Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Testing & quality
(NS) Cypress E2E in independent tests-e2e/ at repo root (bootstrap when missing). DRY device-aware commands (shared/pages/device). Use when writing/refactoring Cypress specs, custom commands, scaffolding E2E, or implementing E2E tasks — not when planning E2E task markdown (ns-spec-driven references/e2e-test-task-generator.md). Mandatory discovery before specs.
Build safe, version-pinned telemetrygen commands for synthetic OTLP traces, metrics, and logs. Use for "send sample traces to this collector," "load-test an OTLP endpoint," "generate traffic to verify a processor, OTTL transform, or tail sampling," and "produce test data for dashboards." Also use when choosing telemetrygen transport, TLS, attributes, counts, duration, or rate. Not for application SDK instrumentation or production collection that does not use telemetrygen.
Prepare a structured, scannable review checklist for a branch or diff, grouped by feature change and applying the four-pass review order (Types, Data Flow, Business Logic, Edge Cases) within each feature. Use when the user asks to "prep a review", "review order", or wants a checklist to manually walk through changes.
Enforce repository coding standards for Swift 6.2 strict concurrency, actor isolation, @Observable models, SWIFT_APPROACHABLE_CONCURRENCY, @concurrent functions, and modern Swift APIs across visionOS app code. Use when reviewing, writing, or migrating Swift code in this plugin's scope.
Static scan of an Expo / React Native iOS app for non-native patterns — Material component kits, JS navigators instead of native stack/tabs, PanResponder, disabled font scaling, hardcoded hex colors, unvirtualized lists — reports each violation with file:line and a link to the rule. Trigger when the user asks to check, lint, audit, or review an Expo iOS app for HIG compliance or "native feel," or before shipping/merging Expo iOS UI. Pairs with the expo-ios-hig rules skill (it checks what that skill teaches) and the expo-ios-screen-scaffolder.
Figma-to-code design handoff patterns including Figma Variables to design tokens pipeline, component spec extraction, Dev Mode inspection, Auto Layout to CSS Flexbox/Grid mapping, and visual regression with Applitools. Use when converting Figma designs to code, documenting component specs, setting up design-dev workflows, or comparing production UI against Figma designs.
Use the minitest CLI to manage scenarios, upload native builds, execute mobile (phone or tablet) and web app test runs, and analyse results. Use when the user asks to test their mobile or web app, create test scenarios, run tests, check test results, or manage native builds via the command line. Also use after any code change that affects UI, navigation, or user journeys to check if existing tests need to be updated.
This skill should be used when the user asks to "playtest my game", "test my Phaser game", "does my game actually run", "check the game in a browser", "verify the game works", "smoke test the game", "automate game testing", "catch black screens", "check for asset 404s", "measure FPS", "screenshot the game", "write a playtest scenario", or whenever Phaser 4 code has been written or changed and needs runtime verification before being handed back to the user.
Pre-flight before creating a pull request: run `greptile review` on the committed branch, VERIFY the review actually matches this branch's diff, triage its findings, apply safe fixes (propose risky ones), and only then `gh pr create`. Runs in repos where greptile is configured. Trigger ONLY on explicit PR-creation intent: "crea el PR", "create the PR", "abrí el PR", "open a PR", or immediately before a `gh pr create`. Do NOT fire on a bare commit.
Generate a requirements-quality checklist for an OpenSpec change and run it against the proposal/design/tasks/delta — validating completeness, clarity, consistency, and testability BEFORE you approve the spec. Modeled on GitHub Spec-Kit's /checklist. Use when reviewing a spec at the human approval gate, or before launching implementation, to catch under-specified requirements early.
Surface-aware end-to-end verification of a change — determine whether it touches a FRONTEND surface (rendered UI) or an API/backend surface, then validate the right way. Frontend → chrome-devtools MCP (open the affected page, screenshot, confirm it renders, check console/network/accessibility). API → a Playwright request script that hits the affected endpoints and asserts status + response shape. Use after implementing a change, or in the harness Validate step, to confirm it actually works in the running app — not just that unit tests pass. Requires the chrome-devtools-mcp plugin (frontend) and/or Playwright (API); both are wired up by the kit installer.
Evidence-based investigative code review using deductive reasoning to determine what actually happened versus what was claimed. Use when verifying implementation claims, investigating bugs, validating fixes, or conducting root cause analysis. Elementary approach to finding truth through systematic observation.
