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...
clipboard-memory
by tristanmanchesterRecall what the user copied on this Mac via the local clipmem archive: exact text, commands, SQL, URLs, file paths, HTML, images, and PDFs. Trigger on requests like "what was that command I copied?", "paste back that SQL", "the URL I copied from Safari", "show me what I copied from Xcode today", "find the snippet/path/link from before I restarted", and indirect paraphrases about clipboard history or recovering copied content. Prefer this over web, repo, or filesystem search only when the target was likely copied.
clipboard-memory
by tristanmanchesterUse this skill when the user wants to recover, inspect, or re-export something they previously copied on this same Mac from the local clipmem archive: exact text, commands, SQL, URLs, file paths, HTML, images, or PDFs. Trigger on requests like "what was that command I copied?", "paste back that SQL", "the URL I copied from Safari", "show me what I copied from Xcode today", "find the snippet/path/link from before I restarted", and indirect paraphrases about clipboard history, copy/paste, or recovering something from earlier even if the user never says "clipboard". Prefer this over web, repo, or filesystem search only when the target was likely copied; do not use for content the user never copied, generic file search, or current web lookups.
clipboard-memory
by tristanmanchesterRecall what the user copied on this Mac via the local clipmem archive — text, commands, URLs, file paths, HTML, images, PDFs. Triggers on requests like "what was that command I copied?", "the URL I copied from Safari", "find that snippet before I restarted", or any paraphrase involving copy, paste, or clipboard. Offers ranked recall, chronological timeline, lexical / FTS search, raw-byte export for binary content, cursor pagination, and filters by app, kind, time window, and content shape. Use before reaching for generic web or repo search whenever the user is trying to recover something they previously had on the clipboard.
clipboard-memory
by tristanmanchesterRecall what the user copied on this Mac via the local clipmem archive — text, commands, URLs, file paths, HTML, images, PDFs. Triggers on requests like "what was that command I copied?", "the URL I copied from Safari", "find that snippet before I restarted", or any paraphrase involving copy, paste, or clipboard. Offers ranked recall, chronological timeline, lexical / FTS search, raw-byte export for binary content, cursor pagination, and filters by app, kind, time window, and content shape. Use before reaching for generic web or repo search whenever the user is trying to recover something they previously had on the clipboard.
styling-nativewind-v4-expo
by tristanmanchesterSets up and uses NativeWind v4 (Tailwind CSS v3) in Expo React Native apps, including Expo Router. Configures tailwind.config.js, global.css, babel.config.js (jsxImportSource + nativewind/babel), metro.config.js (withNativeWind + input), and app.json (web bundler metro). Troubleshoots “className not applying”, Tailwind CLI compilation, and Metro cache issues. Implements reusable components/variants, dark mode + theming via CSS variables (vars/useColorScheme), and third-party component styling (remapProps/cssInterop). Use when working on Expo projects using NativeWind v4, Tailwind-style className utilities, or when debugging NativeWind configuration.
sifs-search
by tristanmanchesterUse this skill when you need to find code in a local checkout or Git source by behavior, intent, symbol, file path, related implementation, or indexed chunk context. Use it before broad file reads or grep-style sweeps for exploratory codebase questions, architecture tracing, call-site discovery, and "where/how is X implemented?" tasks. Do not use it for general web search or non-code files unless the user asks to search a source tree.
fabric-cli
by tristanmanchesterUse this skill for Fabric.so CLI workflows with the `fabric` terminal command: diagnose/install/login, search or browse a Fabric library, save notes/links/files, create folders, ask the Fabric AI assistant, manage tasks/workspaces, generate shell completion, check subscription usage, produce JSON output, and use Fabric as persistent agent memory. Do not use for Microsoft Fabric/Azure/Power BI `fab`, Daniel Miessler's Fabric framework, Python Fabric SSH, Fabric.js, or textile/fashion fabric.
extracting-mistral-ocr
by tristanmanchesterExtracts text, tables, and images from PDFs (including scanned PDFs) using the Mistral OCR API. Use when user asks to OCR a PDF/image, extract text from a PDF, parse a scanned document, convert a PDF to Markdown, or extract structured fields from a document.
sifs-search
by tristanmanchesterUse this skill when you need to find code in a local checkout or Git source by behavior, intent, symbol, file path, related implementation, or indexed chunk context. Use it before broad file reads or grep-style sweeps for exploratory codebase questions, architecture tracing, call-site discovery, and "where/how is X implemented?" tasks. Do not use it for general web search or non-code files unless the user asks to search a source tree.
sifs-search
by tristanmanchesterUse this skill when you need to find code in a local checkout or Git source by behavior, intent, symbol, file path, related implementation, or indexed chunk context. Use it before broad file reads or grep-style sweeps for exploratory codebase questions, architecture tracing, call-site discovery, and "where/how is X implemented?" tasks. Do not use it for general web search or non-code files unless the user asks to search a source tree.
sifs-search
by tristanmanchesterUse this skill when you need to find code in a local checkout or Git source by behavior, intent, symbol, file path, related implementation, or indexed chunk context. Use it before broad file reads or grep-style sweeps for exploratory codebase questions, architecture tracing, call-site discovery, and "where/how is X implemented?" tasks. Do not use it for general web search or non-code files unless the user asks to search a source tree.
assemblyai-transcribe
by tristanmanchesterTranscribe, diarise, translate, post-process, and structure audio/video with AssemblyAI. Use this skill when the user wants AssemblyAI specifically, needs high-quality speech-to-text from a local file or URL, wants speaker labels or named speakers, language detection, subtitles, paragraph/sentence exports, topic/entity/sentiment extraction, Speech Understanding, or agent-friendly transcript output as Markdown or normalised JSON for downstream AI workflows.
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.