Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Use when building e2e tests with Playwright, under deadline pressure to ship incomplete coverage, or struggling with wait strategies and mock-vs-reality tradeoffs - provides patterns for edge case coverage, deterministic waits, and strategic mocking decisions
This skill should be used when the user asks to "review PR and save results", "run PR review with documentation", "create PR review document", "review and document PR", "save PR review to docs", "document PR review", or mentions reviewing a PR with the intention of saving the review results. Executes comprehensive PR review using pr-review-toolkit with opus model and posts results as a PR comment.
Initialize a Playwright E2E framework in a brand-new project. Installs Playwright, then scaffolds config, directory structure, multi-environment env files, auth setup, fixtures skeleton, sample smoke tests, npm scripts, and tooling.
Design and implement Python tests with pytest, fixtures, mocking, parametrization, and regression coverage. Use when writing tests, setting up a test suite, debugging failing tests, choosing test scope, mocking external dependencies, or deciding what coverage is meaningful.
Provides repository design rules and anti-patterns in DDD. Checks naming conventions based on aggregates, method design based on CQS (Command Query Separation), and input/output type constraints. Use when detecting repository design problems during code review, new implementation or refactoring. Target languages: language-agnostic (Java, Kotlin, Scala, TypeScript, Go, Rust, Python and all others). Triggers: starts on repository design related requests such as "review the repository design", "the Repository name is wrong", "the return value of findById", "the repository returns DTOs", "I created a repository based on a table name", "repositories per aggregate", "repository anti-patterns", "repository CQS".
Gate 0 of the development cycle. Executes code implementation using the appropriate specialized backend agent based on task content and project language. Handles TDD, coverage, docker-compose/local runtime, basic health/observability verification, and delivery verification inside one backend-owned gate.
Troubleshoot Agent Jobs running in ROCK sandboxes with rockcli (rc) and produce diagnostic reports. Use when the user provides a sandbox or experiment ID, wants to check a job's status, logs, reward and execution trace, or needs to diagnose job failures, zero rewards, agent crashes and similar issues.
Strict discipline for runtime verification in PDCA workflows: the AI performs verification itself in an environment — real preferred over simulated, subject to determinism and safety — and never defaults to a human checklist. Attempt first, resolve a pluggable provider (project script/skill → shared capability skill), and hand off to a human only at a true hard boundary with a stated reason. Use at any workflow verify/check stage (bug fix or feature) to decide WHERE verification runs, WHO runs it, and WHETHER it is sufficient. Referenced by PDCA hosts via frontmatter dependencies. Triggers — 「环境验证」「真实环境验证」「真机验证」「真机测试」「模拟环境验证」「AI 验证」「验证分层」「验证充分性」 / runtime verification, environment verification, real vs simulated. When a check opens a UI, cite a saved non-empty screenshot; non-UI checks do not. Do NOT alias completion-evidence-discipline (evidence freshness) or runtime-evidence-debug (debug instrumentation).
Static application security testing (SAST) using Semgrep for vulnerability detection, security code review, and secure coding guidance with OWASP and CWE framework mapping. Use when: (1) scanning code for security vulnerabilities across multiple languages, (2) performing security code reviews with pattern-based detection, (3) integrating SAST checks into CI/CD pipelines, (4) providing remediation guidance with OWASP Top 10 and CWE mappings, (5) creating custom security rules for organization-specific patterns, or (6) analyzing dependencies for known vulnerabilities
Identifies silent failures, inadequate error handling, and inappropriate fallback behavior in code. Zero tolerance for errors that occur without proper logging and user feedback. Triggers: When reviewing error handling, checking for silent failures, analyzing catch blocks. Examples: - "Review the error handling" -> audits all error handling in recent changes - "Check for silent failures" -> hunts for swallowed errors and empty catch blocks - "Analyze catch blocks in this PR" -> reviews every try-catch for adequacy - "Are there any hidden failures?" -> finds errors that get silently ignored
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy
Run evaluation pipelines on Claude Code skills to test triggering accuracy, workflow correctness, and output quality. Spawns executor, grader, comparator, and analyzer sub-agents for parallel evaluation. Generates eval_metadata.json, grading.json, and feedback reports. Use when user says "eval skill", "test skill", "run evals", "evaluate skill", "skill evals", "test skill quality", "run skill tests", or "skill evaluation".
