Code Review Checklist Generator

I'll generate comprehensive, context-aware code review checklists based on your changes.

Arguments: $ARGUMENTS - branch name, commit range, or PR number (optional)

Checklist Philosophy

Generate checklists that are:

Framework-specific and relevant
Based on actual changes, not generic
Security-focused for sensitive changes
Performance-aware for critical paths
Actionable and measurable

Token Optimization

This skill uses multiple optimization strategies to minimize token usage while maintaining comprehensive checklist generation:

1. Git Diff Statistics for Overview (800 token savings)

Pattern: Use git diff --stat instead of reading all changed files

# Instead of: Read all changed files (1,200+ tokens)
# Use: git diff --stat (400 tokens)

# Get high-level overview
git diff --stat "$BASE_BRANCH"...HEAD

# Count changes without reading files
total_files=$(git diff --name-only "$BASE_BRANCH"...HEAD | wc -l)
additions=$(git diff --numstat "$BASE_BRANCH"...HEAD | awk '{sum+=$1} END {print sum}')
deletions=$(git diff --numstat "$BASE_BRANCH"...HEAD | awk '{sum+=$2} END {print sum}')

Savings:

Git stats: ~400 tokens (metadata only)
Full file read: ~1,200 tokens (all changed files)
800 token savings (67%)

2. Codebase Analysis Caching (600 token savings)

Pattern: Cache framework/stack detection results

# Cache file: .code-review-stack.cache
# Format: JSON with detected technologies
# TTL: 24 hours (stack rarely changes)

if [ -f ".code-review-stack.cache" ] && [ $(($(date +%s) - $(stat -c %Y .code-review-stack.cache))) -lt 86400 ]; then
    # Read cached stack (80 tokens)
    STACK=$(cat .code-review-stack.cache)
else
    # Full stack detection (680 tokens)
    detect_stack
    echo "$STACK" > .code-review-stack.cache
fi

Savings:

Cached: ~80 tokens (read cache file)
Uncached: ~680 tokens (package.json/requirements.txt analysis, multiple Grep operations)
600 token savings (88%) for subsequent runs

3. Template-Based Checklist Generation (1,800 token savings)

Pattern: Use comprehensive pre-defined checklist template with conditional sections

# Instead of: LLM-generated checklist (2,200+ tokens)
# Use: Template-based checklist (400 tokens)

generate_checklist_template() {
    cat templates/base-checklist.md  # Base template (300 tokens)

    # Add framework-specific sections conditionally
    if [ "$HAS_REACT" = "true" ]; then
        cat templates/react-checklist.md  # 100 tokens
    fi

    if [ "$HAS_SECURITY_CHANGES" = "true" ]; then
        cat templates/security-checklist.md  # 150 tokens
    fi
}

Savings:

Template-based: ~400 tokens (template assembly)
LLM-generated: ~2,200 tokens (full checklist generation with analysis)
1,800 token savings (82%) per checklist

4. Focus Area Filtering (1,000 token savings)

Pattern: Generate only relevant checklist sections based on changes

# Instead of: Full comprehensive checklist (3,000+ tokens)
# Generate: Focused checklist (800 tokens)

# Analyze change categories
has_frontend=$(git diff --name-only "$BASE_BRANCH"...HEAD | grep -q '\.(tsx|jsx|vue)$' && echo "true" || echo "false")
has_backend=$(git diff --name-only "$BASE_BRANCH"...HEAD | grep -q '\.(py|go|java)$' && echo "true" || echo "false")
has_tests=$(git diff --name-only "$BASE_BRANCH"...HEAD | grep -q 'test\|spec' && echo "true" || echo "false")

# Include only relevant sections
[ "$has_frontend" = "true" ] && include_section "Frontend Performance"
[ "$has_backend" = "true" ] && include_section "API Design"
[ "$has_tests" = "true" ] && include_section "Test Quality"

Savings:

Focused checklist: ~800 tokens (3-5 relevant sections)
Full checklist: ~3,000 tokens (all 10 sections)
1,000 token savings (73%) for focused reviews

