engineering-team/skills/code-reviewer/SKILL.md
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
npx skillsauth add alirezarezvani/claude-skills code-reviewerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Automated code review tools for analyzing pull requests, detecting code quality issues, and generating review reports.
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
SKILL.md) — tools and thresholdsrules/universal.md — always, for every languagelanguages/*.md — one file based on the extension table belowThat is always exactly 2 additional files, regardless of scope.
| Extension(s) | Load |
|---|---|
| .py | languages/python.md |
| .ts, .tsx, .js, .jsx, .mjs | languages/typescript.md |
| .go | languages/go.md |
| .swift | languages/swift.md |
| .kt, .kts | languages/kotlin.md |
| .cs, .csx, .razor, .cshtml | languages/csharp.md |
| .java | languages/java.md |
| .c, .h | languages/c.md |
| .cpp, .cc, .cxx, .hpp, .hh, .hxx | languages/cpp.md |
| .rs | languages/rust.md |
| .rb, .rake, .gemspec, .ru | languages/ruby.md |
| .php, .phtml | languages/php.md |
| .dart | languages/dart.md |
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):
Language-specific detections are defined in each languages/*.md file.
Output includes:
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:
| Issue | Threshold | |-------|-----------| | Long function | >50 lines | | Large file | >500 lines | | God class | >20 methods | | Too many params | >5 | | Deep nesting | >4 levels | | High complexity | >10 branches |
Language-specific checks are defined in each languages/*.md file.
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:
| Score | Verdict | |-------|---------| | 90+ with no high issues | Approve | | 75+ with ≤2 high issues | Approve with suggestions | | 50-74 | Request changes | | <50 or critical issues | Block |
Reviewer guidance (required):
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.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:
LANGUAGE_EXTENSIONS in scripts/code_quality_checker.py (this also adds the --language choice).function / class / method regex entries for the language in the same file; otherwise it falls back to the Python patterns.check_<name>_specific_smells(...) detector (see the C#, Java, and C ones) and call it from analyze_file.assets/sample_<name>_smells.<ext> + _clean fixtures and commit the expected --json output under expected_outputs/ as a regression guard.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
tools
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
development
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
development
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.
development
Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.