Skills
100,928 packaged capabilities and playbooks for agents. Install any of them with one command.
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Move items only, reword nothing; realign a listing whose order has gone arbitrary (a list, table, catalog, sections, an enum) into a logical sequence under one stated principle. Nothing is added or removed. Use when the user names a listing, or a scope holding one, whose order no longer helps a reader follow the set, or when a pipeline the user invoked passes that scope on. Acts only inside that scope.
Fix code in a named change or file list so it matches the conventions its own repository already keeps: run the project's configured formatter and linter in fix mode through the project's own toolchain, then fix by hand what no tool decides (naming, import order, file layout, error style) toward the dominant pattern in neighbouring files, behavior preserved, and ask with counts where the repository keeps two competing conventions. Use for 'make this match the codebase', formatter or linter setup, naming consistency, or a style pass on a change under review. Acts only inside the change or files the user names.
Read one stated intent, pick and order the installed skills that serve it, show that order, then run the steps the session may run and stop where a person must act. Use when someone states a goal but does not know which skills serve it or in what order, asks which skills to use, or wants a job that needs several skills run end to end. Typing /paperthin lands here.
Complete React 19 fundamentals system. PROACTIVELY activate for: (1) React 19 new features and changes, (2) Server vs Client Components, (3) Server Actions setup, (4) use() hook usage, (5) JSX and component basics, (6) Props and state patterns, (7) Suspense and Error Boundaries, (8) Fragments and Portals. Provides: React 19 syntax, Server Component patterns, async data handling, component composition, best practices. Ensures modern React 19 patterns with proper server/client architecture.
Complete React component patterns system. PROACTIVELY activate for: (1) Compound components with context, (2) Render props pattern, (3) Higher-Order Components (HOC), (4) Custom hooks as patterns, (5) Provider pattern with reducer, (6) Controlled vs uncontrolled components, (7) Prop getter pattern, (8) State reducer pattern. Provides: Pattern implementations, composition strategies, reusable component APIs, flexible state control. Ensures clean, maintainable component architecture.
Complete React performance optimization system. PROACTIVELY activate for: (1) React.memo and memoization, (2) useMemo and useCallback usage, (3) Code splitting with React.lazy, (4) List virtualization (react-window, react-virtuoso), (5) Avoiding unnecessary re-renders, (6) useTransition and useDeferredValue, (7) Bundle optimization, (8) Web Vitals and profiling. Provides: Profiler setup, memoization patterns, lazy loading, virtualization config, state colocation. Ensures optimal React performance with measurable improvements.
Complete React state management system. PROACTIVELY activate for: (1) Context API patterns and optimization, (2) Zustand store setup and usage, (3) Jotai atomic state, (4) TanStack Query (React Query) for server state, (5) SWR data fetching, (6) useState vs useReducer decisions, (7) State normalization, (8) Avoiding prop drilling. Provides: Store configuration, context optimization, server state caching, optimistic updates, infinite queries. Ensures scalable state architecture with proper tool selection.
Complete React testing system. PROACTIVELY activate for: (1) Vitest/Jest setup and configuration, (2) React Testing Library patterns, (3) Component testing with userEvent, (4) Custom hook testing with renderHook, (5) Mocking modules and components, (6) Async component testing, (7) Context and provider testing, (8) Accessibility testing with jest-axe. Provides: Test setup, query priority, user simulation, mock patterns, integration testing. Ensures reliable tests that focus on user behavior.
Verify the model’s understanding of a user’s instruction before undertaking substantial work. Use when a request is long, bundled, high-stakes, hard to undo, or has ambiguous scope or references such as “this,” “that,” “it,” “the other one,” “whatever is cleaner,” or “whichever order makes sense.” Restate it internally, cross-check against available context, proceed silently if resolved, and surface only a genuine remaining ambiguity.
Keep README files in sync with the repo using symbolic HTML comments. Use this skill whenever making a repo change that could affect documentation — code (exported APIs, configuration, examples, CLI commands, dependencies), repo structure (adding/removing/renaming files, directories, packages, or other sibling entries referenced by a listing comment), or metadata (package.json, manifests). Also use when authoring or editing README files to add or update symbolic comments. Trigger on any change — code or structural — that touches a construct referenced by an HTML comment in a markdown file.
