Code Reviewer

Automated code review tools for analyzing pull requests, detecting code quality issues, and generating review reports.

Table of Contents

• Tools
• PR Analyzer
• Code Quality Checker
• Review Report Generator
• Reference Guides
• Languages Supported

Tools

PR Analyzer

Analyzes git diff between branches to assess review complexity and identify risks.

# Analyze current branch against main
python scripts/pr_analyzer.py /path/to/repo

# Compare specific branches
python scripts/pr_analyzer.py . --base main --head feature-branch

# JSON output for integration
python scripts/pr_analyzer.py /path/to/repo --json

What it detects:

• Hardcoded secrets (passwords, API keys, tokens)
• SQL injection patterns (string concatenation in queries)
• Debug statements (debugger, console.log)
• ESLint rule disabling
• TypeScript any types
• TODO/FIXME comments

Output includes:

• Complexity score (1-10)
• Risk categorization (critical, high, medium, low)
• File prioritization for review order
• Commit message validation

Code Quality Checker

Analyzes source code for structural issues, code smells, and SOLID violations.

# Analyze a directory
python scripts/code_quality_checker.py /path/to/code

# Analyze specific language
python scripts/code_quality_checker.py . --language python

# JSON output
python scripts/code_quality_checker.py /path/to/code --json

What it detects:

• Long functions (>50 lines)
• Large files (>500 lines)
• God classes (>20 methods)
• Deep nesting (>4 levels)
• Too many parameters (>5)
• High cyclomatic complexity
• Missing error handling
• Unused imports
• Magic numbers

Thresholds:

Review Report Generator

Combines PR analysis and code quality findings into structured review reports.

# Generate report for current repo
python scripts/review_report_generator.py /path/to/repo

# Markdown output
python scripts/review_report_generator.py . --format markdown --output review.md

# Use pre-computed analyses
python scripts/review_report_generator.py . \
  --pr-analysis pr_results.json \
  --quality-analysis quality_results.json

Report includes:

• Review verdict (approve, request changes, block)
• Score (0-100)
• Prioritized action items
• Issue summary by severity
• Suggested review order

Verdicts:

References

Code Review Checklist

references/code_review_checklist.md

Systematic checklists covering:

• Pre-review checks (build, tests, PR hygiene)
• Correctness (logic, data handling, error handling)
• Security (input validation, injection prevention)
• Performance (efficiency, caching, scalability)
• Maintainability (code quality, naming, structure)
• Testing (coverage, quality, mocking)
• Language-specific checks

Coding Standards

references/coding_standards.md

Language-specific standards for:

• TypeScript (type annotations, null safety, async/await)
• JavaScript (declarations, patterns, modules)
• Python (type hints, exceptions, class design)
• Go (error handling, structs, concurrency)
• Swift (optionals, protocols, errors)
• Kotlin (null safety, data classes, coroutines)

Common Antipatterns

references/common_antipatterns.md

Antipattern catalog with examples and fixes:

• Structural (god class, long method, deep nesting)
• Logic (boolean blindness, stringly typed code)
• Security (SQL injection, hardcoded credentials)
• Performance (N+1 queries, unbounded collections)
• Testing (duplication, testing implementation)
• Async (floating promises, callback hell)

Languages Supported

Overview

Use this skill for the capability described in this document.

When to Use

Use this skill when the request matches the capability, constraints, and activation cues described below.

Core Workflow

Follow the primary workflow, commands, and decision points documented in the sections below.

Examples

Use the examples and snippets already present in this document whenever they apply to the task.

Best Practices

Follow the constraints, conventions, and cautions documented below, and prefer the documented path over improvisation.