Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
Implements work already planned—a plan.md from tlc-plan or a small ticket—by freezing it into checks with proofs, building, and proving every check with a script and an independent Verifier before declaring done. Use when the user says “implement this plan”, “build this ticket”, “implement this spec”, “write the checks”, “verify work”, or “tlc-implement”. Do NOT use when nobody has decided what to build, or to plan a feature that needs one (that is tlc-plan).
Runs Fastly Compute WASM applications locally with Viceroy, specifically for Rust and Component Model projects. Use when starting a local Fastly Compute dev server with Viceroy, configuring fastly.toml for local backend overrides and store definitions, running Rust unit tests with cargo-nextest against the Compute runtime, debugging Compute apps locally, adapting core WASM modules to the Component Model, or troubleshooting local Compute testing issues (connection refused, missing backends, store config). For non-Rust Compute work or understanding the Compute API, prefer the fastlike skill instead — its source code is easier to understand as a Fastly Compute API reference.
How to test and verify XL1 work — choosing and running the right verification approach. Groups headless verification methods: Sequence testnet verification (in-process seed-phrase signer), local dev-chain verification (public xl1 CLI or vitest-owned chains via public @xyo-network/dapp-kit-vitest-config local-xl1 / api-local, or the restricted XYO-internal @xyo-network/xl1-vitest-config apiLocal), composed local chain + Aries data-store verification, full local dApp backend verification (aries-dapp-core classic path or dapp-kit local path), unattended Sequence testing via @xyo-network/wallet-xl1-cli, and headless browser-mode testing (vitest + Playwright). Includes shared testnet-only safety rules. Activates when an agent needs to test, verify, or smoke-test an XL1 dApp or protocol change, run on-chain tests against a local chain or Sequence, run headless browser tests, or set up unattended / CI testing. Owns the dApp Definition of Done gate, required before reporting any XL1 dApp work complete.
Use this skill to gate iOS SwiftUI design and UX with a pass/fail adversarial review — a single blind reviewer subagent judges screens, diffs, or features against 56 decidable HIG-derived rules, working backwards from mandatory rendered evidence, simulator screenshots (light, dark, accessibility text size) and interaction recordings tiled into filmstrips. Covers navigation/IA (tab bars, five tabs max, system Back), modality and flow grammar (sheets, alerts, unsaved-content protection), layout (44pt targets, safe areas, Dynamic Type), color and contrast, typography floors, Liquid Glass, feedback states (empty, loading, error, launch), motion — filmstrip-primary except the spring-curve and Reduce Motion checks, so code alone never prescribes an unseen animation, and gratuitous decoration fails — plus haptics and craft. Fixes must be the minimal change, removal preferred over addition. Trigger before merging user-facing SwiftUI work. Verdicts only, never fixes; targets without a SwiftUI UI surface abort.
Apply fixes or verify fixes based on a code review report and its development plan. Not for an initial review, changing only a plan, or implementing a plan without review input. Verify fixes one by one, commit as agreed in the plan, and write back dispositions and evidence
[Hyper] Use this skill when optimizing an existing Codex skill through baseline-first experiments, binary evals, Guard checks, and one-mutation-at-a-time iteration. Use for skill autoresearch, measured trigger/workflow improvement, self-optimizing a skill, benchmarking skill changes, or resuming skill experiment artifacts. If invoked without a target, ask for the target skill and eval intent before creating artifacts or mutating files. Do not use for one-off skill creation/refactor, generic docs polish, app QA, commit-only, or push-only requests.
Use when the user wants to run a prepared AWS FIS experiment where the CloudFormation stack has already been deployed. Triggers on "execute FIS experiment", "run FIS experiment", "start chaos experiment", "执行 FIS 实验", "运行混沌实验", "执行故障注入实验", "run the experiment in [directory]", or when the user provides an FIS experiment template ID (e.g. EXT1a2b3c4d5e6f7). Does NOT deploy infrastructure — only checks that it is already deployed.
Accessibility engineering for product interfaces, from focus states and keyboard support to ARIA, forms, and screen readers. Use when building or reviewing UI components, modals, menus, forms, custom widgets, or when the user says "make this accessible" or reports keyboard or screen-reader issues. Triggers on accessibility, a11y, WCAG, aria, focus ring, focus-visible, focus trap, keyboard navigation, tab order, tabindex, screen reader, sr-only, aria-live, alt text, hit area, touch target, pointer-events, hover on touch, prefers-reduced-motion, autoplay, toast duration, skip link, semantic HTML, aria-label, form errors, disabled buttons, "not keyboard accessible".
Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', or reviewing a small diff you don't trust.
Benchmark Blender modeling agents, skills, prompts, scripts, or MCP tools with paired isolated runs. Use for baseline-versus-plugin comparisons, regression suites, skill forward-testing, MCP usefulness evaluation, score calibration, or claims that a Blender workflow improves mesh, visual, structural, animation, cost, or completion quality.
Copilot left 14 review comments on your PR — half are nits. Hours of fix → reply → resolve → re-request, and each round lands MORE comments. This skill runs loop engineering: auto-triggers Copilot Code Review via GraphQL (no @copilot mention), triages every open thread (Copilot, humans, advanced-security) with a fix / decline / escalate rubric, dispatches parallel fix sub-agents that obey the repo build/test/lint conventions, commits per iteration, replies+resolves citing the pushed SHA, then re-triggers until HEAD is reviewed with zero threads awaiting the agent's reply (remaining open threads are explicit hand-offs to the human — escalated declines, design tradeoffs). You merge a clean PR; the bot runs it. Trigger phrases: "address copilot comments", "run a copilot review loop", "fix this PR", "iterate on copilot feedback". Repo-agnostic, gh CLI + PowerShell. Full autopilot needs repo Triage/Write; external PR authors get single-iteration mode plus manual re-trigger (UI 🔄 or substantive-commit push).
Dispatches one subagent per independent domain to parallelize investigation/fixes. Use when you have 2+ unrelated failures (e.g., separate failing test files, subsystems, bugs) with no shared state or ordering dependencies.
Run a read-only documentation drift audit for a feature, PR, branch, release, API, configuration change, workflow, CLI, package, or repository area. Use when the user asks whether docs are stale, missing, inconsistent with code, or need updates after code changes. Checks README files, setup guides, API docs, env docs, changelogs, examples, comments, generated docs, and user-facing instructions. This is not a general code review; it compares what the docs claim against what the code does.
Use when you have an approved implementation plan document and need to execute it step by step. Triggers on /execute command, when transitioning from planning with an approved plan, or when resuming execution of a partially completed plan. Provides batch-based execution with TDD, checkpoint reviews, and verification gates.
Structures the human review experience for factory-mode builds with dashboard-style audit trail summaries (slice counts, rework rates, gate failure distribution), PR digests (per merged PR with gate results), retrospective synthesis (recurring rework causes, pair effectiveness, domain hotspots), quality trend tracking (session-over-session comparison), escalation review (rework history with resolve/override/send-back options), and autonomy tuning interface. Use during Phase 3 human review of factory-mode builds or when assessing autonomous build quality. Triggers on: "review the build", "factory review", "audit trail", "quality trends", "escalation review", "PR digest", "autonomy tuning", "rework rate". NOT for: running the pipeline (use pipeline), individual code reviews (use code-review).
Conduct Failure Mode and Effects Analysis (FMEA) for systematic identification and risk assessment of potential failures in designs, processes, or systems. Supports DFMEA (Design), PFMEA (Process), and FMEA-MSR (Monitoring & System Response). Uses AIAG-VDA 7-step methodology with Action Priority (AP) risk assessment replacing traditional RPN. Use when analyzing product designs for potential failures, evaluating manufacturing process risks, conducting proactive risk assessment, preparing for APQP/PPAP submissions, investigating field failures, or when user mentions "FMEA", "failure mode", "DFMEA", "PFMEA", "severity occurrence detection", "RPN", "Action Priority", "design risk analysis", or needs to identify and prioritize potential failure modes with their causes and effects.
Invoke when the user wants to rapidly prototype a mechanic, test a proof of concept, run hypothesis-driven development, or validate a game idea before full production. Triggers on: "prototype", "proof of concept", "test this mechanic", "validate idea", "rapid prototype", "bake-off". Do NOT invoke for starting a full project (use game-start) or brainstorming concepts (use game-brainstorm). Part of the AlterLab GameForge collection.
Educational guide on best practices for creating implementation plans that prevent drift. Covers style anchors, task sizing, TDD requirements, affirmative instructions, drift handling, and quality gates. Use when creating or improving implementation plans to ensure they follow proven patterns.
