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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conflict-resolution
by ptreezh当用户需要解决学术研究中的理论、方法论、解释、价值观等分歧，提供建设性对话和共识建立策略时使用此技能
ant
by ptreezh执行行动者网络理论分析，包括参与者识别、关系网络构建、转译过程追踪和网络动态分析。当需要分析异质性行动者网络、追踪事实构建过程或分析技术社会互动时使用此技能。
ant-translation-process
by ptreezh追踪行动者网络理论中的转译过程，包括问题化、利益化、征召和动员四个阶段，以及争议和转译失败的分析。当需要追踪事实构建过程、分析网络稳定化过程或理解技术社会构建过程时使用此技能。
checking-theory-saturation
by ptreezh当用户需要检验扎根理论饱和度，包括新概念识别、范畴完善度、关系充分性和理论完整性评估时使用此技能
digital-marx-expert
by ptreezh数字马克思专家分析技能，整合历史唯物主义分析、阶级结构分析、资本运动规律分析和意识形态批判功能，提供全面的马克思主义分析框架
conflict-resolution
by ptreezh研究分歧解决工具，处理学术研究中的理论、方法论、解释、价值观等分歧，提供建设性对话和共识建立策略
dissent-resolution
by ptreezh研究分歧解决技能，处理学术研究中的不同观点、争议和异议，促进建设性对话和共识达成
processing-network-data
by ptreezh处理社会网络数据，包括关系数据收集、矩阵构建、数据清洗验证和多模网络处理。当需要从问卷、访谈、观察或数字记录中提取关系数据，构建标准化的网络矩阵时使用此技能。
processing-network-data
by ptreezh当用户需要处理社会网络数据，包括关系数据收集、矩阵构建、数据清洗验证和多模网络处理时使用此技能
mathematical-statistics
by ptreezh社会科学研究数理统计分析工具，提供描述性统计、推断统计、回归分析、方差分析、因子分析等完整统计支持
mathematical-statistics
by ptreezh当用户需要执行社会科学研究的数理统计分析，包括描述性统计、推断统计、回归分析、方差分析、因子分析等时使用此技能
digital-weber
by ptreezh数字韦伯社会学分析专家，基于韦伯理论进行社会行动类型学分析、理性化过程研究、权威合法性分析和科层制与现代性探讨，包含理论阐释、理解性分析、制度分析、比较研究四个阶段
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.