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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benevolent-governance-demonstration
by baojieUse when building popular support through benevolent governance or demonstrating virtue as a ruler. Applies Tang's three-sided net principle, self-reflection via public welfare, minister appointment, and post-war benevolence practices.
decisive-battle-motivation-strategy
by baojieUse when commanding troops in a decisive battle requiring total commitment. Applies the 破釜沉舟 (break cauldrons, sink boats) technique to eliminate retreat, limit provisions to 3 days, and force maximum engagement. Based on Xiang Yu at Julu (钜鹿之战).
benevolent-governance-implementation
by baojieUse when implementing benevolent governance (仁政) to build popular support and long-term stability. Covers tax reduction, legal reform, agricultural promotion, and frugality following Emperor Wen of Han's model.
yellow-bell-calculation-method
by baojieUse when establishing the fundamental pitch standard (黄钟) or deriving all twelve pitch pipes. Calculates the 9-cun base unit from powers of 3 and links musical tuning to weights, measures, and seasonal timing.
governance-by-adaptation
by baojieUse when governing newly acquired territories or managing diverse populations. Applies Taigong's (Jiang Ziya) four-step method: adapt to local customs (因其俗), simplify rituals (简其礼), develop commerce (通商工之业), and leverage resources (便鱼盐之利).
personnel-evaluation-by-humanity
by baojieUse when vetting subordinates or appointees for trustworthiness. Applies Guan Zhong's humanity test (非人情) to identify dangerous individuals who violate natural bonds — parental, family, and self-preservation — to gain power.
merit-based-reward-classification
by baojieUse when designing a tiered reward system for contributions or ranking officials by merit. Classifies service into four tiers: moral guidance (封邑 grants), administrative execution (卿位 titles), military labor, and routine service, allocating rewards systematically from highest to lowest.
legitimacy-building-for-uprising
by baojieUse when planning a rebellion, popular uprising, or regime change that requires perceived divine mandate. Creates legitimacy through fabricated omens, invocation of beloved historical figures, and the rallying cry 王侯将相宁有种乎 to mobilize popular support.
alliance-building-diplomacy
by baojieUse when building multi-state coalitions or diplomatic alliances against a dominant threat. Applies Su Qin's 合纵 strategy — analyzing each party's geography, military strength, and fears, then proposing concrete mutual defense terms with specific obligations per member.
bian-que-six-incurable-conditions
by baojieUse when screening patients before committing treatment resources. Applies Bian Que's 六不治 framework to identify six incurable conditions (arrogance, greed, poor self-care, yin-yang chaos, extreme weakness, trust in shamans over physicians).
talent-recommendation-best-practice
by baojieUse when building reputation and organizational strength through personnel recommendations. Prioritizes recommending those more capable than yourself (贤於己者) to create networks of competent allies and earn trust from superiors.
besieged-force-defense
by baojieUse when defending a small force surrounded by superior numbers. Employs circular formation, ammunition conservation, targeted strikes on enemy commanders, and aggressive sorties to maintain morale until relief arrives.
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.