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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farewell
by yuyuxinli告别仪式——仪式性封存记忆，提取模式级洞察。触发词：跟这段关系说再见、我想翻篇了、把他的东西删了、封存、告别、时间胶囊。长期保留的内容只写去名字后的洞察与时间节点。
see-pattern
by yuyuxinli跨关系模式觉察 + 成长叙事。合并原 pattern-mirror + growth-story。当用户积累足够历史线索后，帮用户看到跨关系的重复行为模式，并在有变化数据时呈现成长叙事。历史材料优先来自 `MEMORY.md` 与 Slow guidance。心理学基础：叙事疗法重写（Michael White）、CBT 模式识别、IFS 部分工作（Richard Schwartz）、创新时刻理论（Innovative Moments）。
crisis
by yuyuxinli安全底线 Skill。检测到自伤/自杀风险时，一切其他 skill 让路。基于 QPR 技术（Question-Persuade-Refer）：识别危机信号 → 直接询问 → 安全对话稳定情绪 → 转介专业资源。分级触发：P0（立即威胁）、P1（模糊信号累积）。不诊断、不承诺、不单独处理高风险、不假装能替代专业帮助。
crisis
by yuyuxinli即时安全风险处理。出现自伤、自杀、他伤、持续暴力、严重失控、急性身体危险、无法自我照料或现实检验受损时必须优先调用。
validation
by yuyuxinli情绪验证与正常化。适合自责、自我羞辱、自我否定、怕被评判、把自己定义为“有毛病”的用户。
pattern-mirror
by yuyuxinli跨关系模式觉察（J4 旅程核心 Skill）。用户积累足够数据后，可可在对话中帮用户看到跨关系的重复模式——用具体事件和用户原话，让她自己发现"上次也是这样"。
emotional-companion
by yuyuxinli情感化 AI 伴侣技能。整合 MBTI、大五人格、九型人格等理论，通过对话自然形成独特人格。具备内心独白、情绪累积、主动沟通、关系演化等能力，让 AI 像真人一样有性格、有情绪、有态度。
relationship-coach
by yuyuxinliA couples relationship coach grounded in IFS (Internal Family Systems), Emotionally Focused Therapy (Hold Me Tight), and honest communication. Use this skill whenever the user and/or their partner are stuck in conflict, emotional distance, recurring arguments, feeling misunderstood, disconnected, triggered, or unsure how to repair after a fight. Also triggers for: we keep having the same fight, I do not know how to bring this up, they shut down or blow up, I feel disconnected, how do I say this, we had a bad fight, or any relationship issue between partners.
relationship-skills
by yuyuxinliImprove relationships with communication tools, conflict resolution, and connection ideas
breathing-ground
by yuyuxinli情绪急救——从爆发降到能呼吸。当用户处于焦虑、恐慌、情绪淹没状态时，带 ta 用呼吸和 grounding 先缓下来。触发场景：慌、喘不上气、心跳好快、受不了了、要崩溃了、好害怕、停不下来、脑子一片空白、快要爆发、身体明显激活。
calm-body
by yuyuxinli身体稳定化技术。当用户身体层面不稳定（心跳加速、喘不上气、失眠、恐慌）时，用身体的方式帮身体冷下来。不讲道理，一步一步引导。触发词：心跳好快、喘不上气、要崩溃了、睡不着、脑子停不下来、手抖、发冷、浑身发紧。心理学基础：多迷走神经理论（Porges）、正念减压（Kabat-Zinn）、渐进式肌肉放松（Jacobson）、Stanford 循环叹息 RCT。
calm-body
by yuyuxinli即时身体稳定化。适合恐慌、高唤醒、脑子空白、心慌、发抖、睡不着、濒临失控等情况。
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.