Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Explain what a Cargo run or batch actually did, after the fact — trace one run node by node, draw the graph it executed with the failing step marked, sweep a batch or play for errors grouped by root cause, and attribute credit spend down to the node and the provider. Triggers: "why did this fail", "it succeeded but the output is wrong", "half my rows are empty", "why is this column blank", "what broke in this batch", "why did that cost so much", "which node is burning credits", "it worked yesterday", "these results look wrong", "it went down the wrong path", "this step never ran", "show me what the run did". Skip when: setting up an alert for next time — use cargo-observability; just downloading the data — use cargo-analytics.
Expert code reviewer for TypeScript + React 19 applications. Use when reviewing React code, identifying anti-patterns, evaluating state management, or assessing code maintainability. Triggers: code review requests, PR reviews, React architecture evaluation, identifying code smells, TypeScript type safety checks, useEffect abuse detection, state management review.
GAN-inspired Generator-Evaluator agent harness for building high-quality applications autonomously. Based on Anthropic's March 2026 harness design paper. Use when a feature should be built autonomously through generator and evaluator iteration until it clears a quality bar.
Comprehensive guide for writing and running Terraform tests. Use when creating test files (.tftest.hcl), writing test scenarios with run blocks, validating infrastructure behavior with assertions, mocking providers and data sources, testing module outputs and resource configurations, or troubleshooting Terraform test syntax and execution.
Audit a published API reference at the endpoint level against its spec surface — every operation, parameter, response code, and error documented, request/response examples present, per-language code snippets in parity, try-it affordances working — and install the source-of-truth gates that stop reference drift (spec-driven generation, OpenAPI lint, contract tests, snippet parity in CI). Use whenever the user mentions API reference docs, OpenAPI docs completeness, undocumented endpoints or error codes, stale or drifting docs, or docs CI gates — even if they never say "reference quality". Endpoint-level reference only. Do NOT use for portal IA and onboarding — use samber/developer-platform-skills@developer-portal-design instead.
Design the operator-side review and approval process for third-party apps on a B2B SaaS marketplace: review-pipeline shape scaled to data sensitivity, permission and data-access audit checkpoints, automated pre-screening with human triage, objective re-review-on-update triggers, publish-channel hardening against supply-chain attacks, appeal paths, and a severity-tiered revocation policy. Use whenever the user mentions app review, marketplace approval criteria, third-party app permission audits, re-review after an app update, rejection appeals, or delisting and revocation—even if they never say "app review". Operator side only. Do NOT use for submitter onboarding—use samber/developer-platform-skills@partner-app-onboarding instead.
Design the test/sandbox mode of a public API platform so external integrators build and validate without touching real money, messages, or data - isolation architecture (soft test-mode toggle vs hard separate-copy sandbox), test-key prefixing, magic test values, simulated objects and personas, on-demand test events, deterministic time manipulation, reset and seeding, sandbox quotas, abuse controls, and graduation to live. Use whenever the user mentions a sandbox, test mode, test API keys, magic or dummy test values, simulated webhook events, or demo data - even if they never say "test mode". Not code-execution sandboxing or unit-test mocking. Do NOT use for partner sandbox provisioning - use samber/developer-platform-skills@partner-app-onboarding instead.
Uses Chrome DevTools via MCP for efficient debugging, troubleshooting, and browser automation. Use when debugging web pages, automating browser interactions, analyzing performance, or inspecting network requests. This skill does not apply to `--slim` mode (MCP configuration)
Use when reviewing code for anti-patterns. Keywords: anti-pattern, common mistake, pitfall, code smell, bad practice, code review, is this an anti-pattern, better way to do this, common mistake to avoid, why is this bad, idiomatic way, beginner mistake, fighting borrow checker, clone everywhere, unwrap in production, should I refactor, 反模式, 常见错误, 代码异味, 最佳实践, 地道写法
Check whether the Meticulous CLI (@alwaysmeticulous/cli) and skills are installed and up to date, and install or update them if not. Invoked at the start of every other Meticulous skill because the CLI and skills are under active development with frequent changes and improvements
Work through the results of a Meticulous test run and respond to their review state — review unreviewed diffs (and any failing non-visual checks) against the PR's intended changes, flag potential regressions; when working on the PR's code, hand items needing fixes (diffs someone rejected or commented on, and your own rejections) to the `meticulous-fix` skill; also summarize Agent swarm results. Resolves the test run from the current commit in the local repo (the default), or from an explicit test-run ID, commit SHA, or PR number. Use whenever a run's results matter — reviewing a pull/merge request, right after pushing a frontend change yourself, when its Meticulous CI check is failing or pending, or when asked to look at Meticulous results.
