viktorbezdek Comprehensive Code Review Skill
This skill is a curated merge of four complementary code review skills:
- Evidence-Based Code Review: Evidence-based multi-agent swarm review with specialized reviewers
- PR Comment Analysis: Extract, consolidate, and prioritize GitHub PR comments
- Development Workflow Specialist: TDD, debugging, performance optimization, and automated code review
- Consultant: AI-powered deep analysis for complex architectural and security reviews
When to Use
Use this skill when:
- Reviewing PRs systematically with multiple quality dimensions
- Extracting and consolidating feedback from multiple reviewers
- Need evidence-based findings with file:line references
- Assessing security vulnerabilities, performance bottlenecks, or code quality
- Creating action plans from PR comments
- Complex architectural decisions requiring deep analysis
- Audit requirements mandate systematic review (compliance, release gates)
Do NOT use for:
- Simple formatting fixes (use linter/prettier directly)
- Non-code files (documentation, configuration without logic)
- Trivial changes (typo fixes, comment updates)
- Generated code (build artifacts, vendor dependencies)
Core Capabilities
1. Evidence-Based Code Review
Every finding MUST include:
- Code location:
[file:line]with surrounding context (5 lines before/after) - Evidence type: DIRECT, STYLE_RULE, or BEST_PRACTICE
- Severity: CRITICAL, MAJOR, MINOR, or NIT
- Confidence score: 0.0-1.0
- Suggested fix with specific code changes
2. PR Comment Analysis
- Fetches ALL comments from GitHub PR (inline + conversation)
- Groups comments by file path and code section
- Identifies "High Consensus Issues" (2+ reviewers same concern)
- Three-phase analysis: Consolidation, Context Validation, Impact Analysis
3. Multi-Agent Swarm Review
Five specialized review agents working in parallel:
- Security Reviewer: Vulnerabilities, unsafe patterns, secrets
- Performance Analyst: Bottlenecks, optimization opportunities
- Style Reviewer: Code style, best practices, maintainability
- Test Specialist: Test coverage, quality, edge cases
- Documentation Reviewer: Comments, API docs, README updates
4. AI-Powered Consultation
- Supports 100+ LLM providers through LiteLLM
- Deep analysis for complex architectural decisions
- Async operation with session management
5. TRUST 5 Validation Framework
- Truthfulness: Code does what it claims
- Relevance: Changes are appropriate for the context
- Usability: Code is maintainable and understandable
- Safety: No security vulnerabilities or data risks
- Timeliness: Follows current best practices
Severity Levels
- CRITICAL: Architecture-level issues (security vulnerabilities, data loss risks)
- MAJOR: Module-level issues (performance bottlenecks, maintainability problems)
- MINOR: Function-level improvements (code style, readability)
- NIT: Line-level suggestions (formatting, naming)
Validation Rules
- NEVER approve code without evidence
- ALWAYS provide line numbers with every finding
- VALIDATE findings against multiple perspectives
- DISTINGUISH symptoms from root causes
- AVOID false confidence: flag uncertain findings as "needs manual review"
- Findings require 2+ confirming signals before flagging as violations
See Extended Patterns for detailed finding templates, PR comment workflow, multi-agent swarm execution, CI/CD integration, and quality gate configuration.
Success Criteria
- Violations Detected: All quality issues found with ZERO false negatives
- False Positive Rate: <5% (95%+ findings are genuine issues)
- Actionable Feedback: Every finding includes file path, line number, and fix guidance
- Root Cause Identified: Issues traced to underlying causes, not just symptoms
Resources Reference
Scripts
| Script | Purpose |
|---|---|
pr-comment-grabber.py |
Extract all PR comments from GitHub |
pr-comment-filter.py |
Filter comments by criteria |
analyze-pr.sh |
Full PR analysis workflow |
multi_agent_review.py |
Multi-agent swarm orchestration |
security_scan.sh |
Security vulnerability scanning |
performance_check.py |
Performance bottleneck detection |
style_audit.py |
Style and best practices audit |
consultant_cli.py |
AI consultation CLI |
References
| Reference | Description |
|---|---|
extended-patterns.md |
Detailed examples and workflow patterns |
analysis-prompt.md |
LLM prompt for PR comment analysis |
analysis-prompt-v2.md |
Enhanced analysis with validation |
github-api.md |
GitHub API endpoints reference |
impact-analysis-methodology.md |
Risk assessment framework |
validation-workflow.md |
Three-phase validation process |
best-practices.md |
Code review best practices |
review-categories.md |
Severity and scope categories |
Templates
| Template | Purpose |
|---|---|
review-checklist.yaml |
Code review checklist |
security-rules.json |
Security rule definitions |
performance-thresholds.json |
Performance thresholds |