jjmartres/ai-coding-agents/document-project
Claude Code and agent skill by jjmartres/ai-coding-agents
- community
Checking that the SKILL.md file is still in the repository.
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.
Install
npx vskill@latest install jjmartres/ai-coding-agents/document-projectSKILL.md
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:
- README.md - Project overview, quick start, badges
- ARCHITECTURE.md - System design, components, data flow
- USER_GUIDE.md - Usage examples, configuration, troubleshooting
- DEVELOPER_GUIDE.md - Development setup, testing, contribution workflow
- 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:
- README.md first (most visible, sets tone)
- ARCHITECTURE.md (technical foundation)
- DEVELOPER_GUIDE.md (setup and contribution)
- USER_GUIDE.md (end-user focused)
- 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:
- Select template variant from
references/templates.md - Fill in project-specific details
- Add context-appropriate sections
- 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
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingshared/.ai-agents/skills/document-project/SKILL.md
Popularity
- 7-day trend score
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