Run a Meticulous session simulation against a live URL and analyze the visual output—either by inspecting screenshots directly (quick-check mode) or by comparing pixel and HTML diffs against a base replay. Use when checking whether a code change has introduced visual regressions for a specific session
Iterative frontend development loop using Meticulous for per-step visual validation. Use when implementing a multi-step frontend change and wanting to catch visual regressions and unintended side effects at each step, before the final cloud test run
Grades agent skills by scoring agent conversations for efficiency, code quality, procedure compliance, and verbosity, then drafts concrete skill edits and a shareable report. Use when the user wants their agent setup graded from real conversation history, or asks which of their installed skills are actually working.
Run a Meticulous test after implementing a frontend change, then hand off to the `meticulous-review` skill to classify each visual change as intended or unintended. Upload the build once—the same build can be retriggered against different bases without rebuilding—and it works with uncommitted changes. Use when implementing a feature autonomously end to end before creating a PR
Download and use structured Meticulous session data (user flows + network mocks) for testing code changes locally. Use when you need to understand what user interactions and API calls a test covers, or when you want network mocks for writing tests.
Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.
Audit an existing or proposed public REST API surface as a checklist-driven design review: resource and URI naming, HTTP method and status-code correctness, field-naming consistency, pagination pattern choice, filtering/sorting/field-selection conventions, a consistent error envelope, and Hyrum's Law backward-compatibility risk. Bucket each finding as Must-change or Improvement against a cited rule, and cover the standing review program (audience, lifecycle triggers, reviewer authority, linting, federation). Use whenever the user mentions an API design review, REST API consistency, endpoint naming, pagination style, or API design governance—even if they never say “design review.” Do NOT use for GraphQL schemas; use samber/developer-platform-skills@public-graphql-api-design instead.
Generates comprehensive, workable unit tests for any programming language using a multi-agent pipeline. Use when asked to generate tests, write unit tests, improve test coverage, add test coverage, create test files, or test a codebase. Supports C#, TypeScript, JavaScript, Python, Go, Rust, Java, and more. Orchestrates research, planning, and implementation phases to produce tests that compile, pass, and follow project conventions.
PreToolUse fact-forcing gate that denies the first Edit/Write/Bash (including MultiEdit and NotebookEdit) attempt until the agent presents concrete facts (importers, data schemas, verbatim user instruction), then allows retry; A/B-tested at +2.25 quality points. Use when enabling or configuring the GateGuard hook, exempting paths via env vars, or handling first-touch denials.
Find and fix game performance problems methodically — measure with the engine profiler first, reason about the frame-time budget, locate the CPU-vs-GPU bottleneck, then apply the right fix: object pooling, draw-call batching, fewer allocations/GC spikes, and asset budgets. Engine-neutral method that pairs with each engine's profiler. Use when the user mentions performance, optimize, low/dropping FPS, frame drops, stutter, lag, profiler, frame budget, draw calls, batching, garbage collection/GC spikes, object pooling, or "the game runs slow".
Manage Apifox project resources via the Apifox CLI. Trigger scenarios: run API automation tests / test suites; query, create, update, or delete project resources such as APIs, environments, schemas, mocks, and branches; import and export API documentation; view test reports; manage Runner, scheduled tasks, notifications, and other CI/CD configurations. CLI output is structured JSON and often contains agentHints.nextSteps; all commands support --help.
Explains Cypress tests (E2E and component tests), and answers questions about Cypress use and behavior. Use when the user asks to explain how a test works, explain how Cypress works, review or critique a test without writing code. Apply even when the user does not say 'Cypress' (e.g. 'explain this test'). Prefer the cypress-author skill when the user wants to create, fix, or update tests.
Create a new Skill or improve an existing one. First propose a plan based on the scope of the changes and wait for confirmation, then generate or modify files and compare performance before and after using real tasks. Use when the user asks to create a Skill, turn recurring issues into a Skill, modify or fix a Skill, improve a Skill based on failure cases, or inspect or test a Skill. Prepare a GitHub repository and verify `npx skills add` only when the user explicitly asks to share or publish.
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
PluginEval quality methodology, covering dimensions, rubrics, and scoring formulas. Use this skill when understanding how plugin quality is measured, when interpreting a low score on a specific dimension, when deciding how to improve a skill's triggering accuracy or orchestration fitness, when setting score thresholds for your marketplace, or when explaining quality badges to external partners like Neon.
