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code-documenter

Generate docstrings, OpenAPI specs, and documentation sites for Python and TypeScript.

14|Updated Feb 5, 2026
Search Tags:#documentation#python#openapi#docstrings#coverage#api-docs#docs-site
One-click install
npx skills add https://github.com/alexander-danilenko/ai-skills --skill code-documenter-alexander-danilenko
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-documenter
Source: https://github.com/alexander-danilenko/ai-skills/tree/main/skills/code-documenter
Command: npx skills add https://github.com/alexander-danilenko/ai-skills --skill code-documenter-alexander-danilenko

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill removes the friction of inconsistent, incomplete, or missing developer-facing documentation by generating and standardizing docstrings, API specifications, and documentation sites so engineers and technical writers can ship reliable, discoverable APIs faster.

Core Features & Use Cases

  • Docstring Generation & Standardization: Produce language-specific docstrings (Google, NumPy, Sphinx, TSDoc/JSDoc) and enforce formatting rules such as multi-line comment blocks and line-length wrapping.
  • API Specification & Portals: Generate and refine OpenAPI/Swagger specs, configure interactive portals (Swagger UI, Redoc, Stoplight), and add framework-specific decorators for FastAPI, Django (drf-spectacular), NestJS, and Express.
  • Documentation Sites & Coverage: Scaffold static doc sites (Docusaurus, MkDocs, VitePress), test and validate code examples, and produce documentation coverage reports to track improvements.
  • Use Case: Convert an undocumented Python FastAPI backend into a documented API with Google-style docstrings, an OpenAPI 3.1 spec, and a deployable Swagger UI with coverage metrics.

Quick Start

Ask the skill to add Google-style docstrings for all public Python functions and generate an OpenAPI spec for the FastAPI routes.

Frequently Asked Questions about code-documenter

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate OpenAPI specs and docstrings for a FastAPI backend?▼

To generate OpenAPI specs and docstrings for FastAPI, you can produce language-specific Google-style docstrings for public functions and output an OpenAPI 3.1 specification with deployable Swagger UI configurations. This standardizes developer-facing API documentation automatically.

What is the best way to standardize Python docstrings and track documentation coverage?▼

Standardizing Python docstrings involves enforcing formatting rules like Google, NumPy, or Sphinx styles with multi-line blocks and line-length wrapping. You can track documentation coverage by generating reports that measure docstring completeness across the codebase.

Does this approach support generating TSDoc and OpenAPI specs for NestJS and Express?▼

Yes, generating TSDoc and OpenAPI specs supports NestJS and Express by adding framework-specific decorators and refining Swagger specifications. It accommodates TypeScript and Node frameworks to ensure consistent API documentation across different backend technologies.

How do I scaffold a documentation site using MkDocs or Docusaurus from existing code?▼

Scaffolding a documentation site from existing code involves configuring static site generators like Docusaurus, MkDocs, or VitePress. The process outputs portal-ready configurations and validates code examples to ensure a deployable, interactive API portal.

Can I configure interactive API portals like Redoc or Stoplight from an existing Swagger spec?▼

Yes, you can configure interactive API portals like Redoc or Stoplight from an existing Swagger spec by generating and refining OpenAPI specifications. This allows you to set up interactive documentation portals that make APIs discoverable and testable.

Why should I enforce specific docstring styles instead of just writing standard comments?▼

Enforcing specific docstring styles like Google or Sphinx formats ensures consistent, machine-readable API documentation across Python, TypeScript, and Node frameworks. Standard comments lack the structure needed to generate OpenAPI specs and documentation coverage reports reliably.

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