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.
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x-ipe-tool-implementation-java
by Young-ZJava-specific implementation tool skill. Handles Spring Boot, Quarkus, Micronaut, and plain Java projects with built-in best practices (SOLID, clean architecture, JUnit 5). No research step needed — practices are baked in. Called by x-ipe-task-based-code-implementation orchestrator. Triggers on Java tech_stack entries.
pptx
by Young-ZPresentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or editing content, (3) Working with layouts, (4) Adding comments or speaker notes, or any other presentation tasks
x-ipe-task-based-share-idea
by Young-ZConvert refined idea summaries to shareable document formats (PPTX, DOCX, PDF). Use when user wants to share or present an idea. Uses MCP document conversion tools or pandoc. Triggers on requests like "share idea", "convert to ppt", "make presentation", "export idea".
x-ipe-tool-architecture-dsl
by Young-ZGenerate Architecture DSL from requirements or refine existing DSL. Use for layered architectures (Module View) or application landscapes (Landscape View). Triggers on "architecture diagram", "layer diagram", "module view", "landscape view", "draw architecture".
x-ipe-all-task-board-management
by Young-ZDEPRECATED — Redirects to x-ipe-tool-task-board-manager
x-ipe-feature-feature-board-management
by Young-ZDEPRECATED — Redirects to x-ipe-tool-task-board-manager
x-ipe-tool-kb-librarian
by Young-ZOrganize knowledge base intake files — analyze content, assign lifecycle/domain tags, generate YAML frontmatter, move to destination folders. Use when user triggers AI Librarian from KB UI or CLI. Triggers on requests like "organize knowledge base intake files with AI Librarian", "run AI Librarian", "organize intake".
x-ipe-assistant-knowledge-librarian-dao
by Young-ZCentral orchestrator for the knowledge pipeline. Discovers available knowledge skills at runtime, classifies requests, routes to constructors/extractors/ontology skills, and drives the full 格物致知 workflow. Triggers on requests like "build a user manual", "extract knowledge", "run knowledge pipeline", "discover ontology graphs".
x-ipe-knowledge-constructor-notes
by Young-ZDomain expert for general knowledge notes construction. Implements the 4-operation constructor interface (provide_framework, design_rubric, request_knowledge, fill_structure) for notes-type knowledge bases with flexible numbered hierarchy. Triggers on operations like "provide_framework", "design_rubric", "request_knowledge", "fill_structure".
x-ipe-task-based-general-purpose-executor
by Young-ZExecute tasks by following provided instructions with knowledge base guidance. Uses user manuals via x-ipe-tool-user-manual-referencer for step-by-step walkthroughs. Triggers on requests like "execute task", "follow instructions", "run steps", "general purpose", "execute goal", "accomplish task".
x-ipe-tool-knowledge-extraction-notes
by Young-ZGeneral-purpose knowledge extractor that organizes content into structured markdown knowledge bases with hierarchy, embedded images, and linked overview. Use when extracting knowledge, creating structured notes, or organizing content into a knowledge base. Triggers on requests like "extract knowledge notes", "create knowledge base", "organize knowledge", "take key insights".
x-ipe-tool-user-manual-referencer
by Young-ZLook up, retrieve, and interpret instructions from user manuals stored in the knowledge base. Use when executor needs manual guidance to perform a step. Triggers on requests like "lookup user manual instruction", "get step-by-step from manual", "troubleshoot from manual", "list documented features".
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.