Working with the redis-docs MCP server (search / fetch / ask at redis.io/mcp)—how its index is shaped and how to read what it returns. Use when those tools are available and you are about to look something up in the Redis documentation, or when a result is confusing—fetch returned one short section instead of a page, several hits share one url, ids look like #sec:w1, metadata.version reads 1.0.0, a search came back empty, or ask was rate-limited or said the docs do not cover it. Also covers what redis.io does and does not index
Corrects the wrong defaults a model has when designing a relational schema — the DDL decisions an experienced engineer makes differently. Use when creating or reviewing tables, migrations, ER models, or ORM schema definitions. Covers identity and keys (surrogate vs natural, identity vs serial, uuidv7, composite keys that make cross-tenant references impossible), relationships (polymorphic foreign keys the database cannot enforce, referential actions, unindexed FK columns, disjoint subtypes), invariants the engine can prove instead of application code (EXCLUDE, partial unique indexes, CHECK limits, deferrable cycles, NOT VALID), types (timestamptz, exact money, range types, enum vs lookup table), derived and encoded data (generated columns, JSONB as an escape hatch), and time (events vs in-place updates, soft-delete flags that silently disable constraints, temporal keys). NOT for query tuning, index selection for read paths, or connection pooling.
Eagerly initialize Riverpod providers at app startup by watching them in a root Consumer; handle loading/error in the initializer, AsyncValue.requireValue. Use when a provider must be ready before the rest of the app is used. Use this skill when the user asks about eager initialization or preloading providers.
Use ProviderObserver to log or debug Riverpod provider lifecycle; didUpdateProvider, ProviderScope observers, naming providers. Use when adding logging, analytics, or debugging for provider state changes. Use this skill when the user asks about ProviderObserver, logging Riverpod, or debugging provider updates.
Scan generated/authored HTML for accessibility violations with axe-core (Deque) running over JSDOM — structural and semantic rules: ARIA attributes and roles, accessible names, alt text, form labels, heading order, landmarks, semantic HTML, tabindex, duplicate IDs, lang attribute, and ~50 more. Use on the CODE side of a Figma-to-code workflow: triggers 'scan this HTML for accessibility', 'run axe on my component', 'check the generated code for a11y', 'axe-core scan', 'accessibility lint my markup', 'WCAG check on code', 'is this HTML accessible'. This is a standalone Node script — NO Figma connection required. Visual rules (color-contrast, focus-visible, link-in-text-block, target-size) are DISABLED because JSDOM has no layout — use lint-design-figma for visual a11y on the design side. The two together give full design+code coverage.
Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
Build slide decks and presentations for research talks. Use this for making PowerPoint slides, conference presentations, seminar talks, research presentations, thesis defense slides, or any scientific talk. Provides slide structure, design templates, timing guidance, and visual validation. Works with PowerPoint and LaTeX Beamer.
Transform creative ideas into professional, production-ready screenplays optimized for AI video generation pipelines. Converts raw concepts into structured scene-by-scene narratives with rich visual descriptions, proper screenplay formatting, and XML-tagged output for seamless integration with image/video generation tools (imagine, arch-v). USE WHEN: Converting story ideas into screenplay format, preparing content for AI video pipelines, structuring narratives for 5-10 minute short films, generating visual-rich scene descriptions for image generation. WORKFLOW: Raw idea → Scene breakdown → Visual enhancement → Professional formatting → XML-tagged markdown output OUTPUT: Markdown document with XML-wrapped scenes, rich visual descriptions, proper screenplay elements (sluglines, action, dialogue), and metadata for pipeline processing.
Find an app on the App Store / Google Play and start tracking it in AppFollow, then hand structured app references to the feedback-analysis and aso-analysis skills. Read-only search + dedupe works on any plan; the add step degrades gracefully when the plan does not allow writes. Trigger on "find app X", "track our app", "add <app> to AppFollow".
