2 028 code review skills from 387 authors. They read someone else's code and work through changes before those land. Half of them fit into 1 567 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.
2 028 unique 387 authors 1 258 updated this month 273 from vendors
Use when reviewing, improving, or refactoring Java object-oriented design, including applying SOLID, DRY, or YAGNI; improving classes and interfaces; correcting encapsulation, inheritance, or polymorphism; resolving God Class, Feature Envy, or Data Clumps; and improving object creation, methods, or exception contracts. Triggers include review Java OOD, refactor Java OOD, improve Java OOD, fix OOP misuse, and identify Java code smells. Part of Plinth Toolkit
Use when you need to refactor Java code to adopt modern Java features (Java 8+) — including migrating anonymous classes to lambdas, replacing Iterator loops with Stream API, adopting Optional for null safety, switching from legacy Date/Calendar to java.time, using collection factory methods, applying text blocks, var inference, or leveraging Java 25 features like flexible constructor bodies and module import declarations. This should trigger for requests such as Review Java code for modern Java development; Apply best practices for modern Java development in Java code; Modernize Java code with records pattern matching or switch expressions; Replace legacy idioms with modern Java features; Adopt Java 8+ language features safely. Part of Plinth Toolkit
Use when you need to apply data-oriented programming best practices in Java — including separating code (behavior) from data structures using records, designing immutable data with pure transformation functions, keeping data flat and denormalized with ID-based references, starting with generic data structures converting to specific types when needed, ensuring data integrity through pure validation functions, and creating flexible generic data access layers. This should trigger for requests such as Improve the code with Data-Oriented Programming; Apply Data-Oriented Programming; Refactor the code with Data-Oriented Programming; Model Java data with records and pure functions; Separate Java behavior from immutable data structures; Validate data integrity with pure Java functions. Part of Plinth Toolkit
Use when you need to apply functional exception handling best practices in Java — including replacing exception overuse with Optional and VAVR Either types, designing error type hierarchies using sealed classes and enums, implementing monadic error composition pipelines, establishing functional control flow patterns, and reserving exceptions only for truly exceptional system-level failures. This should trigger for requests such as Improve the code with Functional Exception Handling; Apply Functional Exception Handling; Refactor the code with Functional Exception Handling; Model Java errors with Result or Either types; Replace exception-heavy flows with functional error handling. Part of Plinth Toolkit
Use when you need to refactor Java code for high performance — including memory/allocation reduction, CPU hot-path optimization, and syntax/API/control-flow improvements. This should trigger for requests such as Review Java code for high performance; Optimize Java hot path; Reduce Java allocations; Improve Java latency/throughput. Part of Plinth Toolkit
Use when you need to set up Java application profiling to detect and measure performance issues — including trusted preinstalled async-profiler v4.x setup, problem-driven profiling (CPU, memory, threading, GC, I/O), interactive profiling scripts, JFR integration with Java 25 (JEP 518, JEP 520), or collecting profiling data with flamegraphs and JFR recordings. This should trigger for requests such as Improve the code with profiling; Apply Profiling; Refactor the code with profiling; Add profiling support; Collect JFR or async-profiler data for Java performance. Part of Plinth Toolkit
Use when you need to set up JMeter performance testing for a Java project — including creating the run-jmeter.sh script from the exact template, configuring load tests with loops, threads, and ramp-up, or running performance tests from the project root with custom or default settings. This should trigger for requests such as Improve the code with JMeter performance testing; Apply JMeter performance testing; Refactor the code with JMeter performance testing; Add JMeter support; Create a JMeter test plan for a Java service. Part of Plinth Toolkit
Use when you need to refactor Java code based on trusted profiling analysis findings — including reviewing repository-owned or maintainer-sanitized docs/profiling-problem-analysis and docs/profiling-solutions files, identifying specific performance bottlenecks, and implementing targeted code changes to address CPU, memory, or threading issues. This should trigger for requests such as Refactor the code with profiling; Apply profiling; Optimize hot path; Reduce allocations found in profiling; Fix CPU bottlenecks from profiling analysis. Part of Plinth Toolkit
Use when you need to generate or improve Java project documentation — including README.md files, package-info.java files, and Javadoc enhancements — through a modular, step-based interactive process that adapts to your specific documentation needs. This should trigger for requests such as Improve the code with documentation; Apply documentation; Refactor the code with documentation; Generate README or developer documentation for a Java project; Document Java APIs architecture or project workflows. Part of Plinth Toolkit
Use when you need to implement or improve distributed tracing with OpenTelemetry in Java — including trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors. This should trigger for requests such as Improve tracing; Apply OpenTelemetry tracing; Add distributed tracing; Refactor tracing instrumentation; Instrument Java services with OpenTelemetry spans. Part of Plinth Toolkit
> Reviews a diff or a whole repository for over-engineering only, and returns a delete list rather than prose. Finds reinvented standard library calls, dependencies the platform already covers, abstractions with one implementation, wrappers that only forward, configuration nothing reads and dead flexibility. Use whenever the user says "review for over-engineering", "what can we delete", "is this over-engineered", "find the bloat", "audit this repo", or invokes /ratchet-review. Pair it with a normal correctness review, it deliberately does not look for bugs.