5. Early Exit for No Changes (95% savings)

Pattern: Detect no changes immediately

# Quick check: Are there any changes?
if ! git diff --quiet "$BASE_BRANCH"...HEAD; then
    CHANGE_COUNT=$(git diff --name-only "$BASE_BRANCH"...HEAD | wc -l)

    if [ "$CHANGE_COUNT" -eq 0 ]; then
        echo "✓ No changes to review"
        exit 0  # 100 tokens total
    fi
else
    echo "✓ No changes to review"
    exit 0
fi

# Otherwise: Full checklist generation (2,500+ tokens)

Savings:

No changes: ~100 tokens (early exit)
Full generation: ~2,500+ tokens
2,400+ token savings (96%) when no changes exist

6. Security Risk Pattern Matching (500 token savings)

Pattern: Use Grep for security pattern detection instead of full file analysis

# Instead of: Read files for security analysis (800 tokens)
# Use: Grep for security patterns (300 tokens)

# Detect authentication changes
git diff "$BASE_BRANCH"...HEAD | grep -i -E "auth|login|password|token|jwt" > /dev/null && AUTH_CHANGES=true

# Detect SQL queries
git diff "$BASE_BRANCH"...HEAD | grep -E "query|execute|raw|sql" > /dev/null && SQL_CHANGES=true

# Detect file operations
git diff "$BASE_BRANCH"...HEAD | grep -E "fs\.|readFile|writeFile" > /dev/null && FILE_OPS=true

Savings:

Grep patterns: ~300 tokens
Full file analysis: ~800 tokens (read changed files)
500 token savings (62%)

7. Checklist Section Caching (400 token savings)

Pattern: Cache common checklist sections for reuse

# Cache file: .code-review-sections.cache
# Contains pre-loaded checklist sections
# TTL: 24 hours

load_sections() {
    local cache_file=".code-review-sections.cache"

    if [ -f "$cache_file" ]; then
        source "$cache_file"  # 100 tokens
        return
    fi

    # Load all sections (500 tokens)
    SECTION_CODE_QUALITY=$(cat templates/code-quality.md)
    SECTION_SECURITY=$(cat templates/security.md)
    SECTION_PERFORMANCE=$(cat templates/performance.md)
    # ... more sections

    declare -p SECTION_* > "$cache_file"
}

Savings:

Cached sections: ~100 tokens
Load sections: ~500 tokens (multiple file reads)
400 token savings (80%) for subsequent runs

8. File Category Analysis Optimization (300 token savings)

Pattern: Use awk for efficient file categorization

# Instead of: Multiple grep operations (500 tokens)
# Use: Single awk command (200 tokens)

git diff --name-only "$BASE_BRANCH"...HEAD | awk -F'/' '{
    if ($0 ~ /test/) tests++
    else if ($0 ~ /\.md$/) docs++
    else if ($0 ~ /\.(ts|js|tsx|jsx)$/) frontend++
    else if ($0 ~ /\.(py|go|java|rb)$/) backend++
    else if ($0 ~ /\.(yml|yaml|json|toml)$/) config++
    else others++
}
END {
    if (tests) print "Tests: " tests
    if (docs) print "Documentation: " docs
    if (frontend) print "Frontend: " frontend
    if (backend) print "Backend: " backend
    if (config) print "Config: " config
    if (others) print "Other: " others
}'

Savings:

Awk categorization: ~200 tokens
Multiple Grep operations: ~500 tokens
300 token savings (60%)

9. Real-World Token Usage Distribution

Typical Scenarios:

First Run - Medium PR (1,800-2,500 tokens)
- Git diff stats: 400 tokens
- Stack detection: 680 tokens
- File categorization: 200 tokens
- Security analysis: 300 tokens
- Generate focused checklist: 800 tokens
- Total: ~2,380 tokens
Subsequent Run - Same Stack (1,000-1,500 tokens)
- Git diff stats: 400 tokens
- Stack detection (cached): 80 tokens
- File categorization: 200 tokens
- Security analysis: 300 tokens
- Load sections (cached): 100 tokens
- Generate checklist: 400 tokens
- Total: ~1,480 tokens
No Changes to Review (80-120 tokens)
- Git diff check: 50 tokens
- Early exit: 50 tokens
- Total: ~100 tokens
Large PR with Multiple Areas (2,500-3,500 tokens)
- Git diff stats: 400 tokens
- Stack detection: 680 tokens
- File categorization: 200 tokens
- Security analysis: 300 tokens
- Performance analysis: 300 tokens
- Generate comprehensive checklist: 2,000 tokens
- Total: ~3,880 tokens
Small Frontend Change (800-1,200 tokens)
- Git diff stats: 400 tokens
- Stack detection (cached): 80 tokens
- File categorization: 200 tokens
- Generate focused frontend checklist: 400 tokens
- Total: ~1,080 tokens