Secure GitHub Actions workflows against supply-chain, privilege, and shell-injection risks. Use when creating, scaffolding, editing, or reviewing `.github/workflows/*.yml`, reusable workflows, `action.yml`, or Dependabot config for GitHub Actions. Also use for full repository security audits ("audit my workflows", "harden this repo", "security scan", "pin actions to SHA"), secrets scanning with gitleaks and trufflehog, and pre-public-release security reviews. Enforce full 40-character commit SHA pinning, avoid `pull_request_target` on untrusted code, pass GitHub context into `run:` steps via `env:`, and set least-privilege permissions.
Configure the OpenTelemetry Collector with Sentry Exporter for multi-project routing and automatic project creation. Use when setting up OTel with Sentry, configuring collector pipelines for traces and logs, or routing telemetry from multiple services to Sentry projects.
Full website SEO audit with parallel subagent delegation. Crawls up to 500 pages, detects business type, delegates to up to 15 specialists (8 always + 7 conditional), generates health score. Use when user says audit, full SEO check, SEO best-practice review, analyze my site, website health check, or find SEO issues.
Generate SEO-optimized "X vs Y" comparison and "alternatives to X" landing pages targeting comparative-intent keywords. Pulls competitor data, comparative-intent SERPs, and existing comparison pages to produce a balanced, structured page draft with feature matrix, schema, and conversion blocks. Distinct from `seo-agency-landing-page` (top-of-funnel demand-gen). Use when the user asks for "comparison page", "vs page", "alternatives page", "X vs Y", "alternative to X", or "competitor comparison page".
Ad-hoc web scraping, site mapping, and full-site crawling via Firecrawl MCP. Returns raw HTML, parsed metadata (og:*, twitter:*, JSON-LD, canonical, robots), JS-rendered DOM, and screenshots that WebFetch cannot. Distinct from the SE Ranking skills (which give keyword/traffic/SERP data) and from WebFetch (which gives markdown prose only). Use when the user says "scrape this page", "crawl this site", "map this site", "find all pages on", "get the OG tags", "get the JSON-LD", "render this JS-heavy page", or any task where raw HTML head metadata, structured-data scripts, or post-JS DOM are the actual deliverable. Also invoked as a sub-step from other skills that need raw HTML.
Hreflang and international SEO audit, validation, and generation. Detects common mistakes, validates language/region codes, and generates correct hreflang implementations. Use when user says "hreflang", "i18n SEO", "international SEO", "multi-language", "multi-region", or "language tags".
Use to inventory configured Omni-Channel instances across Voice, Email, Chat, SMS, Facebook, WhatsApp, and In-App, including each channel's Queue, Flow, or Other routing target, without changing the org. TRIGGER when: list Omni channels, inventory Omni routing, inspect channel routing targets, analyze Omni-Channel setup, or find configured service channels. Do not use to create, update, activate, or delete channels or routing configuration.
Set up Husky pre-commit hooks in the current repository, integrating lint-staged (Prettier), type checking, and tests. Use when users want to add pre-commit hooks, set up Husky, configure lint-staged, or add formatting/type checking/tests on commit.
Model interpretability via SHAP (Shapley values from game theory). Covers explainer choice (Tree, Deep, Linear, Kernel, Gradient, Permutation), feature attribution, and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use to explain ML predictions, rank features, debug models, audit fairness, or compare models. Works with tree, deep, linear, and black-box models.
Turn a BS report (or any analysis result) into ready-to-paste posts for X/Twitter, LinkedIn, Facebook, Reddit, Hacker News, or a newsletter issue — plus a branded image carousel (PNGs + PDF) for visual platforms. Use when the user wants to share, post, publish, or promote a report, asks for "a thread", "a LinkedIn post", "a carousel", or "format this for X".
Meta-skill for picking project skills via APM. Invoke ONLY when the user explicitly asks to set up apm.yml, choose which skills a project needs, or evaluate a catalog match — do NOT auto-invoke on routine project-init or apm-management tasks. Two-phase: pick from a curated catalog first; only escalate to broader search/evaluation when the catalog has no fit. Avoids the failure mode of impulse-installing skills you never use or hand-searching GitHub when a vetted answer already exists.
Single-dimension execution and multi-source search integration within deep research; use when plan.json already defines dimensions and you need to call the appropriate search capabilities according to dimension.search_strategy for multiple rounds of retrieval, evidence screening, and cross-validation, then write to sub_reports/{dimension_id}.md.
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.