Code Review Claude Skills
2 032 code review skills from 387 authors. They read someone else's code and work through changes before those land. Half of them fit into 1 560 tokens or less — that is what one costs your context window when the agent loads it. 210 ship runnable scripts rather than instructions alone. 3 of them cannot work without an MCP server, most often rube. We also found 250 copies of these same skills sitting in other people's repositories — counted once here, not 250 times.
2 032 unique 387 authors 1 262 updated this month 280 from vendors
複数のAI（自分/Codex/Grok/Cursor等）でPRをレビューするスキル。GitHub PRの差分を取得し、各AIの視点を統合したレビューを提供する。GitHubへの投稿はユーザーの明示OK後に行う。「PRレビューして」「コードレビューして」「PR#123をチェック」で使用。
Use when reviewing Go code or checking code against community style standards. Also use proactively before submitting a Go PR or when reviewing any Go code changes, even if the user doesn't explicitly request a style review. Does not cover language-specific syntax — delegates to specialized skills.
Use when organizing functions within a Go file, formatting function signatures, designing return values, or following Printf-style naming conventions. Also use when a user is adding or refactoring any Go function, even if they don't mention function design or signature formatting. Does not cover functional options constructors (see go-functional-options).
Use when setting up linting for a Go project, configuring golangci-lint, or adding Go checks to a CI/CD pipeline. Also use when starting a new Go project and deciding which linters to enable, even if the user only asks about "code quality" or "static analysis" without mentioning specific linter names. Does not cover code review process (see go-code-review).
List APIView revisions created or actually opened/viewed in a date range, broken out by language and type. Use for: created revisions, opened revisions, viewed revisions, revision counts, how many revisions, revisions for March, revision breakdown, revision types, automatic vs manual revisions, PR revisions, pull request revisions, page views, actually viewed, who opened reviews, opened revision breakdown.
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_typespec_customized_code_update.
Create, get, update, abandon, and link SDK PRs to release plan work items for Azure SDK releases. **UTILITY SKILL**. USE FOR: "create release plan", "get release plan", "update release plan", "update API spec in release plan", "update SDK details in release plan", "abandon release plan", "link SDK PR to plan", "namespace approval", "check release plan status". DO NOT USE FOR: SDK code generation, pipeline troubleshooting, API review feedback. INVOKES: azure-sdk-mcp:azsdk_create_release_plan, azure-sdk-mcp:azsdk_get_release_plan, azure-sdk-mcp:azsdk_get_release_plan_for_spec_pr, azure-sdk-mcp:azsdk_update_release_plan, azure-sdk-mcp:azsdk_update_api_spec_pull_request_in_release_plan, azure-sdk-mcp:azsdk_update_sdk_details_in_release_plan, azure-sdk-mcp:azsdk_abandon_release_plan, azure-sdk-mcp:azsdk_link_sdk_pull_request_to_release_plan, azure-sdk-mcp:azsdk_link_namespace_approval_issue.
Review code for bugs, security vulnerabilities, performance issues, and maintainability. Trigger with "review this code", "check this PR", "look at this diff", "is this code safe?", or when the user shares code and asks for feedback.
> Ultra-compressed commit message generator. Cuts noise from commit messages while preserving intent and reasoning. Conventional Commits format. Subject ≤50 chars, body only when "why" isn't obvious. Use when user says "write a commit", "commit message", "generate commit", "/commit", or invokes /caveman-commit. Auto-triggers when staging changes.
Apply Google Style documentation standards to Python, Go, and Terraform code. Use when writing or reviewing code that needs docstrings/comments, when asked to "document this code", "add docstrings", "follow Google Style", or when improving code documentation quality. Supports Python docstrings, Go comments, and Terraform variable/output descriptions. Enforces consistent, professional documentation standards.
> Ultra-compressed code review comments. Cuts noise from PR feedback while preserving says "review this PR", "code review", "review the diff", "/review", or invokes /caveman-review. Auto-triggers when reviewing pull requests.