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.
Local-evidence production readiness audit for shipped apps, pre-launch reviews, post-merge checks, and "what breaks in prod?" questions without sending repo data to an external audit service. Use when auditing production readiness before launch, after a merge, or when asked what breaks in prod.
Systematic code maturity assessment using Trail of Bits' 9-category framework. Analyzes codebase for arithmetic safety, auditing practices, access controls, complexity, decentralization, documentation, MEV risks, low-level code, and testing, then produces a scorecard with evidence-based ratings and a priority-ordered roadmap. Use when assessing or scoring the maturity of a smart contract or blockchain codebase, producing a maturity scorecard or evaluation, or judging how mature, well-tested, or well-documented such a project is against a rubric.
Use to generate and validate Apex test classes with TestDataFactory patterns, bulk testing (251+ records), mocking, assertions, and disciplined test-fix loops. Use when creating Apex test classes (for triggers, services, controllers, batch jobs, queueables, and callouts), improving coverage, debugging or fixing failing Apex tests, or running test/coverage analysis. Triggers on *Test.cls files, sf apex run test, coverage reports. Do NOT trigger for production Apex (use platform-apex-generate) or Jest/LWC tests.
Writes, reviews, and debugs property-based tests — Hypothesis, fast-check, proptest, jqwik, rapid, and Echidna or Medusa for Solidity invariants. Use whenever tests should cover a whole input domain instead of a hand-picked list of examples: encode/decode and serialize/deserialize pairs, parsers, canonicalizers and normalizers, validators, numeric and Decimal types, comparators and sort order, data structures, and smart-contract state invariants. Also use when adding cases to an existing @given, fast-check, or proptest suite, when judging whether existing property tests assert anything real, and when a generator has shrunk a counterexample and you need to tell a wrong property from a genuine bug. Not for coverage-guided binary fuzzing (libFuzzer, AFL), mutation-testing campaigns, static analysis, benchmarking, or end-to-end UI tests.
Control a Chrome browser session through the chrome-devtools-axi CLI — navigate, snapshot, click, fill forms, run JavaScript, inspect the console and network, take screenshots, audit performance. Use it whenever a task needs a real browser: opening or testing a web page, clicking through a flow, extracting page content, or debugging a website.
Complete browser automation with Playwright. Auto-detects dev servers, writes reusable test scripts, and supports screenshots, responsive checks, UX validation, login flows, link checks, and arbitrary browser automation. Use when the user wants to test a website, automate browser interactions, validate web functionality, or perform browser-based testing.
Apex test execution, coverage analysis, and test-fix loops with 120-point scoring. Use when the user runs Apex tests, checks code coverage, fixes failing tests, or touches *Test.cls / *_Test.cls files. DO NOT TRIGGER when writing Apex production code (use platform-apex-generate), Agentforce agent testing (use agentforce-test), or Jest/LWC tests (use experience-lwc-generate).
Performs AI-powered code review on Git changes using the `ocr` CLI from alibaba/open-code-review. Use when the user asks to review code, review a pull request, review staged/unstaged changes, review a commit, or compare branches for code quality issues. Produces line-level review comments and can automatically apply fixes when requested. With appropriate review rules, can detect various types of issues including bugs, security vulnerabilities, performance problems, and code quality concerns.
Interpret validation errors and guide fixing them. Use when encountering validation errors, validation warnings, false positives, operator structure issues, or need help understanding validation results. Also use when asking about validation profiles, error types, the validation loop process, or auto-fix capabilities. Consult this skill whenever a validate_node or validate_workflow call returns errors or warnings — it knows which warnings are false positives and which errors need real fixes.
Polish a generated CLI to pass verification and become publish-ready. Runs diagnostics (dogfood, verify, scorecard, go vet, gosec), automatically fixes all issues (verify failures, static-analysis findings, dead code, descriptions, README, MCP tool quality), reports the before/after delta, and offers to publish. Use after any /printing-press run, or on any CLI in $PRESS_LIBRARY/. Trigger phrases: "polish", "improve the CLI", "fix verify", "make it publish-ready", "clean up the CLI", "get this ready to ship".
Verification loop for Quarkus projects: build, static analysis (Checkstyle, PMD, SpotBugs), tests with JaCoCo coverage, OWASP dependency and container security scans, GraalVM native compilation, health checks, and config validation. Use when verifying a Quarkus service before a PR, after major refactoring or dependency upgrades, or pre-deploy.
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.