentation

description

Automatically generate clear, comprehensive documentation for codebases — including API references, inline docstrings, README files, and usage guides. Use when the user requests code documentation or provides relevant inputs for this workflow.

license

MIT

metadata.author

awesome-ai-agent-skills contributors

metadata.version

1.1.0

class TokenBucket:
    def __init__(self, capacity, refill_rate):
        self.capacity = capacity
        self.tokens = capacity
        self.refill_rate = refill_rate
        self._last_refill = time.monotonic()

    def consume(self, tokens=1):
        self._refill()
        if self.tokens >= tokens:
            self.tokens -= tokens
            return True
        return False

    def _refill(self):
        now = time.monotonic()
        elapsed = now - self._last_refill
        self.tokens = min(self.capacity, self.tokens + elapsed * self.refill_rate)
        self._last_refill = now

class TokenBucket:
    """A token-bucket rate limiter.

    Tokens are added at a constant rate and consumed by callers.
    When the bucket is empty, requests are rejected until enough
    tokens have accumulated.

    Args:
        capacity: Maximum number of tokens the bucket can hold.
        refill_rate: Tokens added per second.

    Example:
        >>> bucket = TokenBucket(capacity=10, refill_rate=2)
        >>> bucket.consume(3)
        True
    """

    def __init__(self, capacity: float, refill_rate: float) -> None:
        self.capacity = capacity
        self.tokens = capacity
        self.refill_rate = refill_rate
        self._last_refill = time.monotonic()

    def consume(self, tokens: int = 1) -> bool:
        """Attempt to consume tokens from the bucket.

        Triggers an internal refill based on elapsed time before
        checking availability.

        Args:
            tokens: Number of tokens to consume. Defaults to 1.

        Returns:
            True if the tokens were available and consumed, False otherwise.
        """
        self._refill()
        if self.tokens >= tokens:
            self.tokens -= tokens
            return True
        return False

    def _refill(self) -> None:
        """Refill the bucket based on time elapsed since the last refill."""
        now = time.monotonic()
        elapsed = now - self._last_refill
        self.tokens = min(self.capacity, self.tokens + elapsed * self.refill_rate)
        self._last_refill = now

myapi/
├── package.json          (name: @acme/myapi, scripts: dev, build, test)
├── src/
│   ├── index.ts          (Express app entry point)
│   ├── routes/
│   │   ├── users.ts
│   │   └── orders.ts
│   └── middleware/
│       └── auth.ts
├── tests/
│   └── users.test.ts
└── Dockerfile

# @acme/myapi

A RESTful API service built with Express and TypeScript.

## Getting Started

### Prerequisites

- Node.js >= 18
- npm or yarn

### Installation

git clone https://github.com/acme/myapi.git
cd myapi
npm install

### Running Locally

npm run dev        # Start the dev server with hot reload
npm run build      # Compile TypeScript to dist/
npm test           # Run the test suite

### Docker

docker build -t myapi .
docker run -p 3000:3000 myapi

## API Endpoints

| Method | Path           | Description           |
|--------|----------------|-----------------------|
| GET    | /users         | List all users        |
| POST   | /users         | Create a new user     |
| GET    | /orders        | List all orders       |
| POST   | /orders        | Create a new order    |

## Project Structure

- `src/index.ts` — Application entry point and server bootstrap.
- `src/routes/` — Route handlers grouped by resource.
- `src/middleware/auth.ts` — JWT authentication middleware.
- `tests/` — Jest test files.

## License

MIT

© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in code-and-development/code-documentation of seb1n/awesome-ai-agent-skills.

Open the folder on GitHubat commit 75865a5