Code reviewer
Skill OpenSIN-AI/OpenSIN-Skills/engineering-team/code-reviewer
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin. 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.From its SKILL.md
npx -y skills add OpenSIN-AI/OpenSIN-Skills --skill code-reviewerAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
4.6 KB, ~1.0k tokens by cl100k_base, as published. Nobody here has run it
Code Reviewer
Automated code review tools for analyzing pull requests, detecting code quality issues, and generating review reports.
Table of Contents
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
anytypes - 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:
| 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 |
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:
| 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 |
Reference Guides
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
| Language | Extensions |
|---|---|
| Python | .py |
| TypeScript | .ts, .tsx |
| JavaScript | .js, .jsx, .mjs |
| Go | .go |
| Swift | .swift |
| Kotlin | .kt, .kts |
What ships with it: 6 files
83.5 KB alongside SKILL.md, 3 of them executable
references/
- code_review_checklist.md6.9 KB
- coding_standards.md12.1 KB
- common_antipatterns.md16.1 KB
scripts/
- code_quality_checker.pyruns17.1 KB
- pr_analyzer.pyruns14.2 KB
- review_report_generator.pyruns17.1 KB
Gives 0 of the 12 instructions most code review skills give in ~1.0k tokens
Counted across 668 of the 814 authors here whose files we hold, read 2026-09-06
- Provide technical reasoning when pushing backin 84 of 668, across 70 files
- Fix critical issues immediatelyin 77 of 668, across 60 files
- Dispatch a code reviewer subagentin 76 of 668, across 59 files
- Fix important issues before proceedingin 73 of 668, across 56 files
- Ask for clarification on unclear itemsin 68 of 668, across 56 files
- Verify feedback against codebase before implementationin 66 of 668, across 55 files
- Implement fixes one at a timein 64 of 668, across 53 files
- Test each fix individuallyin 62 of 668, across 51 files
- Restate technical requirements in own wordsin 57 of 668, across 46 files
- Reply to inline comments in the specific threadin 51 of 668, across 40 files
- Note minor issues for laterin 49 of 668, across 34 files
- Group findings by severityin 48 of 668, across 47 files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.