code-reviewer

Code Reviewer

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

---

How This Skill Is Organized

code-reviewer/
  SKILL.md                        ← you are here (tools + dispatch table)
  rules/
    universal.md                  ← security, async, resources, exceptions, performance — all languages
  languages/
    python.md                     ← Python-specific rules + idioms
    typescript.md                 ← TypeScript / JavaScript-specific rules + idioms
    go.md                         ← Go-specific rules + idioms
    swift.md                      ← Swift-specific rules + idioms
    kotlin.md                     ← Kotlin-specific rules + idioms
    csharp.md                     ← C# / .NET-specific rules + idioms
    java.md                       ← Java-specific rules + idioms
    c.md                          ← C -specific rules + idioms
    cpp.md                        ← C++ -specific rules + idioms
    rust.md                       ← Rust -specific rules + idioms
    ruby.md                       ← Ruby -specific rules + idioms
    php.md                        ← PHP-specific rules + idioms
    dart.md                       ← Dart / Flutter-specific rules + idioms

Loading order for every review

1. This file (SKILL.md) — tools and thresholds
2. rules/universal.md — always, for every language
3. The matching languages/*.md — one file based on the extension table below

That is always exactly 2 additional files, regardless of scope.

---

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 (universal — see also language file for language-specific signals):

• Hardcoded secrets (passwords, API keys, tokens, connection strings)
• SQL / query injection patterns
• Debug statements left in production code
• Lint / analyzer suppression annotations
• TODO/FIXME comments

Language-specific detections are defined in each languages/*.md file.

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
# Valid values: python, typescript, javascript, go, swift, kotlin, csharp, java, c, cpp, rust, ruby, php, dart
python scripts/code_quality_checker.py . --language java

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

Universal thresholds:

Language-specific checks are defined in each languages/*.md file.

---

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

Verdicts:

---

Adding a New Language

Reviewer guidance (required):

1. Create languages/<name>.md using any existing language file as a template — it must have sections: PR Analyzer Signals, Code Quality Checks, Security, Async, Resource Management, Exception Handling, Performance, Idioms.
2. Add the extension row to the dispatch table above.

That is all the agent-driven review needs.

Deterministic analyzer support (optional, recommended): the bundled scripts only flag a language they explicitly know. To make code_quality_checker.py score the new language:

1. Add the extensions to LANGUAGE_EXTENSIONS in scripts/code_quality_checker.py (this also adds the --language choice).
2. Add function / class / method regex entries for the language in the same file; otherwise it falls back to the Python patterns.
3. Optionally add a check_<name>_specific_smells(...) detector (see the C#, Java, and C ones) and call it from analyze_file.
4. Add assets/sample_<name>_smells.<ext> + _clean fixtures and commit the expected --json output under expected_outputs/ as a regression guard.

---

Regression Fixtures

Labelled fixtures live in assets/ with their committed --json output in expected_outputs/ (C#, Java, and C). Drift from the committed JSON signals a behaviour change in the analyzer:

python scripts/code_quality_checker.py assets/sample_java_smells.java --json \
  | diff - expected_outputs/sample_java_smells_quality.json