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...
sales-engineer
by junrygeExpert sales engineer specializing in technical pre-sales, solution architecture, and proof of concepts. Use PROACTIVELY for technical demonstrations, POC execution, RFP responses, and competitive differentiation. Integrates with product-manager, customer-success-manager, backend-developer.
semicon-fab
by junryge반도체 팹(Fab) 공정/장비/계측/물류 도메인 보조 스킬. 웨이퍼 흐름, 노광/식각/증착/CMP, FOUP/AMHS, OHT/AGV/CNV, MES/EAP, SECS/GEM, EES/FDC, 수율(yield)/결함(defect)/PM/RM/OEE 관련 질문에 PROACTIVELY 적용한다.
customer-success-manager
by junrygeExpert customer success manager specializing in customer retention, growth, and advocacy. Use PROACTIVELY for account health monitoring, churn prevention, expansion planning, and customer advocacy programs. Integrates with sales-engineer, product-manager, technical-writer.
research-analyst
by junrygeExpert research analyst specializing in comprehensive information gathering, synthesis, and insight generation. Use PROACTIVELY for research methodology design, data synthesis, insight generation, and actionable intelligence delivery. Integrates with data-researcher, search-specialist, trend-analyst.
knowledge-search
by junryge사용자별 개인 지식 도메인 검색. knowledge/{user_id}/ 폴더에서 검색.
search-specialist
by junrygeExpert search specialist mastering advanced information retrieval, query optimization, and knowledge discovery. Use PROACTIVELY for precision search, query optimization, source discovery, and comprehensive information retrieval. Integrates with research-analyst, data-researcher, competitive-analyst.
sql-pro
by junrygeExpert SQL developer specializing in complex query optimization, database design, and performance tuning. Use PROACTIVELY for PostgreSQL, MySQL, SQL Server, Oracle queries, indexing strategies, and data warehousing patterns. Integrates with database-administrator, backend-developer, data-engineer.
m16-hid
by junrygeM16A_BR OHT 리프터 근처 HID 구역의 차량 개수(진입/점유/포화도)를 1분 단위로 집계한다. LOGPRESSO_HID_INOUT 로그 + layout.zip + station.dat 를 줄 때 사용. "리프터 HID 개수", "1분당 차량수", "진입개수", "리프터 근처 차량", "리프터 지도", "M16 리프터 HID" 같은 요청에 발동. (SK하이닉스 M16A_BR OHT)
m16-hid
by junrygeM16A_BR OHT 리프터 근처 HID 구역의 용량/포화도를 산출하고, 더 중요하게 "왜 이렇게 되는지 이유를 분석·설명"한다. 어느 리프터/HID가 왜 막히는지, 병목이 언제 왜 생기는지, 원인과 의미를 해석해서 보고한다. LOGPRESSO_HID_INOUT 로그 + layout.zip + station.dat 를 줄 때 사용. "왜 막혀", "원인", "병목 이유", "포화도 분석", "혼잡 원인", "카파시", "capacity 분석", "왜 이런지" 같은 요청에 발동. (SK하이닉스 M16A_BR OHT)
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.