project-docs
Generate comprehensive, professional project documentation structures including README, ARCHITECTURE, USER_GUIDE, DEVELOPER_GUIDE, and CONTRIBUTING files. Use when the user requests project documentation creation, asks to "document a project", needs standard documentation files, or wants to set up docs for a new repository. Adapts to Python/Go projects and OpenSource/internal contexts.
Best use case
project-docs is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate comprehensive, professional project documentation structures including README, ARCHITECTURE, USER_GUIDE, DEVELOPER_GUIDE, and CONTRIBUTING files. Use when the user requests project documentation creation, asks to "document a project", needs standard documentation files, or wants to set up docs for a new repository. Adapts to Python/Go projects and OpenSource/internal contexts.
Teams using project-docs should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/project-docs/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How project-docs Compares
| Feature / Agent | project-docs | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate comprehensive, professional project documentation structures including README, ARCHITECTURE, USER_GUIDE, DEVELOPER_GUIDE, and CONTRIBUTING files. Use when the user requests project documentation creation, asks to "document a project", needs standard documentation files, or wants to set up docs for a new repository. Adapts to Python/Go projects and OpenSource/internal contexts.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Project Documentation Generator Generate complete, professional documentation structures for software projects. Automatically adapts content and structure based on project language (Python/Go), context (OpenSource/internal), and existing files. ## Core Documentation Files Always generate these five core files: 1. **README.md** - Project overview, quick start, badges 2. **ARCHITECTURE.md** - System design, components, data flow 3. **USER_GUIDE.md** - Usage examples, configuration, troubleshooting 4. **DEVELOPER_GUIDE.md** - Development setup, testing, contribution workflow 5. **CONTRIBUTING.md** - Contribution guidelines, code standards, PR process ## Workflow ### 1. Context Detection Before generating docs, detect: - **Language**: Scan for `go.mod`, `pyproject.toml`, `requirements.txt`, `setup.py` - **Project type**: Check for `Dockerfile`, `terraform/`, `k8s/`, AI/ML indicators - **Existing docs**: Identify what already exists to avoid duplication - **License**: Detect from LICENSE file or ask user - **Context**: Determine if OpenSource or internal based on repo structure ### 2. Ask Clarifying Questions Ask user ONE question at a time to fill gaps: - "What's the primary purpose of this project in one sentence?" - "Who's the main audience? (developers, ops, end-users, all)" - "Is this OpenSource or internal? (affects badges, contact info)" - "Any company-specific tooling to mention? (Jira, Slack channels, etc.)" ### 3. Content Adaptation Read `references/templates.md` to select appropriate template variants based on detected context. **Language-specific elements:** - Python: Package managers (`uv`, `pip`, `poetry`), testing (`pytest`), linting (`ruff`, `mypy`) - Go: Build commands, testing, `golangci-lint`, module structure **Context-specific elements:** - OpenSource: Badges, CODE_OF_CONDUCT, security policy, community guidelines - Internal: Slack channels, internal tools, compliance requirements, team contacts **Project type adjustments:** - AI Agents: MCP architecture, prompt patterns, example interactions - Infrastructure: Terraform/K8s setup, deployment procedures, DR plans - Microservices: API schemas, service mesh, health checks - CLI Tools: Installation methods, command examples, flags ### 4. File Generation Generate files in this order: 1. **README.md** first (most visible, sets tone) 2. **ARCHITECTURE.md** (technical foundation) 3. **DEVELOPER_GUIDE.md** (setup and contribution) 4. **USER_GUIDE.md** (end-user focused) 5. **CONTRIBUTING.md** (community guidelines) Each file must: - Use clear headers and structure from templates - Include concrete, runnable examples - Reference other docs when needed (avoid duplication) - Match project's actual structure and commands ### 5. Template Application For each file: 1. Select template variant from `references/templates.md` 2. Fill in project-specific details 3. Add context-appropriate sections 4. Ensure consistency across all files ### 6. Quality Checks Before finalizing, verify: - All code examples are runnable and accurate - Commands match detected language/tooling - Cross-references between docs are correct - No placeholder text remains - Tone is consistent (technical/friendly/formal based on context) ### 7. Output Place all files in `docs/` and use `present_files` to share with user. ## Resources ### references/templates.md Contains complete documentation templates for all five core files with variants for: - Python vs Go projects - OpenSource vs internal contexts - Different project types (agent, service, CLI, infra) - Different complexity levels Claude should read this file to select appropriate templates before generating docs. ## Special Considerations **For AI Agent projects:** - Explain MCP server architecture - Document tool integrations - Show example prompts and interactions - Include LLM configuration details **For Infrastructure/DevOps:** - Environment requirements (cloud providers, versions) - Deployment runbooks - Monitoring setup - Disaster recovery procedures **For Microservices:** - API endpoint documentation - Service dependency diagrams - Inter-service communication patterns - Health check and metrics endpoints ## Quality Standards Every documentation file must: - Have table of contents for files >200 lines - Use proper code fences with language tags - Include "Quick Start" section at top - Show real, tested examples - Explain "why" decisions were made - Use consistent terminology throughout ## Avoid - Generic placeholder text like "TODO" or "Coming soon" - Outdated technology references - Overly complex explanations without examples - Duplicating content across multiple files - Missing concrete code examples
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