Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
Querying local SQLite index...
steve
by mikkerUse the steve CLI to automate macOS apps via Accessibility APIs. Use when you need to drive Mac UI (apps, windows, menus, elements), run UI smoke tests, or script interactions using steve commands and JSON output.
rails-hotwire
by mikkerRuby on Rails Hotwire best practices for building interactive applications with Turbo Drive, Turbo Frames, Turbo Streams, Turbo 8 morphing, and Stimulus controllers. This skill should be used when writing, reviewing, or refactoring Hotwire-powered Rails code to ensure optimal patterns for navigation, partial page updates, real-time broadcasting, morphing, Stimulus controller design, error handling, and progressive enhancement. Triggers on tasks involving Turbo Frames, Turbo Streams, Turbo Drive, broadcasts, morphing, Stimulus controllers, ActionCable, turbo_stream_from, turbo_frame_tag, data-controller, data-action, or Hotwire performance. Complementary to rails-dev, rails-testing, rails-design-system, ruby-optimise, and ruby-refactor skills.
nitro-kit-components
by mikkerBuild or refactor Nitro Kit-style UI components, helpers, and Stimulus behaviors in Rails apps. Use when working in the Nitro Kit repo or when creating app-specific components that should follow Nitro Kit conventions (Phlex + Tailwind + minimal Stimulus), or when reviewing for Nitro Kit style guide compliance.
swiftui-skills
by mikkerApple-authored SwiftUI and platform guidance extracted from Xcode. Helps AI agents write idiomatic, Apple-native SwiftUI with reduced hallucinations.
bare-importmaps
by mikkerEnforce Bare-style builtin imports and package-local import maps for Holepunch, Pear, Autobonk, Bare runtime, and other dual-runtime JavaScript work. Use when editing code that may run under Bare or a repo that already uses package.json "imports" for bare polyfills. Prefer plain builtin specifiers like "fs", "path", "crypto", "url", "http", and "https" instead of "node:*", and update the nearest package.json import map and dependencies when a new builtin is introduced.
beautiful-mermaid
by mikkerRender Mermaid diagrams as SVG and PNG using the Beautiful Mermaid library. Use when the user asks to render a Mermaid diagram.
commit
by mikkerWrite git commit messages. Use when asked to commit, write a commit message, or stage and commit changes.
defuddle
by mikkerRead web pages as markdown with `npx defuddle`. Use when the user wants a simple local URL-to-markdown fetcher instead of Jina or browser automation.
hunk-review
by mikkerInteracts with live Hunk diff review sessions via CLI. Inspects review focus, navigates files and hunks, reloads session contents, and adds inline review comments. Use when the user has a Hunk session running or wants to review diffs interactively.
proper-fix
by mikkerBias bug fixes and refactors toward root-cause solutions, simplification, and removal of workaround layers. Use when Codex is fixing a bug, flaky behavior, confusing edge case, or ugly conditional pileup and the obvious change looks like another patch on top of earlier patches, defensive code, or duplicated repair logic.
reread-files-before-editing
by mikkerEnsure files are re-read before editing to avoid overwriting user changes. Use only when explicitly encouraged to by user.
solidify-codebase
by mikkerDeep investigation and solidification pass on an existing codebase. Use when asked to audit, simplify, or future-proof a system; perform a deep cleanup/refactor pass; identify high-impact improvement opportunities; or present a vetted change list before implementing selected items.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.