code-review
Skill154 starsChanged 7 months agoMITView on GitHub
Perform thorough code reviews with security, performance, and maintainability analysis. Use when user asks to review code, check for bugs, or audit a codebase.
- Installs packages
219 other projects use a version of this; 1 new project in the last 4 weeks, against 1 the 4 before.
Adopted by
Steady: 1 new project took it in the last 4 weeks, 1× the 4 weeks before.thin data
Most recently taken up by
- doggy8088/spec-kittheir own version, first seen Nov 9, 2026
- strelov1/freehiretheir own version, first seen Nov 9, 2026
- storybookjs/storybooktheir own version, first seen Nov 8, 2026
- codewithmukesh/dotnet-claude-kittheir own version, first seen Nov 8, 2026
- supabase/supabasetheir own version, first seen Nov 8, 2026
- The-Vibe-Company/quivrtheir own version, first seen Nov 8, 2026
Used with
Projects with it also use close-spec, design-with-images and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
dzhngSkillclose-spec
When a spec under specs/<feature>/ is done shipping (a write-spec build, or any planned task that produced a spec), archive it to specs/done/ and rewrite it from a build-plan into a durable rationale — the why, the principles, the invariants — pointing back to the real code for the how. Use when implementation has landed and the plan no longer matches what shipped, or the user says a feature/spec is finished. Pairs with [write-spec](../write-spec/SKILL.md) (the plan this closes) and [review](../review/SKILL.md) (run before closing).
Also in 5 projects that use this
dzhngSkilldesign-with-images
Explore UI and visual design with image generation, then iterate the real implementation against the user-selected concept. Use when brainstorming visual directions, offering mockup variants, or implementing an approved generated design; do not stop at the mockups or the first code approximation.
Also in 5 projects that use this
PydanticSkilladd-new-model
Add support for a newly-released language or image generation model in pydantic-ai (e.g. openai:gpt-5.6, anthropic:claude-sonnet-5, openai:gpt-image-2). Use when a provider ships a new model id and you need to wire literals, profile flags, adapters, and tests to recognize it. Handles SDK-lag, gateway list conventions, capability probing, and direct image-model geometry.
Also in 9 projects that use this
transitive-bullshitSkillcompare-screenshots
Compare screenshots against the intended design, distinguishing approved references from historical baselines. Use for iterative implementation matching, before/after visual review, objective image telemetry, or checking a lone capture for flat, empty, or badly framed content.
Also in 5 projects that use this
Microsoft AzureSkillazsdk-common-apiview-feedback-resolution
Analyze and resolve APIView review feedback on Azure SDK PRs. **UTILITY SKILL**. USE FOR: APIView comments, API review feedback, SDK API surface changes. DO NOT USE FOR: general code review, non-APIView feedback. INVOKES: azure-sdk-mcp:azsdk_apiview_get_comments, azure-sdk-mcp:azsdk_customized_code_update, azure-sdk-mcp:azsdk_typespec_delegate_apiview_feedback, azure-sdk-mcp:azsdk_run_typespec_validation, azure-sdk-mcp:azsdk_package_generate_code.
Also in 6 projects that use this
Microsoft AzureSkillazsdk-common-generate-sdk-locally
Generate and finalize Azure SDKs locally from TypeSpec, including build, checks, tests, changelog, metadata, and version updates by default. WHEN: "generate SDK locally", "build SDK", "run SDK tests", "run CI checks", "validate package", "run checks", "update changelog", "fix SDK build errors", "resolve SDK generation errors", "customize TypeSpec", "rename SDK client", "rename SDK model", "hide operation from SDK", "fix analyzer errors", "resolve customization drift", "create subclient", "update metadata", "update version". INVOKES: azsdk_verify_setup, azsdk_package_generate_code, azsdk_package_build_code, azsdk_package_run_check, azsdk_package_run_tests, azsdk_customized_code_update, azsdk_package_update_changelog_content, azsdk_package_update_metadata, azsdk_package_update_version.
Also in 6 projects that use this
- +4 more from dzhng
- +2 more from Azure
Other versions219 projects use a version of this.
Word for word · 27
- shareAI-lab/analysis_claude_code★ 80k100% same
- shareAI-lab/learn-claude-code★ 80k100% same
- shareAI-lab/mini-claude-code★ 373100% same
- wangyuxun6699/learn_claude_code_langchain★ 56100% same
- msober/mini-agent★ 40100% same
- hubooooooo/claude-code-harness-study★ 31100% same
- hubooooooo/claude-code-herness-study★ 31100% same
- truongpx396/learn-harness-engineering-by-building-mini-claude-code★ 7100% same
- alanmurder/learn-claude-code-langchain★ 4100% same
- JinSooo/learn-claude-code-ts★ 4100% same
17 more not listed
Edited · 0
None found.
