Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Generate a project-local skill that formally verifies the code where a bug costs the most and tests reach the least, in any language, with whichever verification tool fits. Use for /create-formal-verification-skill, “model-check this race”, “prove this invariant”, or when concurrency, retries, state machines, parsers, or unsafe code need more than tests.
Creates an integration testing plan for .NET data access artifacts during Oracle-to-PostgreSQL database migrations. Analyzes a single project to identify repositories, DAOs, and service layers that interact with the database, then produces a structured testing plan. Use when planning integration test coverage for a migrated project, identifying which data access methods need tests, or preparing for Oracle-to-PostgreSQL migration validation.
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 for content review. Triggers: PR review, pull request, validate skill, check skill.
Debug LLM applications using the Phoenix CLI (`px`, from @arizeai/phoenix-cli). Fetch traces, spans, and sessions, annotate them, analyze errors, measure how often a failure or pattern occurs across traces, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user works with a Phoenix instance from the terminal, or wants to measure or investigate something in Phoenix with the CLI.
The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.
Scaffolds an xUnit integration test project targeting Oracle in .NET solutions. Creates the test project, transaction-rollback base class, and seed data manager. Use only during Phase 3, before writing Oracle baseline integration tests. Do not invoke during Phase 6 — the PostgreSQL test project is produced by migrating this project, not by running this skill again.
Set up Vitest testing in any project — detects type (Cloudflare Workers, React, Node, library), generates vitest.config.ts, test setup, utilities, and a sample test. Covers mocking patterns, coverage config, workspace setup, Jest migration. Use whenever the user mentions adding tests, setting up Vitest, configuring tests, migrating from Jest, fixing testing infrastructure, or asks 'how do I test this'.
Audits an implementation plan for requirements traceability, avoidable complexity, risky assumptions, and evidence gaps. Use when reviewing an AI-generated or human-authored plan, design proposal, implementation outline, task breakdown, or architecture plan and the user wants a standalone audit report without overwriting the source plan.
Investment banking presentation quality checker. Reviews a pitch deck or client-ready presentation for (1) number consistency across slides, (2) data-narrative alignment, (3) language polish against IB standards, (4) visual and formatting QC. Use whenever the user asks to review, check, QC, proof, or do a final pass on a deck, pitch, or client materials — including requests like "check my numbers", "reconcile figures across slides", "is this client-ready", or "what am I missing before I send this out".
Profile, audit, and optimize frontend page performance with emphasis on animation work, memory-leak risks, long-session slowdowns, CSS animations, canvas/WebGL requestAnimationFrame loops, marquees, skeletons, GSAP/Three/Matter effects, timers, listeners, and observers. Use when the user asks to make animations performant, pause offscreen animations, look for memory leaks, profile pages that slow the computer over time, fix janky scrolling, reduce CPU/GPU use, or repeat the "only play in view" optimization on React/Vite/Next/frontend pages using Codex Browser.
Bring a project's formal verification back in line with the code after the code changes: find the commits that touched verified code, update the model, harness, or properties to describe the code as it is now, rerun, and say whether each failure is drift in the verification or a bug in the code. Use for /maintain-formal-verification-skill, \"is the model still true to the code\", or after merges and refactors that touched verified code without rechecking it.
Triage PR changes by classifying change categories, performing surface-level checks, and determining required references. Outputs results to `.pr-triage.json` for use in subsequent code review phases. Use to optimize costs in CI environments.
A disciplined diagnosis loop for tricky bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when the user says “diagnose this” / “debug this,” reports a bug, says something is broken/throwing/failing, or describes a performance regression.
Browser automation using Playwright MCP. Navigate websites, fill forms, click elements, take screenshots, and extract data. Use when tasks require web browsing, form submission, web scraping, UI testing, or any browser interaction. NOT when only fetching static content (use curl/wget instead).
Primary W&B skill for broad or mixed Weights & Biases work: project overviews, W&B runs and artifacts, Weave traces and evaluations, Reports, Workspaces, Automations, Sweeps, Launch, and Agent Lens. Use when the task spans multiple W&B surfaces or the user asks generally what is happening in a W&B project.
