Skills
The open registry for AI agent skills — structured prompts and workflows with recommended models, example prompts, and compatible tools.
Skills
38
Categories
9
Compatible tools
5
Contributors
1
Showing 22–38 of 38 skills
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.
Builds, explains, and tests regular expressions from natural language descriptions. Supports multiple regex flavors (PCRE, JavaScript, Python, Go). Provides step-by-step breakdowns, test cases, and performance considerations for complex patterns.
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.
Designs, optimizes, and iterates on prompts for LLM applications. Covers system prompt design, few-shot examples, chain-of-thought reasoning, output formatting, and prompt testing strategies. Helps build reliable AI-powered features.
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.
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.
Generates clear, conventional commit messages from code diffs. Follows Conventional Commits specification with appropriate type prefixes, scopes, and descriptions. Handles breaking changes, multi-file changes, and produces both concise subjects and detailed bodies.
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).
Translates code between programming languages while preserving logic, idioms, and best practices of the target language. Handles differences in type systems, error handling, concurrency models, and standard library APIs. Produces idiomatic target code, not line-by-line transliteration.
Performs comprehensive security analysis of code and configurations. Identifies OWASP Top 10 vulnerabilities, insecure patterns, missing input validation, authentication flaws, and secrets exposure. Provides remediation steps with secure code examples.
Guides systematic code refactoring while preserving exact behavior. Identifies code smells, suggests appropriate refactoring patterns, and executes transformations incrementally with verification at each step. Follows Chesterton's Fence principle — understands why code exists before changing it.
Identifies and resolves performance bottlenecks in code and systems. Covers algorithmic complexity analysis, memory optimization, caching strategies, database query tuning, and frontend performance (Core Web Vitals). Follows a measure-first approach.
Designs normalized database schemas from business requirements. Covers entity relationships, indexing strategies, migration planning, and performance considerations. Supports PostgreSQL, MySQL, MongoDB, and other databases with dialect-specific optimizations.
Designs RESTful and GraphQL APIs following contract-first principles. Covers endpoint structure, request/response schemas, error handling, versioning, pagination, authentication, and rate limiting. Produces OpenAPI/Swagger specifications and implementation scaffolding.
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.
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.
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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