A comprehensive auditor for any agent skill — including Manus, OpenClaw/ClawHub, Claude, LobeHub, or custom SKILL.md-based skills. Use this skill whenever a user wants to evaluate, audit, review, score, or quality-check an agent skill before publishing, updating, or deploying. Covers two hard veto gates (structural redlines + research integrity redlines), static quality scoring across 25 criteria (ISO 25010 + OpenSSF + Agent), dynamic test input generation, multi-mode execution testing, multi-layer output evaluation with five specialized category rubrics (Evidence Insight / Protocol Design / Data Analysis / Academic Writing / Other), a Research Veto that applies to all four research categories, human eval viewer generation, actionable P0/P1/P2 optimization recommendations, and automatic skill improvement that outputs a polished, production-ready SKILL.md. Also use whenever a user says "audit my skill", "evaluate my skill", "improve my skill", or wants a corrected version after evaluation.
Designs and implements pytest test suites for Python libraries with fixtures, parametrization, mocking, Hypothesis property-based testing, and CI configuration. Use when creating tests, improving coverage, setting up testing infrastructure, or implementing property-based testing.
Authoritative App Intents best practices from Apple. Consult for any App Intents best-practices or correctness review, and when writing, reviewing, refactoring, or extending App Intents code. Supersedes prior training on these topics. For code generation, consult the relevant reference when working on any of the following: - execution-model: perform() is Sendable, not @MainActor (hop with await MainActor.run); it's retriable (restartPerform), so do irreversible work last; requestConfirmation before destructive work; return via .result(...) factories, never a bare value. - entities-and-queries: AppEntity.id must be stable across launches/devices; entities(for:) (batched) vs empty-default suggestedEntities(); EntityStringQuery.entities(matching:) is not auto-filtered; only @Property members are system-visible; EnumerableEntityQuery loads all (use EntityPropertyQuery for large stores). - entity-property-queries: EntityPropertyQuery for Shortcuts \"Find X where…\" — properties/sortingOptions/comparators; you execute the predicate, the framework only parses it. - app-enum: AppEnum raw values are persisted by string (never renumber/reorder); every case needs a caseDisplayRepresentations entry or it's a runtime fatalError. - parameters / parameter-summaries: requestValue vs needsValueError; non-optional AppEnum auto-disambiguates; only params in Summary(...) show in the Shortcuts editor; When/Switch for conditional display. - dependencies: @Dependency must be Sendable and registered at launch (unregistered = fatalError); goes on the intent/query, never on the AppEntity/AppEnum. - results-and-errors: only CustomLocalizedStringResourceConvertible errors surface a real message; prebuilt AppIntentError.* (iOS 18+). - donation: in-app actions are NOT auto-donated — call IntentDonationManager.shared.donate; PredictableIntent is descriptions only. - localization: user-facing strings must be literal LocalizedStringResource (a runtime String yields no extractable key). - app-shortcut-phrases: provide shortTitle/systemImageName; include \\(.applicationName) or the runtime index silently drops the phrase. - url-representation: OpenIntent / OpenURLIntent / URLRepresentableEntity for opening and universal links. - configuration-intents: WidgetConfigurationIntent / ControlConfigurationIntent are parameter-only — no perform(). - factoring: AppEnum (fixed set) vs AppEntity+query (dynamic) vs plain @Parameter; one intent per atomic task. For iOS 26/27 new-API adoption, use the app-intents-whats-new-27 skill instead.
Audits all OrchestKit skills for quality, completeness, and compliance with authoring standards. Use when checking skill health, before releases, or after bulk skill edits to surface SKILL.md files that are too long, have missing frontmatter, lack rules/references, or are unregistered in manifests.
Guides benchmarking and comparing explicit multi-statement transactions versus single-statement CTE transactions in CockroachDB, with fair test methodology, contention analysis, and performance interpretation. Use when comparing transaction formulations, benchmarking CockroachDB workloads under contention, investigating retry pressure, or deciding whether to rewrite multi-step application flows into single SQL statements.
Guided, scripted pipeline for running JSX/TSX/React codemods safely across large legacy codebases. Use when you need to scaffold a codemod, dry-run it, validate its findings, and apply it across many files (50 to 100k+) without breaking the build. Walks the full inner/outer loop with the Codemod CLI (JSSG, ast-grep, workflows). Triggers on large-scale refactor, legacy React migration, codemod a prop/API change, migrate components across the codebase, run a codemod safely, batched/resumable codemod apply, dry-run a codemod.
Perform a read-only, defect-first review of a specified code change and return every actionable finding, reading the whole diff before running anything. Use when another agent delegates review of uncommitted changes, a base-branch diff, a commit, or custom review instructions. Prefer this over the Codex-bundled review-agent, which carries no read-before-you-run gate.