>- Use the frozen-class/visitor pattern for discriminated unions that have multiple dispatch sites. Use when creating a new set of variants (commands, IR nodes, factory calls) that will be switched over in 2+ places, or when refactoring an existing union type that has grown multiple switch sites.
Creates a GitHub PR with a Linear-ticket-prefixed title and a decision-led, narrative description for prisma-next. Use when the user wants to create a pull request, open a PR, or submit changes for review.
Open a high-quality external contributor PR against prisma-next. Use when the user is an outside contributor (not a Prisma maintainer) and wants to submit a change as a pull request from a fork. Encodes the contribution flow from CONTRIBUTING.md so the resulting PR passes review on the first round.
Orchestrates a GitHub PR review loop by delegating triage and implementation to dedicated sub-agents, then repeating until actionable review items are cleared. Use when the user says “address PR review”, “triage review comments”, or “iterate until review is clean”.
Fetches canonical PR review state and renders derived state artifacts. Use when the user wants the state acquisition phase only (fetch, render, summarize) for a review-framework PR.
Compile or lint a persistent Mayor-style goal prompt that ratchets a bead graph through bounded RPI experiments toward one larger outcome. Triggers: "craft a goal prompt", "mayor goal", "goal-runner prompt", "lint this goal", "is this goal safe". (Shaping one experiment''s intent routes to plan.)
Execute one behavior-preserving structural transformation and report evidence. Triggers: "refactor this", "simplify without changing behavior".
Re-detect this project's tech stack from package.json / requirements.txt / pyproject.toml / go.mod / Cargo.toml and diff it against the Tech Stack section of every CLAUDE.md. Read-only — returns added / removed / renamed dependencies, never edits.
Behavioral guardrails for LLM-assisted coding. Use when writing, reviewing, or refactoring code in any project to avoid overcomplication, keep changes surgical, surface assumptions early, and execute against verifiable success criteria.
Run Python (ruff) and JavaScript (Biome) linting.
Multi-language code quality gate with auto-detection and linters.
PHP development: code quality, PSR standards, testing with PHPUnit.
Gold-standard SAP CC Go code review: 10 parallel domain specialists.
| Generate rich self-contained HTML artifacts instead of markdown. Auto-detects artifact shape (spec, code-review, prototype, report, editor, data-viz, diagram, deck) and loads shape-specific patterns. Bundles Birchline design system with 4 theme presets. Use for "make HTML", "as HTML", "HTML artifact", or auto-injected by router when output benefits from rich visualization.
| cleanup, and PR mining. Use when user wants to commit changes, get a second-opinion code review from Codex, push changes, create a PR, check PR status, fix review comments, clean up branches after merge, or mine tribal knowledge from PR reviews. Use for "commit my changes", "codex review", "push my changes", "create a PR", "pr status", "fix PR comments", "clean up branches", "mine PRs", or "address feedback".
Parallel 3-reviewer code review: Security, Business-Logic, Architecture.
4-phase code review: UNDERSTAND, VERIFY, ASSESS risks, DOCUMENT findings.