Expected Token Savings:

Average 40% reduction from baseline (2,500 → 1,500 tokens)
96% reduction when no changes exist
Aggregate savings: 1,000-1,500 tokens per checklist generation

Optimization Summary

Strategy	Savings	When Applied
Git diff statistics	800 tokens (67%)	Always
Codebase analysis caching	600 tokens (88%)	Subsequent runs
Template-based generation	1,800 tokens (82%)	Always
Focus area filtering	1,000 tokens (73%)	Focused reviews
Early exit for no changes	2,400 tokens (96%)	No changes
Security pattern matching	500 tokens (62%)	Always
Checklist section caching	400 tokens (80%)	Subsequent runs
File categorization optimization	300 tokens (60%)	Always

Key Insight: The combination of git diff statistics, template-based generation, and focus area filtering provides 40-50% token reduction while maintaining comprehensive review guidance. Early exit patterns provide 96% savings when no changes exist.

Pre-Flight Checks

Before generating checklist, I'll verify:

Git repository exists
Changes to review exist
Can access modified files
Framework/stack detected

<think> Checklist Strategy: - What files changed? - What frameworks are involved? - Are there security implications? - Performance impacts? - What testing is needed? </think>

First, let me analyze the changes:

#!/bin/bash
# Analyze changes for review checklist

set -e

echo "=== Code Review Analysis ==="
echo ""

# Parse arguments for branch/commit range
TARGET="${ARGUMENTS:-HEAD}"

# 1. Verify git repository
if ! git rev-parse --git-dir > /dev/null 2>&1; then
    echo "Error: Not a git repository"
    exit 1
fi

# 2. Determine comparison target
echo "Review Target: $TARGET"
echo ""

# 3. Get changed files
echo "Changed Files:"
if [ "$TARGET" = "HEAD" ]; then
    # Compare with main/master
    BASE_BRANCH=$(git symbolic-ref refs/remotes/origin/HEAD 2>/dev/null | sed 's@^refs/remotes/origin/@@' || echo "main")
    git diff --stat "$BASE_BRANCH"...HEAD
else
    git diff --stat "$TARGET"
fi
echo ""

# 4. Count changes
total_files=$(git diff --name-only "$BASE_BRANCH"...HEAD 2>/dev/null | wc -l || echo "0")
additions=$(git diff --numstat "$BASE_BRANCH"...HEAD 2>/dev/null | awk '{sum+=$1} END {print sum}' || echo "0")
deletions=$(git diff --numstat "$BASE_BRANCH"...HEAD 2>/dev/null | awk '{sum+=$2} END {print sum}' || echo "0")

echo "Change Summary:"
echo "  Files changed: $total_files"
echo "  Lines added: $additions"
echo "  Lines deleted: $deletions"
echo ""

# 5. Categorize changes
echo "File Categories:"
git diff --name-only "$BASE_BRANCH"...HEAD 2>/dev/null | awk -F'/' '{
    if ($0 ~ /test/) tests++
    else if ($0 ~ /\.md$/) docs++
    else if ($0 ~ /\.(ts|js|tsx|jsx)$/) frontend++
    else if ($0 ~ /\.(py|go|java|rb)$/) backend++
    else if ($0 ~ /\.(yml|yaml|json|toml)$/) config++
    else if ($0 ~ /\.(css|scss|sass)$/) styles++
    else others++
}
END {
    if (tests) print "  Tests: " tests
    if (docs) print "  Documentation: " docs
    if (frontend) print "  Frontend: " frontend
    if (backend) print "  Backend: " backend
    if (config) print "  Config: " config
    if (styles) print "  Styles: " styles
    if (others) print "  Other: " others
}'
echo ""

Phase 1: Framework & Stack Detection

Detect technologies to generate relevant checks:

#!/bin/bash
# Detect frameworks and technologies

detect_stack() {
    echo "=== Technology Stack Detection ==="
    echo ""

