Skills
The open registry for AI agent skills — structured prompts and workflows with recommended models, example prompts, and compatible tools.
Skills
88
Categories
9
Compatible tools
7
Contributors
2
Showing 64–84 of 88 skills
Analyzes complex networks and graphs using NetworkX, igraph, and PyTorch Geometric. Supports social network analysis, biological interaction networks, knowledge graphs, and citation networks. Performs community detection, centrality analysis, link prediction, graph neural networks, and network visualization with force-directed layouts.
Creates publication-quality scientific figures and plots using matplotlib, seaborn, and plotly. Supports common scientific plot types including heatmaps, volcano plots, survival curves, network graphs, phylogenetic trees, and multi-panel figures with proper statistical annotations, color-blind safe palettes, and journal formatting.
Creates production-grade frontend interfaces with high design quality. Generates complete UI components, pages, and layouts using modern frameworks (React, Vue, Svelte) with Tailwind CSS or CSS-in-JS. Focuses on unique, polished designs that avoid generic AI aesthetics — emphasizing typography, spacing, color harmony, and micro-interactions.
Generates professional PowerPoint presentations from text descriptions, outlines, or research notes. Creates structured slide decks with appropriate layouts, bullet points, speaker notes, and visual hierarchy. Outputs valid PPTX files or structured JSON for presentation frameworks.
Interacts with GitHub via the gh CLI for managing issues, pull requests, CI runs, releases, and repository settings. Automates common GitHub workflows including PR creation with proper descriptions, issue triage, release drafting, and CI debugging.
Designs and implements A/B testing experiments including hypothesis formulation, sample size calculation, variant configuration, metric definition, and statistical analysis planning. Covers both frontend feature flags and backend experiment frameworks.
Generates complete, responsive landing pages from a product description or brief. Produces semantic HTML, modern CSS (Tailwind or vanilla), and optional JavaScript for interactions. Follows conversion-optimized layouts with hero sections, features, social proof, pricing, and CTAs.
Transforms complex technical concepts into clear, well-structured documentation for different audiences. Produces README files, architecture decision records (ADRs), runbooks, onboarding guides, and technical blog posts. Follows documentation best practices with consistent tone, proper formatting, and useful examples.
Generates type-safe API client SDKs from OpenAPI specs, API documentation, or example requests. Produces clean, well-typed client code with error handling, retry logic, pagination helpers, and authentication setup. Supports TypeScript, Python, Go, and Rust.
Audits project dependencies for security vulnerabilities, license compliance, maintenance status, and bundle size impact. Identifies outdated packages, suggests alternatives for abandoned libraries, and flags risky transitive dependencies.
Analyzes and resolves git merge conflicts by understanding the intent of both sides. Examines the conflict markers, surrounding context, and commit history to produce a correct merged result that preserves both changes without breaking functionality.
Generates Docker Compose configurations from application requirements. Handles service dependencies, networking, volumes, health checks, environment variables, and multi-stage builds. Supports development and production profiles.
Generates production-ready React components with TypeScript, proper props interfaces, accessibility attributes, responsive design, and test files. Follows modern patterns including Server Components, Suspense boundaries, and composition over inheritance.
Summarizes research papers, technical articles, and documentation into structured briefs. Extracts key findings, methodology, limitations, and practical implications. Adapts output format from executive summary to detailed technical breakdown.
Generates TypeScript type definitions from various sources — JSON data, API responses, database schemas, or plain descriptions. Produces strict types with proper generics, utility types, discriminated unions, and JSDoc comments.
Generates complete OpenAPI 3.1 specifications from API descriptions, existing code, or route definitions. Includes request/response schemas, authentication, error responses, examples, and server configurations. Produces valid YAML ready for Swagger UI.
Designs and implements comprehensive error handling for APIs. Covers error response formats (RFC 7807 Problem Details), HTTP status code selection, error logging strategies, retry logic, and client-friendly error messages with proper i18n support.
Generates comprehensive documentation from code including API references, README files, architecture decision records (ADRs), inline comments, and user guides. Adapts tone and detail level to the target audience (developers, end-users, stakeholders).
Generates comprehensive unit tests for existing code, covering happy paths, edge cases, error conditions, and boundary values. Follows testing best practices including the test pyramid, DAMP over DRY, and the Arrange-Act-Assert pattern. Adapts to the project's existing test framework and conventions.
Designs and implements CI/CD pipelines for various platforms (GitHub Actions, GitLab CI, Jenkins, CircleCI). Covers build, test, lint, security scan, deploy stages with proper caching, parallelization, and environment management.
Reviews web interfaces for WCAG 2.1 AA compliance. Identifies accessibility barriers including missing ARIA attributes, keyboard navigation issues, color contrast problems, and screen reader incompatibilities. Provides remediation code with proper semantic HTML.
Skills vs MCP servers
what's the difference?Skillsthe “what to do”
A skillA reusable, structured prompt/workflow with recommended models, an example prompt, and compatible tools. packages know-how — instructions, an example promptA ready-to-use prompt template that demonstrates how to invoke the skill., and recommended models — so an agent performs a task consistently. Skills add knowledge, not new connections.
MCP serversthe “how to connect”
An MCP serverModel Context Protocol server — a standard way to expose tools, resources, and prompts to AI agents and IDEs. gives an agent new capabilities by connecting it to real systems (databases, APIs, files) over a transportHow the client talks to the server: stdio (local process), SSE, or HTTP streaming.. MCP adds connections and actions, not task instructions.
Rule of thumb: reach for a skill when you need the model to do a task well, and an MCP server when you need it to reach a tool or system. They compose — a skill can rely on tools an MCP server provides.
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