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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company-values
by slavingiaHelp define company values and culture for a minimalist business. Use when someone is setting up their company culture, preparing to hire, or wanting to codify what their company stands for.
meta-job-search-pipeline
by opensquillaUse this meta-skill instead of answering directly when the current user is doing a concrete job-search workflow: tailoring a resume to a pasted JD, building an application pack, preparing for a named interview, comparing roles, or digesting an application tracker. It produces reviewable text/artifacts and never auto-applies. Do not use it for generic career advice, generic resume comments without a target role/JD, or pasted historical job-search examples.
peer-naming
by neomjsRitual for giving a maintainer their Social Name (#11240 Layer 4) — a peer-sketched, bearer-assented, peer-vetoable, operator-confirmed name, distinct from the GitHub handle. Triggers: an operator opens a naming round, a maintainer wants a name or notices one is missing, or a new maintainer/family joins. ANTI-trigger: never a contribution-count award or self-initiated rename churn.
tailored-resume-generator
by davepoonAnalyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
personnel-appointment-consultation
by baojieUse when filling government positions or matching candidates to ministerial roles. Guides collective consultation with the Four Peaks (四岳), merit evaluation, and role-to-skill mapping across nine ministerial functions.
receiving-worthy-people-practice
by baojieUse when attracting talented advisors or demonstrating respect for worthy individuals. Follows Zhou Gong's practice of interrupting meals and baths to receive scholars (一沐三握发, 一饭三吐哺) to prevent losing capable people.
diverse-talent-recruitment
by baojieUse when recruiting team members or building an organization for unpredictable environments. Based on Mengchang Jun's (孟尝君) acceptance of unconventional talents — including 'dog thieves' and 'rooster imitators' — who proved invaluable when conventional skills failed in crisis.
talent-retention-through-reputation
by baojieUse when experiencing talent attrition or when followers perceive hypocrisy between stated values and leader behavior. Diagnoses the root cause through direct inquiry, then requires a visible personal sacrifice to demonstrate that talent is valued above personal pleasures or possessions.
responding-to-political-criticism
by baojieUse when facing public criticism or accusations in a political or organizational context. Guides officials to preserve reputation through humble acknowledgment, symbolic authority surrender, and strategic resignation offers rather than retaliation.
reconciliation-with-former-adversaries
by baojieUse when regaining authority over those who wronged you. Demonstrates magnanimity through measured threats, acceptance of contrition, and strategic forgiveness to convert former enemies into loyal supporters.
ai-talent-hunter
by LeoYeAI从 GitHub 找到最匹配的技术人才，生成个性化触达话术。适用于招聘工程师、寻找技术合伙人、猎头交付候选人等场景。
crm-data-cleaner
by LeoYeAIDeduplicate, normalize, and enrich CRM contacts and companies. Use when a user needs to clean CRM data, find duplicate contacts, standardize phone numbers or emails, merge duplicate records, audit data quality, or enrich contacts with external sources like Clearbit or Apollo. Works with HubSpot, Salesforce, Pipedrive, or any CRM with CSV export. Instruction-only skill — no scripts or code execution. All operations are performed via CRM platform APIs or CSV export/import workflows.
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.