Periodic pass that keeps a project's verification skill and feature map honest: parallel source readers per feature, one live session driving every feature, at most one PR of proven corrections. Use for /maintain-verification-skill or "audit the verify skill".
Use Mergify merge queue to queue/dequeue PRs, to monitor and inspect the queue, and to diagnose a dequeued PR — whether it is queued, why it was dequeued, where its CI failure is, and what to do next. ALWAYS use this skill when queuing or dequeuing a PR, checking queue status, investigating PR merge state, finding out why a PR left the queue, pausing/unpausing the queue, or debugging merge failures. Triggers on queue a PR, requeue, dequeue, dequeued, why was my PR dequeued, dequeue reason, merge queue, queue status, queue pause, queue show, pause, unpause, frozen, bisecting, batch, CI checks, CHECKS_FAILED, PULL_REQUEST_UPDATED.
Use this skill to wire MockK (the Kotlin-first mocking framework) into a JVM unit-test suite, especially when coroutines, singleton/`object` mocking, or constructor mocking dominate. Covers `io.mockk:mockk-jvm:1.14.x` (and `mockk-android` + `mockk-agent` for instrumented), `every { } returns`, `coEvery` / `coVerify` (native suspend support — the big win over Mockito), `mockk(relaxed = true)` / `relaxUnitFun = true`, `slot<T>()` capture, `verifySequence` / `verifyOrder` / `confirmVerified`, `mockkStatic` / `mockkObject` / `mockkConstructor` with `unmockkAll()` cleanup, `@MockK` / `@RelaxedMockK` / `@SpyK` / `@InjectMockKs` annotations, and the Mockito-vs-MockK decision criteria. Use when the user mentions `coEvery`, `mockkObject`, `mockkStatic`, `slot`, `MockKException`, `every returns vs coEvery`, `relaxed = true`, or asks how to mock a Kotlin `object`/singleton.
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.
Analyze Android, Linux, and Chromium Perfetto traces with local trace_processor_shell evidence. Use for startup, scrolling or jank, input latency, ANR, CPU scheduling, memory or GC, Binder or IO, GPU or SurfaceFlinger, power or thermal, rendering-pipeline identification, trace capture guidance, scene reconstruction, and single- or multi-trace performance comparison.
Govern Playwright TypeScript regression suites across many tests. Use when asked to plan, select, tier, execute, or optimize suites with risk/change analysis, tags, CI/CD, sharding, flaky-test management, or suite-health metrics; not for authoring one UI spec. Keywords: regression strategy, smoke tests, test selection, CI pipeline, flaky tests, test sharding, impact analysis, git diff.
Use when a user wants an AI assistant or coding agent to provide setup guidance, evaluation, review, improvement, recommendation follow-up, or paired skill/CLI update help for quality management of a project/entity or one of its components/areas. Trigger for requests about quality factors, characteristics, attributes, criteria, areas, factors, requirements, improving a quality factor such as security/reliability/usability, reviewing a QUALITY.md model or evaluation result, evaluating a root area against quality criteria, applying or handing off recommendations, updating the /quality stack, or authoring/improving a QUALITY.md file.
Use when building or auditing a radiomics or tabular clinical-ML prediction model with a classical learner (LASSO, SVM, random forest, XGBoost and similar). Enforces nested CV, dimensionality control, in-fold feature selection, feature stability, calibration and external validation.
Systematic code refactoring following Martin Fowler's catalog. Methodologies: characterization tests, Red-Green-Refactor, incremental transformation. Capabilities: SOLID compliance, DRY cleanup, code smell detection, complexity reduction, legacy modernization, design patterns, functional programming patterns. Actions: refactor, extract, inline, rename, move, simplify code. Keywords: refactor, SOLID, DRY, code smell, complexity, extract method, inline, rename, move, clean code, technical debt, legacy code, design pattern, characterization test, Red-Green-Refactor, functional programming, higher-order function, immutability, pure function, composition, currying, side effects. Use when: improving code quality, reducing technical debt, applying SOLID principles, fixing DRY violations, removing code smells, modernizing legacy code, applying design patterns.
Use when reviewing code for security vulnerabilities, implementing authentication or authorization, handling user input, managing secrets, or auditing dependencies for known CVEs. Triggers: auth implementation, input handling, secrets management, dependency audit, pre-deployment security check, OWASP compliance review.
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.