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

• This file (SKILL.md) — tools and thresholds

• rules/universal.md — always, for every language

• 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):

• 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.

• 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:

• Add the extensions to LANGUAGE_EXTENSIONS in scripts/code_quality_checker.py (this also adds the --language choice).

• Add function / class / method regex entries for the language in the same file; otherwise it falls back to the Python patterns.

• Optionally add a check_<name>_specific_smells(...) detector (see the C#, Java, and C ones) and call it from analyze_file.

• 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