    # Frontend frameworks
    if [ -f "package.json" ]; then
        echo "JavaScript/TypeScript:"
        if grep -q "\"react\":" package.json; then
            echo "  ✓ React"
        fi
        if grep -q "\"vue\":" package.json; then
            echo "  ✓ Vue"
        fi
        if grep -q "\"next\":" package.json; then
            echo "  ✓ Next.js"
        fi
        if grep -q "\"angular\":" package.json; then
            echo "  ✓ Angular"
        fi
        echo ""
    fi

    # Backend frameworks
    if [ -f "requirements.txt" ] || [ -f "pyproject.toml" ]; then
        echo "Python:"
        if grep -q "django" requirements.txt 2>/dev/null || grep -q "django" pyproject.toml 2>/dev/null; then
            echo "  ✓ Django"
        fi
        if grep -q "flask" requirements.txt 2>/dev/null || grep -q "flask" pyproject.toml 2>/dev/null; then
            echo "  ✓ Flask"
        fi
        if grep -q "fastapi" requirements.txt 2>/dev/null || grep -q "fastapi" pyproject.toml 2>/dev/null; then
            echo "  ✓ FastAPI"
        fi
        echo ""
    fi

    # Database
    if [ -f "package.json" ]; then
        echo "Database:"
        if grep -q "\"prisma\":" package.json; then
            echo "  ✓ Prisma ORM"
        fi
        if grep -q "\"typeorm\":" package.json; then
            echo "  ✓ TypeORM"
        fi
        if grep -q "\"pg\":" package.json || grep -q "\"postgres\":" package.json; then
            echo "  ✓ PostgreSQL"
        fi
        if grep -q "\"mongodb\":" package.json || grep -q "\"mongoose\":" package.json; then
            echo "  ✓ MongoDB"
        fi
        echo ""
    fi

    # Testing frameworks
    echo "Testing:"
    if grep -q "\"jest\":" package.json 2>/dev/null; then
        echo "  ✓ Jest"
    fi
    if grep -q "\"vitest\":" package.json 2>/dev/null; then
        echo "  ✓ Vitest"
    fi
    if grep -q "\"playwright\":" package.json 2>/dev/null; then
        echo "  ✓ Playwright"
    fi
    if grep -q "pytest" requirements.txt 2>/dev/null; then
        echo "  ✓ Pytest"
    fi
    echo ""
}

detect_stack

Phase 2: Security Analysis

Analyze changes for security implications:

#!/bin/bash
# Security-focused analysis

analyze_security_risks() {
    echo "=== Security Risk Analysis ==="
    echo ""

    local has_security_risks=0

    # Check for authentication/authorization changes
    echo "Authentication & Authorization:"
    if git diff "$BASE_BRANCH"...HEAD | grep -i -E "auth|login|password|token|session|jwt|oauth" > /dev/null; then
        echo "  ⚠ Authentication-related changes detected"
        has_security_risks=1
    else
        echo "  ✓ No auth changes"
    fi
    echo ""

    # Check for database queries
    echo "Database Security:"
    if git diff "$BASE_BRANCH"...HEAD | grep -E "query|execute|raw|sql" > /dev/null; then
        echo "  ⚠ Database query changes detected"
        echo "    → Review for SQL injection risks"
        has_security_risks=1
    else
        echo "  ✓ No raw queries"
    fi
    echo ""

    # Check for user input handling
    echo "Input Validation:"
    if git diff "$BASE_BRANCH"...HEAD | grep -E "req\.body|req\.query|request\.|input|form" > /dev/null; then
        echo "  ⚠ User input handling detected"
        echo "    → Review validation and sanitization"
        has_security_risks=1
    else
        echo "  ✓ No input handling changes"
    fi
    echo ""

    # Check for file operations
    echo "File Operations:"
    if git diff "$BASE_BRANCH"...HEAD | grep -E "fs\.|readFile|writeFile|unlink|path\.join" > /dev/null; then
        echo "  ⚠ File system operations detected"
        echo "    → Review for path traversal vulnerabilities"
        has_security_risks=1
    else
        echo "  ✓ No file operations"
    fi
    echo ""

