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...
promise-fulfillment-protocol
by baojieUse when deciding whether to honor a commitment after circumstances change, especially after the recipient's death. Applies Ji Zha's sword-promise principle: internal assent creates moral obligation that transcends death.
parenting-psychology
by LeoYeAIEmotional and developmental parenting guidance for ages 0-18. Use when someone is struggling with child behavior, needs age-appropriate expectations, wants discipline strategies that work, or is overwhelmed by parenting.
romantic-relationship-maintenance
by LeoYeAIPractical relationship maintenance for couples. Use when a relationship feels stale, tense, or on autopilot, when communication has broken down, or when someone wants to strengthen a partnership before it's in crisis.
conflict-mediate
by XiaoLuoLYGMediate disagreement through calm conversation.
relationship-skills
by sundial-orgImprove relationships with communication tools, conflict resolution, and connection ideas
hello-reflect
by hellowind777任务完成后使用 — 生成会话反思，提取经验教训追加到相关模块文档，供未来会话参考。
social-physics-filter
by winstonkoh87Unified boundary enforcement, interpersonal diagnostic, and relational audit engine. Absorbs 40 psychology + 2 social protocols and all relationship case studies.
she-love-me
by 863401402Analyze exported WeChat or QQ chat logs to assess relationship dynamics, communication asymmetry, attachment patterns, and risk signals, then generate a structured Chinese relationship analysis HTML report. Use when the user provides chat exports or asks for relationship analysis based on message history.
she-love-me
by 863401402Analyze exported WeChat or QQ chat logs to assess relationship dynamics, communication asymmetry, attachment patterns, and risk signals, then generate a structured Chinese relationship analysis HTML report. Use when the user provides chat exports or asks for relationship analysis based on message history.
cognitive-toolkit
by glebisEvidence-based CBT and DBT intervention skills — guided thought records, opposite action, DEAR MAN roleplay, crisis skills with optional HRV biofeedback. Configurable therapeutic pushback. Triggers on "/cbt", "/dbt", "/thought-record", "/record", "/opposite", "/opposite-action", "I need to work through something", "help me with a thought", "cognitive distortion", "I'm spiraling", "can we do a thought record".
hearth
by geekjourneyxHearth (火炉边) — Relationships & family deliberation room. Convene Fromm, Adler, Zhuangzi, Kant, Aurelius, and Watts for relationship dynamics, parenting, family conflict, and workplace politics.
partner-text-coach
by curiositechReal-time communication coach for navigating partner/relationship texts. Analyzes incoming messages for emotional subtext, suggests thoughtful responses, helps de-escalate conflict, and provides follow-up conversation strategies. Expert in attachment theory, nonviolent communication (NVC), Gottman research, and healthy relationship dynamics. Activate on "what should I say", "how to respond", "partner text", "relationship message", "what does this mean", "text my partner", "conversation with partner". NOT for manipulation tactics, revenge/ghosting advice, replacing couples therapy, or abusive relationships (seek professional help).
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.