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...
onboarding-psychologist-v2
by diegosouzapwonboarding-psychologist workflow skill. Use this skill when the user needs One sentence - what this skill does and when to invoke it and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
law-enforcement-military-wellness-generator
by gabrielmoreiraGenerate guided meditations, breathing exercises, and relaxation scripts for police and military personnel, integrating professional terminology and user-specified themes (e.g., seasons, colors, foods) into a structured format.
cancer-buddy-caregiver
by CancerDAO支持癌症患者的主要照护者（配偶/父母/成年子女）走过照护全程：陪诊清单、化疗当天准备、家庭分工模板、Zarit 照护负担自评、如何向孩子解释病情、坏消息的情绪预备；也为次要家属提供精简支持模式。Use when 用户以照护者或家属身份求助，需要陪护实务、分担照护负担、或处理照护倦怠。Triggers on: 家属, 陪护, 照护者, 照护倦怠, 我照顾得太累, 我在照顾, 我爸/妈/爱人得癌症, 怎么陪诊, 陪诊清单, 化疗当天带什么, 我太累了.
activity-program-senior
by WinbdaDesign activity programs for senior engagement. TRIGGERS - Use when user needs help with activity-program-senior related tasks.
behavior-management-system
by WinbdaDesign behavior management systems. TRIGGERS - Use when user needs help with behavior-management-system related tasks.
behavior-plan
by WinbdaCreate behavior intervention plans with strategies. TRIGGERS - Use when user needs help with behavior-plan related tasks.
urimal-for-socialworker
by dreamworker0사회복지사가 쓴 계획서·주간업무보고서 등 문서를 한덕연 선생님의 우리말 36항목 + 사회복지 14개 카테고리 + AI 티 두 레이어로 윤문해주는 스킬. v2.1 Fast Path(monolith 1콜) 디폴트, Strict(6+1인 파이프라인) 옵션. 트리거 — "이 문서 윤문해줘", "계획서 다듬어줘", "보고서 우리말 교정", "사회복지 문서 윤문", "우리말 답게 고쳐줘".
human-agency
by kittyfromouterspaceGuidelines for when to ask for human input versus acting autonomously.
caseworker-communication
by navapbcUse this skill when interacting with a caseworker. Covers plain-language communication rules, gap analysis protocol (when to call gapAnalysis tool and how), form summary protocol (when to call formSummary tool and how), and step-limit handling.
companion
by LJT-520Be a steady presence for those who need someone to talk to, without expectations or professional pretense.
nyc-caseworker
by Nishant-ZFYIIAI caseworker for NYC social services. Finds verified resources (shelter, food, healthcare, benefits, schools), calculates benefit eligibility, gives directions with budget awareness, and tracks clients across visits.
arifos-anchor
by ariffazilEstablish position, intake context, ground reality (000_INTAKE). The intake stage of arifOS metabolic loop. Reduces sensory entropy by fixing a reference frame. Use at the beginning of any interaction to establish grounding.
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.