Install
$ agentstack add skill-jjmartres-opencode-project-docs ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
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, CODEOFCONDUCT, 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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: jjmartres
- Source: jjmartres/opencode
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.