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...
typographic-hierarchy
by RandyHaylorFormat documentation, READMEs, and structured text using header hierarchy where each level stands alone. Use when creating docs, research notes, summaries, or when user requests 'scannable,' 'well-structured,' 'skimmable,' or 'readable at multiple depths' output. Applies to markdown, technical specs, and any hierarchical text formatting.
rtf-creator
by RandyHaylorGenerate valid RTF (Rich Text Format) documents from scratch. Use when the user asks to create .rtf files, convert content to RTF, or produce formatted documents compatible with Word, LibreOffice, and other word processors.
claude-web-search-helper
by RandyHaylorGuidelines AND an optional PreToolUse hook for web searches. Use when searching the web, googling, looking something up, researching a topic, or finding documentation. Also use when setting up the install-web-search-hook so WebSearch calls require self-check answers in the preceding message.
afk-mode
by RandyHaylorManage the user's AFK (away-from-keyboard) status for the PermissionRequest auto-deny hook. Use when the user says they're stepping away, coming back, switching to focus/presenting/debugging mode, or asks to change their Claude Code permission-dialog behavior. Also use when the user asks to set up, configure, or troubleshoot the afk-mode system.
d2-diagram
by RandyHaylorGenerate D2 diagram files (.d2) for technical reference — flowcharts, architecture diagrams, sequence diagrams, state machines, database schemas, and class diagrams. Use whenever the user wants to create, document, or visualize any system, flow, architecture, API, database, or process. Trigger on "make a diagram", "draw a flowchart", "document this architecture", "visualize this flow", "create a sequence diagram", "map this system", or any request to illustrate how something works.
draw-io
by RandyHaylorGenerate and edit draw.io diagrams (.drawio XML). Use for flowcharts, architecture diagrams, swimlane diagrams in .drawio format. Handles font settings, arrow z-ordering, VS Code preview, and PNG export.
fine-grained-skill-builder
by RandyHaylorGuide for building fine-grained skills where content is split into many small files to minimize tokens loaded per invocation. Use when creating or restructuring a skill to be token-efficient with section-based progressive disclosure.
icloudpd-icloud-photo-backup
by RandyHayloricloudpd CLI for downloading/syncing iCloud Photos. Use when user asks about icloudpd, iCloud photo backup, or syncing iCloud photos locally.
mermaid-diagramming
by RandyHaylorCreate, edit, and layout Mermaid diagrams (.mmd files) correctly in VS Code using the MermaidChart extension. Use when creating or modifying any Mermaid diagram — flowcharts, sequence diagrams, etc. Covers node shapes, edge types, line breaks in labels, subgraph layout rules (side-by-side vs stacked), and VS Code preview commands. Critical: subgraph direction TB is silently ignored when nodes link across subgraphs.
offloaded-skills
by RandyHaylorRegistry of extended skills stored in ~/.claude/offloaded-skills/ to save context window space. Consult ~/.claude/offloaded-skills/index.md to discover available skills and load them on demand. Also documents how to offload and restore skills.
pdf-to-text
by RandyHaylor"Convert PDF files to clean text. Handles both embedded-text PDFs and scanned/image PDFs via OCR. Use when the user wants to import, extract, or convert a PDF to text."
rtf-creator
by RandyHaylorGenerate valid RTF (Rich Text Format) documents from scratch. Use when the user asks to create .rtf files, convert content to RTF, or produce formatted documents compatible with Word, LibreOffice, and other word processors.
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.