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Project Docs

CommunityPopular
jezweb
project-docs

Generate project documentation from codebase analysis — ARCHITECTURE.md, API_ENDPOINTS.md, DATABASE_SCHEMA.md. Reads source code, schema files, routes, and config to produce accurate, structured docs. Use when starting a project, onboarding contributors, or when docs are missing or stale. Triggers: 'generate docs', 'document architecture', 'create api docs', 'document schema', 'project documentation', 'write architecture doc'.

Overview

Publisherjezweb
Repositoryclaude-skills
Skill nameproject-docs
Stars
1K
Forks
102
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jezweb on GitHub. Read the source before you install it.

Installation

Install the Project Docs AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jezweb/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/plugins/dev-tools/skills/project-docs .claude/skills/project-docs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Project Docs in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Project Docs on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Project Docs is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Project Documentation Generator

Generate structured project documentation by analysing the codebase. Produces docs that reflect the actual code, not aspirational architecture.

When to Use

  • New project needs initial documentation
  • Docs are missing or stale
  • Onboarding someone to the codebase
  • Post-refactor doc refresh

Workflow

1. Detect Project Type

Scan the project root to determine what kind of project this is:

IndicatorProject Type
wrangler.jsonc / wrangler.tomlCloudflare Worker
vite.config.ts + src/App.tsxReact SPA
astro.config.mjsAstro site
next.config.jsNext.js app
package.json with honoHono API
src/index.ts with HonoAPI server
drizzle.config.tsHas database layer
schema.ts or schema/Has database schema
pyproject.toml / setup.pyPython project
Cargo.tomlRust project

2. Ask What to Generate

Which docs should I generate?
1. ARCHITECTURE.md — system overview, stack, directory structure, key flows
2. API_ENDPOINTS.md — routes, methods, params, response shapes, auth
3. DATABASE_SCHEMA.md — tables, relationships, migrations, indexes
4. All of the above

Only offer docs that match the project. Don't offer API_ENDPOINTS.md for a static site. Don't offer DATABASE_SCHEMA.md if there's no database.

3. Scan the Codebase

For each requested doc, read the relevant source files:

ARCHITECTURE.md — scan:

  • package.json / pyproject.toml (stack, dependencies)
  • Entry points (src/index.ts, src/main.tsx, src/App.tsx)
  • Config files (wrangler.jsonc, vite.config.ts, tsconfig.json)
  • Directory structure (top 2 levels)
  • Key modules and their exports

API_ENDPOINTS.md — scan:

  • Route files (src/routes/, src/api/, or inline in index)
  • Middleware files (auth, CORS, logging)
  • Request/response types or Zod schemas
  • Error handling patterns

DATABASE_SCHEMA.md — scan:

  • Drizzle schema files (src/db/schema.ts, src/schema/)
  • Migration files (drizzle/, migrations/)
  • Raw SQL files if present
  • Seed files if present

4. Generate Documentation

Write each doc to docs/ (create the directory if it doesn't exist). If the project already has docs there, offer to update rather than overwrite.

For small projects with no docs/ directory, write to the project root instead.

Document Templates

ARCHITECTURE.md

markdown
# Architecture

## Overview
[One paragraph: what this project does and how it's structured]

## Stack
| Layer | Technology | Version |
|-------|-----------|---------|
| Runtime | [e.g. Cloudflare Workers] ||
| Framework | [e.g. Hono] | [version] |
| Database | [e.g. D1 (SQLite)] ||
| ORM | [e.g. Drizzle] | [version] |
| Frontend | [e.g. React 19] | [version] |
| Styling | [e.g. Tailwind v4] | [version] |

## Directory Structure
[Annotated tree — top 2 levels with purpose comments]

## Key Flows
### [Flow 1: e.g. "User Authentication"]
[Step-by-step: request → middleware → handler → database → response]

### [Flow 2: e.g. "Data Processing Pipeline"]
[Step-by-step through the system]

## Configuration
[Key config files and what they control]

## Deployment
[How to deploy, environment variables needed, build commands]

API_ENDPOINTS.md

markdown
# API Endpoints

## Base URL
[e.g. `https://api.example.com` or relative `/api`]

## Authentication
[Method: Bearer token, session cookie, API key, none]
[Where tokens come from, how to obtain]

## Endpoints

### [Group: e.g. Users]

#### `GET /api/users`
- **Auth**: Required
- **Params**: `?page=1&limit=20`
- **Response**: `{ users: User[], total: number }`

#### `POST /api/users`
- **Auth**: Required (admin)
- **Body**: `{ name: string, email: string }`
- **Response**: `{ user: User }` (201)
- **Errors**: 400 (validation), 409 (duplicate email)

[Repeat for each endpoint]

## Error Format
[Standard error response shape]

## Rate Limits
[If applicable]

DATABASE_SCHEMA.md

markdown
# Database Schema

## Engine
[e.g. Cloudflare D1 (SQLite), PostgreSQL, MySQL]

## Tables

### `users`
| Column | Type | Constraints | Description |
|--------|------|-------------|-------------|
| id | TEXT | PK | UUID |
| email | TEXT | UNIQUE, NOT NULL | User email |
| name | TEXT | NOT NULL | Display name |
| created_at | TEXT | NOT NULL, DEFAULT now | ISO timestamp |

### `posts`
[Same format]

## Relationships
[Foreign keys, join patterns, cascading rules]

## Indexes
[Non-primary indexes and why they exist]

## Migrations
- Generate: `npx drizzle-kit generate`
- Apply local: `npx wrangler d1 migrations apply DB --local`
- Apply remote: `npx wrangler d1 migrations apply DB --remote`

## Seed Data
[Reference to seed script if one exists]

Quality Rules

  1. Document what exists, not what's planned — read the actual code, don't invent endpoints or tables
  2. Include versions — extract from package.json/lock files, not from memory
  3. Show real response shapes — copy from TypeScript types or Zod schemas in the code
  4. Keep it scannable — tables over paragraphs, code blocks over prose
  5. Don't duplicate CLAUDE.md — if architecture info is already in CLAUDE.md, either move it to ARCHITECTURE.md or reference it
  6. Flag gaps — if you find undocumented routes or tables without clear purpose, note them with <!-- TODO: document purpose -->

Updating Existing Docs

If docs already exist:

  1. Read the existing doc
  2. Diff against the current codebase
  3. Show the user what's changed (new endpoints, removed tables, updated stack)
  4. Apply updates preserving any hand-written notes or sections

Never silently overwrite custom content the user has added to their docs.

Frequently asked questions

What does the Project Docs AI skill do?

Generate project documentation from codebase analysis — ARCHITECTURE.md, API_ENDPOINTS.md, DATABASE_SCHEMA.md. Reads source code, schema files, routes, and config to produce accurate, structured docs. Use when starting a project, onboarding contributors, or when docs are missing or stale. Triggers: 'generate docs', 'document architecture', 'create api docs', 'document schema', 'project documentation', 'write architecture doc'.

Why use Project Docs on TypingMind?

Because you install it once and use it with any model. Project Docs is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Project Docs in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jezweb/claude-skills/tree/main/plugins/dev-tools/skills/project-docs. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Project Docs?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Project Docs?

As many as you like. As long as a model supports skills, you can use Project Docs with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Project Docs AI skill free?

Yes. It is published on GitHub by jezweb under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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