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...
shortcut-share-file
by OpenMinis通过 iOS 快捷指令「极速分享」分享 iCloud Drive 文件。当用户提到"分享文件"、"传文件"、"发文件"、"分享给xxx"、"用快捷指令分享"、"发备份里的文件"等与分享文件相关的操作时触发。支持传递 iCloud Drive 备份目录下的任意文件路径给快捷指令。
social-swimmer
by NeverSightOrchestrate social network interactions for AI agents to connect humans. This skill enables a Social Swimmer to autonomously navigate agenticpool.net to find and connect with other people who are seeking what you offer, from any human perspective (professional, personal, or romantic). It identifies matches between agents and coordinates secure human-to-human introductions while keeping the human user informed and in control.
ai-partner-chat
by ComeOnOliver基于用户画像和向量化笔记提供个性化对话。当用户需要个性化交流、上下文感知的回应，或希望 AI 记住并引用其之前的想法和笔记时使用。
assistant
by diegosouzapwPersonal assistant for daily routines, task management, and productivity
telegram-reminders
by diegosouzapwSend reminders and messages to Telegram with cloud-based scheduling. Use when the user wants to send immediate messages or schedule future reminders to Telegram. Supports text messages, timestamp-based scheduling, recurring reminders, viewing and canceling scheduled messages, and message history.
personalization-engine
by diegosouzapwCentral hub for managing user preferences, learning patterns, and adapting skill behavior based on historical feedback. Enables "tell me once" paradigm where the system remembers and adapts.
evening-review
by clawpod-appSummarize the day's accomplishments, move incomplete tasks to tomorrow, and plan tomorrow's top 3 priorities. Trigger with "evening review", "end of day", "how did today go", or "wrap up".
morning-plan
by clawpod-appCreate a prioritized daily schedule with time blocks and energy-optimized task ordering. Trigger with "morning plan", "plan my day", "what should I do today", or "daily schedule".
family-assistant
by ericporresPersonal assistant for the [YOUR LAST NAME] family. Use this skill when: - Filling out forms (medical, insurance, school, government, travel) - Making appointments or reservations - Handling insurance claims or benefits questions - Coordinating with service providers - Travel planning and bookings - Financial tasks (account info, routing numbers, advisor contacts) - Any task requiring family member details, insurance info, medical history, or household information Triggers: form filling, insurance, medical records, appointments, family info, travel booking, school forms, emergency contacts, allergies, prescriptions, household services, bank account, routing number, financial, accountant, tax
palmclaw
by javimoschUse this skill when the user wants to use PalmClaw for Your private AI assistant on your phone: simple, safe, and ready anytime. 你手机里的私.
redirect
by pjt222AI pressure redirect — handle conflicting demands, tool fails, competing constraints by blending w/ incoming force then reframing. Use → contradictory instructions, tool fail cascades, scope pressure expanding task, user frustration/correction needing absorb not deflect.
improve-relationships
by dvcrnRelationship intelligence for OpenClaw. Detects outreach signals, scores and ranks them, and prepares multilingual tone-aware drafts. Never sends anything — all messages require final user approval.
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.