Core conventions and rules for Playwright E2E testing with TypeScript. Covers project structure, naming conventions, selector strategy, authentication, navigation, environment configuration, test independence, and parallelism. Automatically loaded when writing or modifying E2E tests. Use when: generating E2E tests, creating POMs, reviewing test code, or setting up a Playwright test suite.
Huawei Cloud MRS Hive SQL specification checking skill. Checks SQL statements against defined syntax and specification rules using the automated checker engine. No extra manual analysis beyond defined rules. Trigger: "Hive SQL优化", "检查Hive SQL", "Hive SQL检查", "Hive SQL规范", "Hive SQL语法", "Hive SQL review"
Triage a daily msverl regression run by reading the baseline comparison log, stopping on success, extracting the most relevant training failure evidence from the daily training log when needed, collecting recent commits from verl main and MindSpeed master, and ranking the most likely culprit commits with concise fix-direction guidance.
Test LLM applications for prompt injection vulnerabilities — jailbreak attempts, system prompt extraction, context manipulation, guardrail bypass techniques, direct injection, indirect injection, multi-stage attacks, and reconnaissance. USE WHEN prompt injection, jailbreak, LLM security, AI security assessment, pentest AI application, test chatbot, guardrail bypass, direct injection, indirect injection, RAG poisoning, multi-stage attack, complete assessment, reconnaissance.
Conducts comprehensive requirements review including completeness validation, clarity assessment, consistency checking, testability evaluation, and standards compliance. Produces detailed review reports with findings, gaps, conflicts, and improvement recommendations. Use when reviewing requirements documents (BRD, SRS, user stories), validating acceptance criteria, assessing requirements quality, identifying gaps and conflicts, or ensuring standards compliance (IEEE 830, INVEST criteria). Trigger when users mention "review requirements", "validate requirements", "check requirements quality", "find requirement issues", or "assess BRD/SRS quality".
SkillOpt-flavored offline training loop for any SKILL.md. Treats accumulated learn-rule corrections as training trajectories, proposes bounded patches via an optimizer LLM, gates each candidate against a held-out validation set built from the user's own past corrections, and ships only candidates that demonstrably improve the score. Inspired by Microsoft SkillOpt's ReflACT pipeline (rollout → reflect → aggregate → select → update → evaluate) adapted to pro-workflow's SQLite store. Use when a skill has accumulated 8+ learn-rule rows and the user wants the skill itself to get better, not just longer.
Use this skill to write non-flaky Compose animation tests by setting mainClock.autoAdvance = false and stepping frames by hand with advanceTimeByFrame and advanceTimeBy(durationMillis). Explains why InfiniteAnimationPolicy cancels indeterminate animations the moment the test starts unless autoAdvance is disabled, why the first frame after a state toggle does not initialize playTime, and why state mutations during the paused-clock window must go through runOnUiThread instead of runOnIdle. If the user mentions "animation never finishes", "InfiniteAnimationPolicy CancellationException", "Crossfade test flaky", "indeterminate progress indicator", "Thread.sleep(500) waiting for animation", advanceTimeByFrame, advanceTimeBy, or autoAdvance, use this skill.
Full web app security assessment — app understanding, threat modeling, OWASP testing, ffuf fuzzing, Playwright automation, AI-assisted vuln analysis. Coordinates with Recon and PromptInjection skills. USE WHEN web assessment, pentest, security testing, vulnerability scan, threat model app, understand application, create threat model, vulnerability analysis, Gemini analysis, ffuf, fuzzing, bug bounty, OSINT, browser automation, Playwright.
Warehouse Management System testing patterns for inventory operations, pick/pack/ship workflows, wave management, EDI X12/EDIFACT compliance, RF/barcode scanning, and WMS-ERP integration. Use when testing WMS platforms (Blue Yonder, Manhattan, SAP EWM).
Independent audit of AI-implemented work — certifies a completed task actually did what it claims, checking files, diffs, tests, and CI rather than the agent's self-report. Flags skipped or weakened tests, mock-hidden integration, snapshot drift, happy-path-only coverage, flaky retries, and status/evidence mismatches. Use when validating completed Compozy tasks, AI-authored PRs, or codex-loop iterations. Not for real-user, persona, or journey QA — use qa-execution for those.
Create, migrate, and optimize skills for this alicloud-skills repository. Use whenever users ask to add a new skill, import an external skill, refactor skill structure, improve trigger descriptions, add smoke tests under tests/**, or benchmark skill quality before merge.
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.