Skills
The open registry for AI agent skills — structured prompts and workflows with recommended models, example prompts, and compatible tools.
Skills
122
Categories
9
Compatible tools
6
Contributors
2
Showing 85–100 of 100 skills
Analyzes SQL queries and database schemas to identify performance bottlenecks and suggest optimizations. Recommends index strategies, query rewrites, denormalization opportunities, and partitioning schemes. Explains EXPLAIN plans and provides before/after comparisons with expected performance improvements.
Designs and scaffolds AI agent architectures including tool definitions, system prompts, memory strategies, and orchestration logic. Supports multi-agent workflows, ReAct patterns, function calling schemas, and MCP server configurations. Helps structure agents that are reliable, observable, and easy to debug.
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.
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 Identifies outdated packages, suggests alternatives for abandoned libraries, and flags risky transitive dependencies.
Facilitates structured brainstorming sessions using proven ideation frameworks — SCAMPER, Six Thinking Hats, How Might We, Crazy Eights, and more. Generates diverse ideas, challenges assumptions, and helps converge on the strongest concepts.
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.
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 clear, structured pull request descriptions from code diffs. Includes summary of changes, motivation, testing notes, and reviewer guidance. Follows team conventions and links related issues automatically.
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.
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.
Rewrites vague or technical error messages into clear, actionable user-facing messages. Considers the audience (end-user vs developer), suggests error codes, and provides guidance on what the user can do to resolve the issue.
Generates .env files, configuration schemas, and environment variable documentation from application requirements. Includes validation rules, default values, required vs optional flags, and example values. Supports multiple environments (dev/staging/prod).
Drafts professional emails for various business contexts — follow-ups, introductions, requests, escalations, and announcements. Adapts tone from formal to friendly based on audience and relationship. Keeps messages concise and action-oriented.
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.
Builds and explains cron expressions from natural language schedules. Supports standard cron (5-field), extended cron (6-field with seconds), and cloud-specific formats (AWS EventBridge, Google Cloud Scheduler). Validates expressions and shows next run times.
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.
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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