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
Execute QA test plans on Holoscan Sensor Bridge hardware. Reads a user-provided test document, filters tests based on the user's setup, determines which tests can run automatically, executes them with pass/fail evaluation, and produces a structured test results report.
Use when building features with Claude Code in any codebase and the work should go through a disciplined build → review → test → fix loop. Triggers on "run the build loop", "build the next task", "continue the plan", "build this feature properly", or any request to implement work from a plan file or a direct feature prompt. Builds from the plan (or the prompt if no plan exists), runs Claude Code's `/review` (plus `/security-review` for sensitive surfaces) and fixes every issue found, tests and verifies the feature end to end, fixes anything testing surfaces, and reports back once complete. Repeats until all plan tasks are checked off.
UX/UI design guidance for building, reviewing, and critiquing interfaces and frontend code. Use when designing screens or components, auditing usability or accessibility (WCAG 2.2, EAA), writing microcopy, or designing forms, navigation, search, tables, dashboards, onboarding, notifications, real-time collaboration, canvas/whiteboard apps, AI/chat interfaces, i18n/RTL, voice, design systems, or barcode/QR scanning and display.
Build, run, and validate evaluators for AI/LLM applications with Phoenix, in Python or TypeScript (including Vercel AI SDK apps) — code-based checks and LLM-as-a-judge, run over exported traces or dataframes or as Phoenix experiments, gate them in CI, and validate judges against human-labeled data.
Review the comments on a PR and address the findings that are valid and required. Use when the user says "address-pr-review", asks to address the PR review, handle or apply the review comments or review feedback on a PR, or fix what the reviewers asked for. Defaults to the PR for the current branch when none is named. This skill changes code; it is not a review pass.
End-to-end testing patterns with Playwright for full-stack Python/React applications. Use when writing E2E tests for complete user workflows (login, CRUD, navigation), critical path regression tests, or cross-browser validation. Covers test structure, page object model, selector strategy (data-testid > role > label), wait strategies, auth state reuse, test data management, and CI integration. Does NOT cover unit tests or component tests (use pytest-patterns or react-testing-patterns).
Use this when reviewing AEM Edge Delivery Services (EDS, Franklin, Helix) code, either self-review at the end of development before opening a PR, or reviewing an existing pull request. Validates block structure, CSS and JS patterns, DOM output, Lighthouse performance, and accessibility against EDS best practices, and posts findings as review comments or GitHub suggestions.
Use this for any AEM Edge Delivery Services (EDS, Franklin, Helix) code change—new blocks, block modifications, CSS styling, bug fixes, core functionality (scripts.js, styles, delayed.js), or any JavaScript/CSS work that needs validation. Covers the Content Driven Development process: identify or create real test content before coding, design an author-friendly content model, and validate against that content throughout development. Do not use for documentation-only changes or configuration changes that do not affect authoring.
Performs gap analysis on NVIDIA TAO VCN Classify (Visual Component Net) experiments by invoking the pinned TAO data-services container directly via `docker run … gap_analysis vcn_aoi …` — picks the optimal decision threshold, ranks per-sample weakness, and emits a top-K weakest parquet expanded per-lighting for downstream augmentation. Use when analyzing VCN classification failures, picking SDA augmentation targets, or auditing PASS/NO_PASS boundary cases.
Create, record, edit, replay, or repair reusable Argent e2e test YAML files. Use when the user asks to record, replay, or run an e2e test or a repeatable device path, set up profiling or an A/B comparison, or invoke the authoring engine behind argent-qa-e2e. Also use before repeating three or more interactions. For one-off UI checks, acceptance-driven regression tests, or screen video, use argent-test-ui-flow, argent-qa-e2e, or argent-screen-recording respectively.
Write repeatable QA regression e2e tests from test cases, tickets, or acceptance criteria. Use when the user asks to write, automate, or generate an e2e test or preserve an automated regression scenario, with deterministic setup, stable targets, executable structural or visual evidence, and two consecutive full passes. For one-off UI checks or replayable paths without acceptance criteria, use argent-test-ui-flow or argent-create-e2e. Supports iOS, Android, Chromium, and Vega (Fire TV), where recorded tv-remote steps replace touch directives. Apple TV and Android TV are unsupported; use argent-tv-interact there and report the limitation.
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.