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 85–88 of 88 skills
Automated code review that provides actionable feedback on code quality, potential bugs, performance issues, security vulnerabilities, and style violations. Analyzes code changes with the rigor of a senior engineer, providing specific suggestions with code examples.
Generates optimized SQL queries from natural language descriptions. Supports multiple dialects (PostgreSQL, MySQL, SQLite, SQL Server), handles complex joins, subqueries, window functions, and CTEs. Includes query explanation and performance optimization hints.
Systematic debugging workflow that helps identify, isolate, and fix bugs. Follows a structured approach: reproduce, localize, reduce, fix, guard. Analyzes error messages, stack traces, and logs to pinpoint root causes rather than symptoms.
Analyzes datasets to extract insights, identify patterns, and generate visualizations. Supports exploratory data analysis (EDA), statistical testing, trend detection, and report generation. Works with CSV, JSON, and database outputs.
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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