code-reviewer
Skill2 starsChanged 7 months agoMITView on GitHub
Run a structured five-stage code review on a diff, file, or pull request. Use whenever the user wants code reviewed, audited, or checked before merge. Triggers on "review this code", "audit this PR", "code review", "check this diff", "review my changes", or any time the user pastes code and wants quality, performance, or security feedback. Always use this skill rather than ad-hoc commentary when the user asks for a code review, even if they describe the request in their own words.
182 other projects use a version of this.
Adopted by
Most recently taken up by
- santiago-migoni/claude-skillstheir own version, first seen Nov 8, 2026
- SaehwanPark/agent-skills-archivetheir own version, first seen Nov 8, 2026
- nainishshafi/developer-productivity-skillstheir own version, first seen Nov 8, 2026
- Flowerf19/agents-skillstheir own version, first seen Nov 8, 2026
- darkweb19/skillstheir own version, first seen Nov 8, 2026
- kazemcodes/skill-treetheir own version, first seen Nov 8, 2026
Used with
Projects with it also use git-doc-updater, git-precommit 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.
u9401066Skillgit-doc-updater
Auto-check and update key documentation before Git commits to keep docs in sync with code. Triggers: docs, 文檔, 更新文檔, sync docs, release, 發布.
Also in 5 projects that use this
u9401066Skillgit-precommit
Orchestrate pre-commit workflow including Memory Bank sync, README/CHANGELOG/ROADMAP updates. Triggers: GIT, gc, push, commit, 提交, 準備 commit, 要提交了, git commit, pre-commit, 推送.
Also in 5 projects that use this
JeffallanSkillshopify-expert
Builds and debugs Shopify themes (.liquid files, theme.json, sections), develops custom Shopify apps (shopify.app.toml, OAuth, webhooks), and implements Storefront API integrations for headless storefronts. Use when building or customizing Shopify themes, creating Hydrogen or custom React storefronts, developing Shopify apps, implementing checkout UI extensions or Shopify Functions, optimizing performance, or integrating third-party services. Invoke for Liquid templating, Storefront API, app development, checkout customization, Shopify Plus features, App Bridge, Polaris, or Shopify CLI workflows.
Also in 5 projects that use this
JeffallanSkillspec-miner
Reverse-engineering specialist that extracts specifications from existing codebases. Use when working with legacy or undocumented systems, inherited projects, or old codebases with no documentation. Invoke to map code dependencies, generate API documentation from source, identify undocumented business logic, figure out what code does, or create architecture documentation from implementation. Trigger phrases: reverse engineer, old codebase, no docs, no documentation, figure out how this works, inherited project, legacy analysis, code archaeology, undocumented features.
Also in 6 projects that use this
- +4 more from u9401066
- +4 more from Jeffallan
Other versions182 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 182
- google-gemini/gemini-cli★ 109ksame name
- NVIDIA/SkillSpector★ 25ksame name
- Jeffallan/claude-skills★ 12ksame name
- jewbetcha/openflight★ 1.2ksame name
- web-infra-dev/rsdoctor★ 1.2ksame name
- zhnnky329/MathModeling-skills★ 1.1ksame name
- MageByte-Zero/spec-superflow★ 875same name
- waybarrios/opencode-power-pack★ 547same name
- Karanjot786/TermUI★ 238same name
- elithrar/dotfiles★ 222same name
172 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 repository4 other files it gives its agents.
- brand-voiceSkill
- csv-cleanerSkill
- decision-docSkill
- one-pagerSkill
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 3 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 155 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 40
Do assertions check behavior, not implementation details?
- Very specific: line 77
If a stage has zero findings, write No findings.
- Distinctive: line 100
Run all five stages even if one finds nothing.
- Distinctive: line 101
Do not invent issues to look thorough.
“Common” is measured: kept by at least 6 in 10 of the 155 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 Reviewer
Five-stage pipeline. Each stage runs independently with its own checklist. Findings get severity tags so the reviewer can decide what blocks merge.
Severity scale
blocker: must fix before mergemajor: fix before next releaseminor: improve when convenientnit: style preference, take or leave
Stages
Stage 1: Architecture
- Are module boundaries respected?
- Is coupling between components appropriate?
- Are there leaky abstractions?
- Does this change introduce circular dependencies?
- Is the layer of abstraction consistent within each function?
Stage 2: Quality
- Are names clear and specific (not "data", "info", "manager")?
- Is there duplication that should be extracted?
- Is there complexity that should be inlined (KISS)?
- Dead code, unreachable branches, commented-out code?
- Inconsistent error handling within the same function?
- Magic numbers or strings that should be named constants?
Stage 3: Tests
- Are new code paths covered?
- Are edge cases tested (empty, null, boundary, max)?
- Do assertions check behavior, not implementation details?
- Are tests isolated (no shared mutable state)?
- Are test names descriptive of what they verify?
- Any tests that could pass against incorrect code?
Stage 4: Performance
- Algorithmic complexity reasonable for input size?
- N+1 queries or redundant database round trips?
- Unbounded memory growth (lists, caches, accumulators)?
- Missing caching where output is deterministic and expensive?
- Synchronous operations that could be async?
- Hot paths doing unnecessary allocations?
Stage 5: Security
- Input validation on all external inputs?
- SQL injection, command injection, path traversal vectors?
- Authentication and authorization on every endpoint?
- Secrets in code, logs, error messages, or commits?
- Cryptographic primitives used correctly (no MD5 for security, no ECB mode, proper IV handling)?
- CSRF, XSS, SSRF surface in web code?
- Race conditions on shared state?
Output format
For each stage, emit a markdown table with this shape:
## Stage 1: Architecture
| Severity | File:Line | Finding | Fix |
|---|---|---|---|
| <tag> | <ref> | <one line> | <one line> |Repeat for stages 2, 3, 4, and 5.
If a stage has zero findings, write No findings. under the stage heading.
End with a summary block:
## Summary
- Blockers: <count>
- Majors: <count>
- Minors: <count>
- Nits: <count>
## Verdict
<APPROVE | REQUEST_CHANGES | COMMENT>Verdict rules:
APPROVEonly if zero blockers and zero majorsREQUEST_CHANGESif any blocker or three or more majorsCOMMENTotherwise
Hard rules
- Run all five stages even if one finds nothing.
- Do not invent issues to look thorough. If the code is clean, say so.
- For diffs over 500 lines, ask the user to split before reviewing.
- Quote exact code in findings. Vague references like "around the validation logic" are not actionable.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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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.