Differential abundance testing for microbiome data using compositionally-aware methods like ALDEx2, ANCOM-BC2, and MaAsLin2. Use when identifying taxa that differ between experimental groups while accounting for the compositional nature of microbiome data.
Use when designing or refactoring a model-native engineering workflow with bounded autonomy, probes, custom evaluation, durable state, and verified write-back.
Use when an Obsidian wiki needs a reproducible health audit for structure, provenance, links, understanding, lifecycle, and promotion readiness.
Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.
Rich downstream visualisation and reporting for bulk RNA-seq differential expression and scRNA marker/contrast outputs.
Iterative code refinement through plan → code → evaluate → refine cycles. Runs lint checks (ruff), tests (pytest), and structured self-evaluation each cycle, then diagnoses failures and refines. Decomposes complex tasks into sequential phases, iterates up to 3 times per phase (10 total). Use when: the main agent delegates a code task with 'MODE: MORE_EFFORT', the user selects 'More Effort' code generation mode, or the task explicitly requests iterative refinement for higher code quality. Do NOT use for single-pass code generation (Lite mode), experiment pipeline orchestration (use experiment-pipeline), or diagnosing a specific experiment failure (use experiment-craft).
Enforce a hard-cut cleanup policy: keep one canonical implementation and delete compatibility, migration, fallback, adapter, coercion, and dual-shape code. Use for pre-release or internal-draft refactors where the goal is one final shape, especially when changing schemas, contracts, persisted state, routing, configuration, feature flags, enum/value sets, or architecture.
Run local Go health checks (tests, coverage, lint) in Go repositories that contain go.mod/go.sum. Use when the user asks to run or interpret local Go test/coverage/lint workflows using tools like lazygotest, gocovsh, tparse, and golangci-lint. Do not use for Rust or non-Go projects.
Run shellcheck on shell scripts after editing scripts or when debugging shell errors. Use for linting scripts in a repo (especially scripts/), catching issues like set -u with unset vars, bad subshell usage, or quoting mistakes.
Delegate tasks to Google's Antigravity CLI (agy) for prototyping, debugging, code review, and research. Supports multi-turn sessions via SESSION_ID. Successor to the retired Gemini CLI.
Delegate tasks to Grok CLI (xAI) for prototyping, debugging, code review, research, and cross-model second opinions. Supports multi-turn sessions via SESSION_ID.
Use when the user is addressing pull request review comments locally and asks for help evaluating, implementing, or drafting responses to reviewer feedback - requires technical rigor and verification, not performative agreement or blind implementation
Use this skill when categorizing code review findings into severity levels. Apply when determining which emoji and label to use for PR comments, deciding if an issue should be flagged at all, or classifying findings as CRITICAL, IMPORTANT, DEBT, SUGGESTED, or QUESTION.
Use this skill when posting inline comments to GitHub pull requests. Apply when formatting comments following Bitwarden engineering standards with severity emojis, clear explanations, and actionable suggestions. Use after findings are classified and ready to post. DO NOT USE when posting summary comments.
Perform a rigorous, multi-agent code review with architecture-compliance, parallel quality/security analysis, finding validation, and severity audit. Use when the user asks for a structured, deep, thorough, multi-pass, or multi-agent code review — or a review that includes architecture/pattern compliance, confidence-scored findings, or a severity audit. Use when the user asks for a code review across a commit range, time window, or N most recent commits in a locally checked-out repo.
Use this skill when posting the final summary comment after all inline comments are posted. Apply as the LAST step of code review after all findings are classified and inline comments are complete. Detects context (agent mode sticky comment, GitHub Actions MCP tool, or local file) and routes output accordingly.
Use this skill to validate findings during a code review. For each finding, run the rejection criteria and verification checks. If a finding fails any check, drop it.
Git commit conventions and workflow for Bitwarden repositories. Use when committing code, writing commit messages, or preparing changes for commit. Triggered by "commit", "git commit", "commit message", "prepare commit", "stage changes".