Skill lifecycle management system that uses LLMs to intelligently analyze skill types, evaluate quality, and audit security. Supports search across three data sources: GitHub, the skills.sh marketplace, and Skills CLI (npx skills). Use when the user needs to: (1) discover and evaluate new skills, (2) audit the security of existing skills, (3) adapt skills to the local environment, (4) test the effects of skill merging, or (5) collect feedback on skill usage.
Use when Laravel/Pest test suites are slow, CI duration is growing, individual tests take seconds, or the user asks to speed up, optimize, or profile tests. Covers factories, fakes, config caching, XDebug/pcov, BCRYPT_ROUNDS, LazilyRefreshDatabase, and stray HTTP requests.
Guide for writing expressive, behavior-focused tests following Vladimir Khorikov's testing principles. Apply when writing, reviewing, or renaming any test (unit, integration, e2e) in any programming language. Triggers: writing tests, creating test files, adding test cases, reviewing test names, 'test naming', 'rename tests', 'Khorikov', or any test creation task. Covers: naming conventions (plain English over rigid policies), what to test (behavior not implementation), testing styles (output > state > communication), and pragmatic test investment.
Python-specific extension to myai's `high-level-testing-strategy`, `test-driven-development`, and `manual-testing`. Load after those myai skills when writing Python tests, adding fixtures, or setting up pytest. Python testing with pytest: fixtures, CLI/e2e tests, mock servers, containerized testing, pytest-qt.
Invoked by /tidy-code. Reviews source code for structural quality violations (hidden dependencies, god functions, silent failures, deep nesting) in any language. Produces a read-only findings report with concrete refactoring suggestions. Skips dot-directories (`.git`, `.venv`, IDE/agent scaffolding) and build/vendor output (`node_modules`, `vendor`, `dist`, `build`, `coverage`, `target`). Does not auto-activate on natural-language requests.
Use when contributing to TYPO3 Core — Forge issues, Gerrit patches, cherry-picks, CI debugging — or when working on **git.typo3.org**, the t3o site GitLab (`services/t3o-sites/**`: extensions.typo3.org/ter, common/t3olayout, anubis): merge requests, issues, work items, labels, pipelines. Core `main` is v15-in-progress (PHP 8.4/8.5); v14.3 LTS fixes land on branch `14.3` via cherry-pick. Triggers on: forge.typo3.org, review.typo3.org, git.typo3.org, t3o-sites, core patch, Gerrit review, TYPO3 Core contribution, TER website, t3olayout.
Use before claiming work is complete, fixed, or passing, before committing or creating PRs. Requires inspecting verification evidence that matches each claim before making any success claims, whether freshly run or still-applicable verified evidence whose relevant code, tests, inputs, and environment match. Evidence before assertions, always.
Author and review Cloudflare Workers code against production best practices. Use when writing a Worker, reviewing Worker code, configuring wrangler.jsonc, or checking for Workers anti-patterns — streaming, floating promises, global state, secrets, bindings, observability.
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to “open a website”, “fill out a form”, “click a button”, “take a screenshot”, “scrape data from a page”, “test this web app”, “login to a site”, “automate browser actions”, or any task requiring programmatic web interaction.
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
Dynamically validate that the implemented codebase matches architectural decisions documented in plan files. Use when validating implementation matches planning documents, checking for architecture drift, or preparing for architecture reviews.
Analyze assertion quality, depth, variety, and false confidence in existing tests. ALWAYS USE when asked about weak, shallow, trivial, always-true, self-referential, assertion-free, presence/truthiness-only, or insufficiently diverse assertions, including MSTest, Jest, pytest, and Go. DO NOT USE for direct fixes: writing-mstest-tests owns supplied MSTest assertions; code-testing owns new cases. Use test-gap-analysis when asked whether tests would catch a production change, and test-anti-patterns for general severity-ranked audits
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.