| id | SKL-code-CODEREVIEW |
| name | Code Review |
| description | Code review is a systematic examination of source code intended to find bugs, improve code quality, enforce standards, and share knowledge across teams. Effective code reviews catch defects early, pre |
| 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 |
Code Review
Skill Profile
(Select at least one profile to enable specific modules)
Overview
Code review is a systematic examination of source code intended to find bugs, improve code quality, enforce standards, and share knowledge across teams. Effective code reviews catch defects early, prevent technical debt accumulation, accelerate onboarding, and foster collaborative learning through constructive feedback.
Why This Matters
- Catches Bugs Early: Reviews identify issues before they reach production, reducing incident costs and user impact
- Improves Code Quality: Consistent feedback enforces coding standards, maintainability, and best practices
- Accelerates Onboarding: New team members learn codebase architecture and patterns through reviewing others' code
- Shares Knowledge: Reviews distribute knowledge about system design, implementation details, and architectural decisions
- Prevents Technical Debt: Identifying and addressing code issues early prevents debt accumulation
- Builds Team Culture: Regular, constructive feedback creates collaborative learning environment and improves code quality over time
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:
- Pull request with code changes
- PR description and context
- Related issues and documentation
- Code quality reports (lint, test coverage)
- Entry Conditions:
- PR is created and ready for review
- Code compiles and tests pass
- PR description is complete
- Outputs:
- Review comments and feedback
- Approval status
- Requested changes
- Action items for author
- Artifacts Required (Deliverables):
- Review comments
- Approval/rejection decision
- Updated PR if changes requested
- Acceptance Evidence:
- All reviewers have provided feedback
- Critical issues are addressed
- PR is approved or changes are requested
- Success Criteria:
- All critical issues are identified and addressed
- Code quality improves
- Knowledge is shared
- PR can be merged safely
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