    # Check for environment variables
    echo "Secrets Management:"
    if git diff "$BASE_BRANCH"...HEAD | grep -E "process\.env|API_KEY|SECRET|PASSWORD" > /dev/null; then
        echo "  ⚠ Environment variable usage detected"
        echo "    → Verify no secrets in code"
        has_security_risks=1
    else
        echo "  ✓ No environment changes"
    fi
    echo ""

    if [ $has_security_risks -eq 1 ]; then
        echo "⚠ Security-sensitive changes require thorough review"
    else
        echo "✓ No major security risks detected"
    fi
    echo ""
}

analyze_security_risks

Phase 3: Performance Analysis

Check for performance implications:

#!/bin/bash
# Performance impact analysis

analyze_performance_impact() {
    echo "=== Performance Impact Analysis ==="
    echo ""

    local has_perf_concerns=0

    # Check for database queries in loops
    echo "Database Performance:"
    if git diff "$BASE_BRANCH"...HEAD | grep -B5 -A5 "for\|while\|map" | grep -E "query|find|create|update|delete" > /dev/null; then
        echo "  ⚠ Potential N+1 query pattern"
        echo "    → Review for bulk operations"
        has_perf_concerns=1
    else
        echo "  ✓ No obvious N+1 patterns"
    fi
    echo ""

    # Check for synchronous operations
    echo "Async Operations:"
    if git diff "$BASE_BRANCH"...HEAD | grep -E "fs\.readFileSync|fs\.writeFileSync|execSync" > /dev/null; then
        echo "  ⚠ Synchronous operations detected"
        echo "    → Consider async alternatives"
        has_perf_concerns=1
    else
        echo "  ✓ No blocking operations"
    fi
    echo ""

    # Check for large data processing
    echo "Data Processing:"
    if git diff "$BASE_BRANCH"...HEAD | grep -E "\.map\(|\.filter\(|\.reduce\(" > /dev/null; then
        echo "  ⓘ Array operations detected"
        echo "    → Verify efficient for large datasets"
    fi
    echo ""

    # Check for caching
    echo "Caching:"
    if git diff "$BASE_BRANCH"...HEAD | grep -i -E "cache|redis|memcached|memo" > /dev/null; then
        echo "  ✓ Caching implemented"
    else
        echo "  ⓘ Consider caching for frequently accessed data"
    fi
    echo ""

    if [ $has_perf_concerns -eq 1 ]; then
        echo "⚠ Performance concerns require review"
    else
        echo "✓ No major performance concerns"
    fi
    echo ""
}

analyze_performance_impact

Phase 4: Generate Checklist

Create comprehensive, context-aware checklist:

Now I'll generate a customized code review checklist based on the analysis:

Code Review Checklist

Review Information:

Date: [timestamp]
Reviewer: _____________
Changes: [X files, Y additions, Z deletions]
Risk Level: Low / Medium / High

1. Code Quality

General:

Code is readable and self-documenting
Naming follows project conventions
No commented-out code
No unnecessary console.log/print statements
Error messages are helpful and actionable
No hardcoded values that should be config

TypeScript/JavaScript Specific:

Python Specific:

Type hints provided
Follows PEP 8 style guide
Context managers used for resources
List comprehensions over loops where appropriate

2. Functionality

Core Logic:

Business Logic:

Follows business rules
Data transformations correct
Calculations verified
State management appropriate

3. Security

Authentication & Authorization:

Authentication required where needed
Authorization checks present
Session management secure
Token validation implemented
No authentication bypass possible

Input Validation:

Data Protection:

Dependencies:

No known vulnerabilities in new dependencies
Dependencies from trusted sources
Version pinning appropriate

4. Performance

Database:

Caching:

Appropriate caching strategy
Cache invalidation handled
Cache keys unique and clear

Resource Management:

Memory leaks prevented
File handles closed
Connections released
Large files streamed, not loaded

Frontend Performance:

Components optimized (React.memo, useMemo)
Images optimized and lazy-loaded
Code splitting implemented
Bundle size acceptable

5. Testing

Test Coverage:

Test Quality:

Manual Testing:

Feature tested locally
Works in different browsers (if frontend)
Mobile responsive (if applicable)
Accessibility verified

6. Architecture & Design

Code Organization:

Database Schema:

Migrations included
Schema changes backward compatible
Foreign keys defined
Indexes on frequently queried columns

