| id | SKL-repo-REPOMAPSSOT |
| name | Repo Map Ssot |
| description | Single Source of Truth (SSOT) for repository structure that enables AI and humans to understand codebase immediately. Includes folder structure, key files, and architectural decisions in one place. |
| version | 1.0.0 |
| status | active |
| owner | @cerebra-team |
| last_updated | 2026-02-22 |
| category | Backend |
| tags | ["api","backend","server","database"] |
| stack | ["Python","Node.js","REST API","GraphQL"] |
| difficulty | Intermediate |
Repo Map Ssot
Skill Profile
(Select at least one profile to enable specific modules)
Overview
Single Source of Truth (SSOT) for repository structure that enables AI and humans to understand codebase immediately. Includes folder structure, key files, and architectural decisions in one place.
Why This Matters
- Quick orientation: Know what's where in 30 seconds
- AI efficiency: AI doesn't need to explore repeatedly
- Onboarding: New team members understand repo structure quickly
- Consistency: Everyone shares the same mental model
Core Concepts & Rules
1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs
2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment
Inputs / Outputs / Contracts
- Inputs:
- Repository structure
- Key files and directories
- Build/deploy configuration
- External integration details
- Entry Conditions:
- Repository exists
- Basic understanding of project structure
- Outputs:
- REPO.md file at repository root
- Clear documentation of structure, entry points, and boundaries
- Links to detailed documentation
- Artifacts Required (Deliverables):
- REPO.md file
- Directory structure documentation
- Entry point documentation
- Build/deploy command reference
- Acceptance Evidence:
- REPO.md exists at root
- All major directories documented
- Entry points clearly marked
- Links to detailed docs work
- Success Criteria:
- New team members can understand repo in 30 seconds
- AI can use REPO.md for context
- Map is kept up-to-date
Skill Composition
Quick Start / Implementation Example
- Review requirements and constraints
- Set up development environment
- Implement core functionality following patterns
- Write tests for critical paths
- Run tests and fix issues
- Document any deviations or decisions
def example_function():
pass
Assumptions / Constraints / Non-goals
- Assumptions:
- Development environment is properly configured
- Required dependencies are available
- Team has basic understanding of domain
- Constraints:
- Must follow existing codebase conventions
- Time and resource limitations
- Compatibility requirements
- Non-goals:
- This skill does not cover edge cases outside scope
- Not a replacement for formal training
Compatibility & Prerequisites
- Supported Versions:
- Python 3.8+
- Node.js 16+
- Modern browsers (Chrome, Firefox, Safari, Edge)
- Required AI Tools:
- Code editor (VS Code recommended)
- Testing framework appropriate for language
- Version control (Git)
- Dependencies:
- Language-specific package manager
- Build tools
- Testing libraries
- Environment Setup:
.env.example keys: API_KEY, DATABASE_URL (no values)
Test Scenario Matrix (QA Strategy)
| Type | Focus Area | Required Scenarios / Mocks |
|---|
| Unit | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage |
| Integration | DB / API | All external API calls or database connections must be mocked during unit tests |
| E2E | User Journey | Critical user flows to test |
| Performance | Latency / Load | Benchmark requirements |
| Security | Vuln / Auth | SAST/DAST or dependency audit |
| Frontend | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |
Technical Guardrails & Security Threat Model
1. Security & Privacy (Threat Model)
- Top Threats: Injection attacks, authentication bypass, data exposure
2. Performance & Resources
3. Architecture & Scalability
4. Observability & Reliability
Agent Directives & Error Recovery
(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)
- Thinking Process: Analyze root cause before fixing. Do not brute-force.
- Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
- Self-Review: Check against Guardrails & Anti-patterns before finalizing.
- Output Constraints: Output ONLY the modified code block. Do not explain unless asked.
Definition of Done (DoD) Checklist
Anti-patterns / Pitfalls
- ⛔ Don't: Log PII, catch-all exception, N+1 queries
- ⚠️ Watch out for: Common symptoms and quick fixes
- 💡 Instead: Use proper error handling, pagination, and logging
Reference Links & Examples
- Internal documentation and examples
- Official documentation and best practices
- Community resources and discussions
Versioning & Changelog
- Version: 1.0.0
- Changelog:
- 2026-02-22: Initial version with complete template structure