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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.

45 stars
1.2k downloads
Updated 1/18/2026

Package Files

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SKILL.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:

  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

Install

Download ZIP
Requires askill CLI v1.0+

AI Quality Score

78/100Analyzed 2/19/2026

High-quality documentation skill with clear 7-step workflow, comprehensive coverage of project types (Python/Go, OpenSource/internal, AI/Infrastructure/Microservices), and good structure. Includes when-to-use guidance, tags, and detailed instructions. Minor deduction for reliance on external templates.md file not included in skill, and tags include some irrelevant items. Located in proper skills folder. Score benefits from high-density technical content, structured approach, and reusability despite path suggesting tool-specific context.

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Metadata

Licenseunknown
Version-
Updated1/18/2026
Publisherjjmartres

Tags

apici-cdgithub-actionslintingllmobservabilitypromptingsecuritytesting