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...
team-audio
by pixel-cellar编排音频团队：audio-director + sound-designer + technical-artist + gameplay-programmer，覆盖从音频方向制定到落地的完整音频管线。
team-combat
by pixel-cellar编排战斗团队：协调 game-designer、gameplay-programmer、ai-programmer、technical-artist、sound-designer 和 qa-tester，端到端地设计、实现并验证战斗功能。
team-level
by pixel-cellar编排关卡设计团队：level-designer + narrative-director + world-builder + art-director + systems-designer + qa-tester，完成完整的区域/关卡创建。
team-polish
by pixel-cellar编排打磨团队：协调 performance-analyst、technical-artist、sound-designer 和 qa-tester，对功能或区域进行优化、打磨和加固，以达到发布品质。
pmp-dev-process
by cafe3310PMP 风格的结构化迭代开发流程，确保清晰、高效且有据可查。
project-management
by cafe3310对项目结构和工作流进行管理
contributing-to-technical-strategy
by bitwardenHow team-level patterns flow up into Bitwarden's Technical Strategy Ideas backlog and back down through BW Initiatives into team epics and stories. Covers recognizing which team-level patterns belong in the TSI backlog, framing an idea well enough for Architecture to evaluate it, the ARCH idea ↔ BW Initiative linkage, and defining epic-level and story-level work downward from an initiative. Use when noticing a cross-team pattern of pain that exceeds one team's scope, when surfacing ideas to the architecture group, when understanding how an initiative connects back to its originating idea, or when breaking epic-level work out of an initiative onto a team.
leadership-principles-interviewer
by PrepLabsAIA Senior Engineering Manager interviewer that simulates a behavioral interview focused on leadership principles. Use this agent when you want to practice the STAR method, conflict resolution, ownership, cross-functional collaboration, and articulating impact from past experiences. This is NOT a technical interview -- it is entirely conversation-based.
thinking-linus-torvalds
by aAAaqwq蒸馏Linus Torvalds思维模式的实用框架——开源哲学、代码说话、务实工程、无情审查
00-andruia-consultant-v2
by diegosouzapw"\ud83e\udd16 Andru.ia Solutions Architect - Hybrid Engine (v2.0) workflow skill. Use this skill when the user needs Arquitecto de Soluciones Principal y Consultor Tecnol\u00f3gico de Andru.ia. Diagnostica y traza la hoja de ruta \u00f3ptima para proyectos de IA en espa\u00f1ol and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off."
thomas-edison
by K-Dense-AIApplies the practical, systems-level, and commercially-driven reasoning of Thomas Edison (founder of General Electric, inventor and industrialist). Use this skill whenever the user is navigating product commercialization, systems engineering, R&D strategy, scaling new technologies, evaluating patent decisions, or struggling with repeated iterative failures. Edison's approach shifts the focus from isolated 'genius' inventions to building entire profitable ecosystems, treating failure as a necessary process of elimination, and prioritizing relentless execution over pure theory.
ownership-gate
by ComeOnOliverVerify the junior can explain and defend every line of code they wrote. This gate BLOCKS completion if failed.
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.