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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death-preparation
by LeoYeAIPractical end-of-life preparation and philosophical mortality awareness. Use when someone needs to create a will or advance directive, wants to have the death conversation with family, is processing mortality after a health scare, or wants to get their affairs in order.
enetic-counseling-report
by AGI4SciGenetic Counseling Report - Generate genetic counseling reports: variant interpretation, inheritance patterns, recurrence risks, and clinical recommendations. Use this skill for clinical genetics tasks involving interpret variants determine inheritance calculate recurrence recommend clinically. Combines 4 tools from 2 SCP server(s).
bm-wellness
by BrikerManPhysical and mental health care: preliminary symptom assessment, medication tracking, mood check-in, psychological self-help tools, stress management, emotional support, crisis intervention. Use when user mentions symptoms, illness, medication, mood, anxiety, stress, emotional support, mental health, 不舒服, 症状, 吃药, 情绪, 焦虑, 压力, 心理, 难过, 失眠. Includes safety red lines — never replaces professional medical care.
pain-journal
by khalilbenazJournal de douleur chronique ou récurrente pour suivre localisation, intensité, déclencheurs et évolution. À utiliser quand l'utilisateur décrit des douleurs répétées, chroniques ou difficiles à expliquer. Se déclenche aussi avec "j'ai mal depuis longtemps", "douleur chronique", "la douleur revient", "mal de dos", "migraine", "douleurs articulaires", ou toute description de douleur persistante ou récurrente.
coordinating-social-work-needs
by lev-osIdentifies psychosocial barriers to discharge and coordinates social work interventions. Use when assessing social needs, coordinating community resources, or planning post-discharge support.
managing-eating-disorders
by lev-osGuides eating disorder assessment with medical stability criteria and treatment level determination. Use when evaluating eating disorders, assessing medical stability, or determining treatment level.
health-optimization
by tools-onlyHealth, energy, supplements, and medication management. USE WHEN user mentions Vyvanse, energy, tired, sleep, supplements, health, focus, crash, or any health-related concern.
cancer-buddy-find-care
by CancerDAO查找能做特定治疗资源的医院、专科医生和临床试验。**只做资源发现，不做临床判断**。典型问题：哪家医院能做 MTB（分子肿瘤委员会）？我这个癌种谁是国内做得最好的医生？我能去的城市里有没有 X 靶点的临床试验？这个免疫治疗副作用问题哪里看更专业？输入：profile.json（癌种/分期/分子分型/所在城市/能否跨城/经济条件）+ 一个具体诉求。输出：排序后的资源短名单（含挂号路径、地址、联系方式、匹配理由）。Triggers on: 找医院, 哪家医院能做 MTB, 哪个医生擅长, 临床试验在哪招, 异地就医, 推荐医生, 找专家, 哪儿能做 NGS, 找肿瘤多学科会诊, MDT 哪里有, 找试验中心.
reporting
by dtcolliganNarrate committed recommendations and their audit trail to the end user in plain language. Use when the user asks what today's plan is, why it was made, or how past sessions have gone.
mental-health-integration
by WinbdaDesign behavioral health integration. TRIGGERS - Use when user needs help with mental-health-integration related tasks.
firefly-disclosure
by CancerDAO罕见病遗传风险家庭告知。不是癌症的'要不要告诉患者'禁忌，而是罕见病特有的'如何告诉孩子诊断'、'如何告诉有生育风险的兄弟姐妹'、'如何告诉配偶携带者状态'。按年龄/关系分层告知脚本、家庭会议协议、拒绝回应预案. Use when: 家长纠结要不要告诉孩子诊断、已确诊患者犹豫要不要告诉亲属遗传风险、备孕夫妻发现携带者状态后如何沟通. Triggers on: 告诉孩子诊断, 告诉亲属, 遗传风险告知, 家族告知, 携带者告知, 基因诊断披露.
firefly-mind
by CancerDAO罕见病患者与照护者心理筛查与支持。使用 PHQ-9（抑郁）+ GAD-7（焦虑）+ IES-R（诊断奥德赛创伤应激）三套标准化量表；按分数分级建议（自助/热线/就医）；提供罕见病专属心理支持资源（热线、互助群、照护者支持小组）. Use when: 患者说'情绪不好'、'睡不着'、'撑不住了'、'不知道怎么办'、'感觉绝望'；照护者说'好累'、'不想做了'；诊断奥德赛后出现创伤应激症状. Triggers on: 情绪不好, 抑郁, 焦虑, 睡不好, 撑不住, 崩溃, 想不开, PHQ-9, GAD-7, 创伤应激, 心理支持, 心理筛查.
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.