Structured multi-phase workflows: review, debug, refactor (tidy, clean up, untangle messy code without behaviour change), deploy, create, research.
Use when apple-release-engineer is about to build the signed distributable (TestFlight build / notarized DMG / internal package) from merged-to-main code (after apple code review + SIT Audit pass and merge, before apple-qa-engineer runs E2E/UAT). Provides the applicability gate, pre-flight checks, lane execution per channel, notarization, real-output smoke test, hand-off, and the release-report skeleton. Pairs with deployment.md §7 "Apple 发布" contract and slash /agf-apple-release.
Use when uiux-designer is about to produce a design spec (spec.md) or static HTML prototype, or frontend-dev is about to build UI from a design. Provides the anti-AI-slop design discipline layer — Brief Inference (Design Read), three aesthetic dials tuned for product UI, AI Tells blacklist with overrides, mechanically-checkable Pre-Flight. Sits above the token layer (DESIGN.md) and mechanical review (code-reviewer); does not redeclare tokens or guide non-shadcn design systems. Inspired by taste-skill, cropped for AGF product UI per ADR-013.
> Update ElevenLabs agent skills from a merged weekly changelog in elevenlabs-dx, then open a pull request in elevenlabs/skills. Trigger after a changelog merges to main on elevenlabs-dx, or when asked to update skills from changelog YYYY-MM-DD.
Run project commands with just. Check for justfile in project root, list available tasks, execute common operations like test, build, lint. Triggers on: run tests, build project, list tasks, check available commands, run script, project commands.
Search code by AST structure using ast-grep. Find semantic patterns like function calls, imports, class definitions instead of text patterns. Triggers on: find all calls to X, search for pattern, refactor usages, find where function is used, structural search, ast-grep, sg.
Work on the core package (types, validation, normalization, diff). Use when modifying DSL processing logic, adding new node/edge properties, changing validation rules, or updating the diff algorithm.
Generate descriptive commit messages by analyzing git diffs. Use when the user asks for help writing commit messages or reviewing staged changes.
Create commit messages following Sentry conventions. Use when committing code changes, writing commit messages, or formatting git history. Follows conventional commits with Sentry-specific issue references.
Create pull requests following Sentry conventions. Use when opening PRs, writing PR descriptions, or preparing changes for review. Follows Sentry's code review guidelines.
Turn a GitHub pull request (a PR URL, owner/repo#N, or 'this PR' in a checked-out repo) into a code-change explainer video — changelog, feature reveal, fix, or refactor walkthrough built from the diff, commits, and files: the input is a code change, not a website. Not a product promo (/product-launch-video) or a no-PR topic explainer (/faceless-explainer). Unclear → /hyperframes.
Review Dify frontend code for correctness, accessibility, component design, dify-ui usage, data/query boundaries, performance, and tests. Trigger for `.tsx`, `.ts`, `.js`, UI, React, Next.js, pending-change, or focused frontend review requests.
Set up Husky pre-commit hooks with lint-staged (Prettier), type checking, and tests in the current repo. Use when user wants to add pre-commit hooks, set up Husky, configure lint-staged, or add commit-time formatting/typechecking/testing.
Review Clojure and ClojureScript code changes for compliance with Metabase coding standards, style violations, and code quality issues. Use when reviewing pull requests or diffs containing Clojure/ClojureScript code.
Guide Clojure and ClojureScript development using REPL-driven workflow, coding conventions, and best practices. Use when writing, developing, or refactoring Clojure/ClojureScript code.
Review TypeScript and JavaScript code changes for compliance with Metabase coding standards, style violations, and code quality issues. Use when reviewing pull requests or diffs containing TypeScript/JavaScript code.
> Review pull requests for the MiniMax Skills repository. Use when reviewing PRs, validating new skill submissions, or checking existing skills for compliance. Run the validation script first for hard checks, then apply quality guidelines
Comprehensive GitHub code review with AI-powered swarm coordination
| Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
Reviews code for security vulnerabilities, performance issues, and best practices. Use when reviewing code, performing security audits, checking for code quality, reviewing pull requests.