Open a pull request from a branch in a Bitwarden repository — pick the conventional commit type prefix that drives the t: label, fill in the repo's PR template, choose an ai-review label, and confirm a full submission preview before running gh pr create.
Conventional commit type keywords for PR titles and commit messages. Use when determining the change type for commits or PRs. Triggered by "what type", "label", "change type", "conventional commit", "t: label".
>- Reference for all Bitwarden workflow linter (bwwl) rules. Covers all 10 linter rules split into permissions_exist, pinned_job_runner, step_pinned, underscore_outputs, job_environment_prefix, check_pr_target) and judgment rules requiring user input (name_exists, step_approved, run_actionlint). Use the workflow-audit skill to run the linter and report findings, and the workflow-fix skill to apply fixes. <example> </example> <example> </example>
> Run the Bitwarden workflow linter (bwwl) against one or more repos and report findings. Strictly read-only — does not modify any files. Categorizes findings as mechanical or judgment using the bitwarden-workflow-linter-rules skill. Supports single repo, multiple repos, or single file/directory scope. <example> </example> <example> </example>
> Apply fixes for workflow linter findings identified by the workflow-audit skill. Applies mechanical fixes automatically, pauses for judgment calls, verifies with a re-lint, and creates draft PRs. Run the workflow-audit skill first to identify findings before using this skill. <example> </example> <example> </example>
Performs a security-focused code review by launching multiple specialized agents and a verification agent to ensure comprehensive coverage and accurate findings. Use this skill when the user asks for a "perform-security-review", "bitwarden-security-review", "execute a security review", "run a comprehensive security audit", "perform an end-to-end security assessment", or needs to coordinate multiple security checks across code, dependencies, secrets, and configurations. The skill manages the workflow, delegates tasks to specialized agents, and presents final findings to the user.
Analyzes git commits and changes within a timeframe or commit range, providing structured summaries for code review, retrospectives, work logs, or session documentation.
Provide structured code review guidance for catching defects and improving quality. This skill should be used when the user asks to 'review this code', 'check for issues', 'PR review', 'code quality check', or wants systematic code evaluation. Keywords: code review, PR, pull request, quality, defects, security, maintainability, performance.
Intelligently routes code reviews between Gemini CLI and Codex CLI based on tech stack, complexity, and change characteristics. Use when you want an automated code review of your current changes.
Use when refactoring a user-level or project-level CLAUDE.md for progressive disclosure
Use when refactoring a user-level or project-level AGENTS.md for progressive disclosure
Create and manage release plan work items for Azure SDK releases across languages. **UTILITY SKILL**. USE FOR: \"create release plan\", \"update release plan\", \"link SDK PR to plan\", \"namespace approval\", \"check release plan status\". DO NOT USE FOR: SDK code generation, pipeline troubleshooting, API review feedback. INVOKES: azure-sdk-mcp:azsdk_create_release_plan, azure-sdk-mcp:azsdk_get_release_plan, azure-sdk-mcp:azsdk_link_sdk_pull_request_to_release_plan.
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.
Use when the user invokes `/pr-precheck <repo> <topic>` or — implicitly, before any other skill opens a pull request — to check the target repo's merged and closed PR history for prior attempts at the same fix, so the agent doesn't open the Nth duplicate of an already-rejected approach. Wraps the `vouch pr-cache` CLI (build / check / show) and turns its verdict into a clear stop / ask / proceed signal.
Use when a proposed open-source feature must be checked across source, history, branches, issues, pull requests, roadmaps, and contributor guidance before implementation begins.
Prepare an open pull request for merge from a GitHub Copilot cloud agent. Drives Reviews, local validation, and Mergeable to a ready state. Does not merge, and cannot trigger CI.
> Assemble and extract Gaussian .gjf input file sections (directives, route, title, molecule blocks, appendices) and build single- or multi-step Link1 jobs from modular component files. USE WHEN needed for generating, refactoring, templating, or scripting Gaussian job files.