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...
emergency-distress-signal
by aiskillstore紧急求救 - 飞船坠毁后与Stella的首次联系,帮助她评估损伤并制定初步生存计划
illumination
by nimbusxrSun/moon position and illumination levels for NVG operations and timing
structs-building
by AgentWorkers在Structs中构建和管理各种结构体。负责结构的创建、激活、停用、移动、防御定位、隐身功能以及能量生成器的注入等操作。适用于构建结构体、激活或停用结构体、在不同位置之间移动结构体、设置防御任务、启用隐身功能或注入能量生成器等场景。构建时间因具体结构体类型而异：Command Ship的构建时间约为17分钟，World Engine的构建时间则长达约6.4小时。
air-shoes
by HmbownUse Air Shoes when Hermes needs to traverse, inspect, or plan across fragile, hostile, or low-trust terrain without binding the whole move to local surface hazards.
fan
by HmbownUse Fan when the right move is short-range airflow control: gather loose material, pull a target toward a chosen lane, or create a directional repositioning effect without claiming territory or committing to a heavier strike.
invisible
by HmbownUse Invisible when you need a short protected interval to inspect, move, or recover without drawing fire.
hensei-opus
by simokitafresh【将軍専用】家老・忍者は使用禁止。将軍以外が呼んだ場合は即座に中断せよ。 全忍者をOpus統一に戻す（決戦モード）。idle安全機構付き。 TRIGGER: /hensei-opus、Opus全戻し、決戦モード DO NOT TRIGGER: 混成編成（→/hensei-mixed）、個別忍者の手動切替
boba-distill
by stonestorm2024宇宙最强赏金猎人。曼达洛族的传奇战士，沉默寡言，效率至上。追踪千年隼号的经典角色。
civil-affairs-key-leader-engagement-commitment-and-grievance-ledger-cell
by zwright8Track key-leader engagements, promises, grievances, and closure actions to preserve legitimacy in stability operations. Use when commanders need auditable civil commitments instead of ad hoc memory and rumor-driven drift.
coalition-host-nation-civil-order-restoration-cell
by zwright8Support restoration of civil order, policing continuity, and public confidence after major destabilization in host-nation urban centers. Use when commanders or staffs need mission-ready options with explicit tool/protocol bindings, authority gates, and degraded-mode branches.
combat-search-and-rescue-coordinator
by zwright8Synchronize combat search and rescue planning for isolated personnel recovery. Use when preparing CSAR alert posture, package composition, and authentication procedures.
contested-personnel-recovery-planner
by zwright8Plan personnel recovery support in contested environments with joint and coalition assets. Use when coordinating location, authentication, recovery forces, and reintegration steps.
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.