Wrote their own version · 192
- mattpocock/skills★ 309ksame name
- TheAlgorithms/Python★ 226ksame name
- microsoft/vscode★ 195ksame name
- github/spec-kit★ 149ksame name
- ggml-org/llama.cpp★ 134ksame name
- supabase/supabase★ 115ksame name
- storybookjs/storybook★ 91ksame name
- vectorize-io/hindsight★ 60ksame name
- dbeaver/dbeaver★ 52ksame name
- roboflow/supervision★ 52ksame name
182 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository3 other files it gives its agents.
- agent-builderSkill
- mcp-builderSkill
- pdfSkill
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 182 edited versions we read keep these.
4
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 151
Understand context: Read PR description, linked issues
- Distinctive: line 153
Read top-down: Start with main entry points
- Distinctive: line 155
Security scan: Run automated tools
- Distinctive: line 156
Manual review: Use checklist above
“Common” is measured: kept by at least 6 in 10 of the 182 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Code Review Skill
You now have expertise in conducting comprehensive code reviews. Follow this structured approach:
Review Checklist
1. Security (Critical)
Check for:
- Injection vulnerabilities: SQL, command, XSS, template injection
- Authentication issues: Hardcoded credentials, weak auth
- Authorization flaws: Missing access controls, IDOR
- Data exposure: Sensitive data in logs, error messages
- Cryptography: Weak algorithms, improper key management
- Dependencies: Known vulnerabilities (check with
npm audit,pip-audit)
# Quick security scans
npm audit # Node.js
pip-audit # Python
cargo audit # Rust
grep -r "password\|secret\|api_key" --include="*.py" --include="*.js"2. Correctness
Check for:
- Logic errors: Off-by-one, null handling, edge cases
- Race conditions: Concurrent access without synchronization
- Resource leaks: Unclosed files, connections, memory
- Error handling: Swallowed exceptions, missing error paths
- Type safety: Implicit conversions, any types
3. Performance
Check for:
- N+1 queries: Database calls in loops
- Memory issues: Large allocations, retained references
- Blocking operations: Sync I/O in async code
- Inefficient algorithms: O(n^2) when O(n) possible
- Missing caching: Repeated expensive computations
4. Maintainability
Check for:
- Naming: Clear, consistent, descriptive
- Complexity: Functions > 50 lines, deep nesting > 3 levels
- Duplication: Copy-pasted code blocks
- Dead code: Unused imports, unreachable branches
- Comments: Outdated, redundant, or missing where needed
5. Testing
Check for:
- Coverage: Critical paths tested
- Edge cases: Null, empty, boundary values
- Mocking: External dependencies isolated
- Assertions: Meaningful, specific checks
Review Output Format
## Code Review: [file/component name]
### Summary
[1-2 sentence overview]
### Critical Issues
1. **[Issue]** (line X): [Description]
- Impact: [What could go wrong]
- Fix: [Suggested solution]
### Improvements
1. **[Suggestion]** (line X): [Description]
### Positive Notes
- [What was done well]
### Verdict
[ ] Ready to merge
[ ] Needs minor changes
[ ] Needs major revisionCommon Patterns to Flag
Python
# Bad: SQL injection
cursor.execute(f"SELECT * FROM users WHERE id = {user_id}")
# Good:
cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
# Bad: Command injection
os.system(f"ls {user_input}")
# Good:
subprocess.run(["ls", user_input], check=True)
# Bad: Mutable default argument
def append(item, lst=[]): # Bug: shared mutable default
# Good:
def append(item, lst=None):
lst = lst or []JavaScript/TypeScript
// Bad: Prototype pollution
Object.assign(target, userInput)
// Good:
Object.assign(target, sanitize(userInput))
// Bad: eval usage
eval(userCode)
// Good: Never use eval with user input
// Bad: Callback hell
getData(x => process(x, y => save(y, z => done(z))))
// Good:
const data = await getData();
const processed = await process(data);
await save(processed);Review Commands
# Show recent changes
git diff HEAD~5 --stat
git log --oneline -10
# Find potential issues
grep -rn "TODO\|FIXME\|HACK\|XXX" .
grep -rn "password\|secret\|token" . --include="*.py"
# Check complexity (Python)
pip install radon && radon cc . -a
# Check dependencies
npm outdated # Node
pip list --outdated # PythonReview Workflow
- Understand context: Read PR description, linked issues
- Run the code: Build, test, run locally if possible
- Read top-down: Start with main entry points
- Check tests: Are changes tested? Do tests pass?
- Security scan: Run automated tools
- Manual review: Use checklist above
- Write feedback: Be specific, suggest fixes, be kind
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

