code-review
Skill1 starsChanged 14 days agoApache-2.0View on GitHub
Review PRs, branches, local changes, snapshots, or codebases for concrete, material defects; report actionable findings without editing code.
194 other projects use a version of this.
Adopted by
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 versions194 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 194
- 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
- shareAI-lab/analysis_claude_code★ 80ksame name
- shareAI-lab/learn-claude-code★ 80ksame name
- vectorize-io/hindsight★ 60ksame name
184 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 repository19 other files it gives its agents.
- AGENTS.mdAGENTS.md
- ai-chatSkill
- argocdSkill
- commitSkill
- dependency-trackSkill
- financeSkill
- handoffSkill
- java-21-to-25-migrationSkill
- jenkinsSkill
- jiraSkill
- perplexitySkill
- pr-readySkill
- researchSkill
- shipSkill
- skanetrafikenSkill
- sonarqubeSkill
- tddSkill
- to-tasksSkill
- verifySkill
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 184 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 12
Do not edit project files, commit, push, or post review comments.
- Distinctive: line 63
Follow with one short paragraph stating the triggering conditions, incorrect behavior, and material consequence.
- Very specific: line 65
If nothing qualifies, say No findings.
- Distinctive: line 69
Before delivering, verify each citation against the reviewed snapshot, confirm that each finding survives the selection filter, and …
“Common” is measured: kept by at least 6 in 10 of the 184 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
1. Establish scope and instructions
Review the requested scope for concrete defects. Investigate broadly within that scope, then filter the final report to findings with material consequences.
Do not edit project files, commit, push, or post review comments. Read the applicable AGENTS.md and other repository instructions.
Honor the user's target, refs, paths, concerns, and exclusions. Infer routine details from context; ask only when ambiguity would materially change the review.
Resolve the comparison according to the requested review:
- PR or branch-change review: resolve the actual base and target and inspect
git diff <base>...<target>, unless another comparison was requested. - Explicit snapshot comparison: use
git diff <base> <target>. - Local changes: inspect the requested staged, unstaged, and untracked content. Preserve the distinction between the index and working tree.
- Codebase or directory audit: inspect the requested code as it exists. Existing defects are eligible; no PR or diff is required.
Preserve explicitly requested refs. Do not silently substitute an upstream or include unrelated working-tree edits in a committed review. Read supporting code from the revision or snapshot under review. Report unavailable targets rather than inventing a comparison.
Done when: the reviewed snapshot or local-change layers, comparison, boundaries, and applicable instructions are established, or the target is confirmed unavailable.
2. Investigate the complete scope
Cover the complete requested diff or audit scope, including relevant callers, contracts, configuration, and tests. Continue after finding the first issue. Read dependencies outside a path restriction when needed to assess in-scope behavior; keep findings within the requested scope.
For codebase audits, map the main subsystems and use available subagents to investigate distinct areas in parallel. Give them bounded assignments, the same review criteria, and a shared understanding of the reviewed snapshot. The lead reviewer checks interactions between areas, evaluates candidate findings against the code, and consolidates the report. If subagents are unavailable, proceed directly. For change reviews, delegate only substantial independent work.
Establish each candidate's trigger, failure path, and practical consequence. Check for existing safeguards or intentional requirements that invalidate it. Use targeted tests or reproductions when they resolve uncertainty; a clear code path can establish a defect without runtime reproduction. Keep any reproductions isolated from project files and avoid tests with destructive or external side effects.
Done when: the requested scope and relevant interactions have been inspected, each candidate has supporting evidence, and any coverage limits are recorded.
3. Select findings
Apply this filter after investigating, not as a reason to stop exploring.
Report a finding when it is discrete, actionable, supported by the code, and materially affects correctness, security, reliability, data integrity, performance, or compatibility. Explain the concrete consequence that makes it worth fixing.
For change reviews, establish that the reviewed change introduced or exposed the failure. Do not report unrelated pre-existing defects.
Exclude style nits, minor inconveniences, speculative concerns, and refactoring or maintainability preferences without a demonstrated material consequence. Missing tests alone are not a finding. An intended change is not itself a bug, but unintended breakage caused by it still qualifies.
Report all qualifying findings, with no quota. Merge duplicate reports of the same root cause. Omit low-impact findings rather than promoting their severity.
Assign priority from realistic impact, reachability, affected scope, and workarounds:
- P0: A demonstrably reachable critical defect, such as irreversible data loss, severe security compromise, or widespread outage. Block release.
- P1: A serious defect causing substantial harm or breaking important functionality. Fix before merge or deployment unless explicitly accepted.
- P2: A material defect with limited or recoverable impact that warrants a dedicated fix. Small improvements and low-impact issues do not qualify.
Rare failures can still be severe. Severity does not compensate for weak evidence.
Done when: every retained finding meets the evidence and materiality criteria, priorities reflect realistic consequences, and duplicate root causes are merged.
4. Report and verify
Present findings first, ordered by severity. Use one entry per root cause:
[P1] Correct the concrete failure: path/to/file.ext:line
Follow with one short paragraph stating the triggering conditions, incorrect behavior, and material consequence. Cite the smallest useful location; for a change review, anchor it in the reviewed diff and explain any downstream impact. For an audit, cite the responsible code.
If nothing qualifies, say No findings. If the target could not be inspected, say the review is blocked instead.
Finish with a brief scope and verification note, including material coverage limits or checks that could not run. Do not imply unperformed checks passed or that partial inspection established the whole codebase is safe.
Before delivering, verify each citation against the reviewed snapshot, confirm that each finding survives the selection filter, and distinguish performed checks from unperformed ones.
Done when: the report contains all qualifying findings or the required no-findings/blocked outcome, accurate citations, and an honest scope and verification note.
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.