API Design:

7. Documentation

Code Documentation:

User Documentation:

User-facing changes documented
Migration guide if breaking changes
Examples provided

8. Integration

Dependencies:

Compatible with existing code
Dependencies updated in package.json/requirements.txt
No breaking changes to public APIs
Backward compatibility maintained

CI/CD:

Database:

Migrations tested
Rollback plan exists
Seed data updated

9. Deployment

Configuration:

Environment variables documented
Feature flags configured
Monitoring/logging added

Rollback:

Rollback plan documented
Database migrations reversible
Feature flags allow quick disable

10. Framework-Specific Checks

React:

Hooks follow rules of hooks
useEffect dependencies correct
No prop drilling (use context if needed)
Components reasonably sized
Key props on lists

Next.js:

Server/client components appropriate
Data fetching optimized
SEO meta tags included
ISR/SSR/SSG appropriate

Django:

Models have proper Meta class
Migrations properly named
Admin interface configured
Signals used appropriately

FastAPI:

Pydantic models for validation
Dependency injection used
Background tasks for long operations
OpenAPI docs accurate

Review Summary

Strengths:

Concerns:

Blocking Issues:

Suggestions:

Decision:

Approve
Approve with comments
Request changes
Needs discussion

Reviewer Signature: _____________ Date: _____________

Integration with /review Skill

This checklist complements /review:

/review - Automated code analysis
/code-review-checklist - Human review guidance

Workflow:

# 1. Run automated review
/review

# 2. Generate human review checklist
/code-review-checklist

# 3. Perform manual review using checklist

# 4. Approve or request changes

Customization

I'll customize checklists based on:

File types changed - Frontend vs backend checks
Frameworks detected - Framework-specific checks
Security sensitivity - Enhanced security section
Performance critical - Enhanced performance section
Database changes - Schema review section
API changes - API design section

Export Formats

I can export checklists as:

Markdown - For GitHub PR templates
Plain text - For internal reviews
JSON - For automation tools

#!/bin/bash
# Save checklist to file

save_checklist() {
    local output_file="CODE_REVIEW_CHECKLIST.md"

    echo "Saving checklist to: $output_file"
    # Checklist content written to file

    echo "✓ Checklist saved"
    echo ""
    echo "Usage:"
    echo "  1. Copy to PR description"
    echo "  2. Use as review template"
    echo "  3. Track review progress"
}

What I'll Actually Do

Analyze Changes - Use git diff to understand scope
Detect Stack - Identify frameworks and technologies
Security Scan - Flag security-sensitive changes
Performance Check - Identify performance risks
Generate Checklist - Create relevant, actionable checklist
Customize Sections - Focus on actual changes
Save Template - Export for use

Important: I will NEVER:

Generate generic, irrelevant checklists
Miss critical security concerns
Ignore framework-specific patterns
Add AI attribution

The checklist will be comprehensive, relevant, and immediately useful for code reviews.

Credits: Checklist methodology based on industry best practices, OWASP security guidelines, and framework-specific review patterns.

code-review-checklist

Safety Notice

Copy this and send it to your AI assistant to learn

Code Review Checklist Generator

Checklist Philosophy

Token Optimization

1. Git Diff Statistics for Overview (800 token savings)

2. Codebase Analysis Caching (600 token savings)

3. Template-Based Checklist Generation (1,800 token savings)

4. Focus Area Filtering (1,000 token savings)

5. Early Exit for No Changes (95% savings)

6. Security Risk Pattern Matching (500 token savings)

7. Checklist Section Caching (400 token savings)

8. File Category Analysis Optimization (300 token savings)

9. Real-World Token Usage Distribution

Optimization Summary

Pre-Flight Checks

Phase 1: Framework & Stack Detection

Phase 2: Security Analysis

Phase 3: Performance Analysis

Phase 4: Generate Checklist

Code Review Checklist

1. Code Quality

2. Functionality

3. Security

4. Performance

5. Testing

6. Architecture & Design

7. Documentation

8. Integration

9. Deployment

10. Framework-Specific Checks

Review Summary

Strengths:

Concerns:

Blocking Issues:

Suggestions:

Integration with /review Skill

Customization

Export Formats

What I'll Actually Do

Source Transparency

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