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...
release-note-creator
by CoooolfanCreate release notes for the project
jimmer-orm
by CoooolfanJimmer ORM 开发助手，帮助编写实体映射代码和 DSL 查询语句。适用于：(1) 定义或修改 Jimmer 实体（使用 @Entity 注解的 interface）；(2) 编写 Jimmer DSL 查询代码；(3) 配置实体关联关系（@ManyToOne, @OneToMany, @ManyToMany）；(4) 使用动态谓词、动态表连接或隐式子查询；(5) 编辑包含 Jimmer 相关代码的 Java/Kotlin 文件。
local-testing
by CoooolfanLocal app and bot testing. Uses agent-browser CLI for Electron/web app UI testing, and osascript (AppleScript) for controlling native macOS apps (WeChat, Discord, Telegram, Slack, Lark/飞书, QQ) to test bots. Triggers on 'local test', 'test in electron', 'test desktop', 'test bot', 'bot test', 'test in discord', 'test in telegram', 'test in slack', 'test in weixin', 'test in wechat', 'test in lark', 'test in feishu', 'test in qq', 'manual test', 'osascript', or UI/bot verification tasks.
agent-tracing
by CoooolfanAgent tracing CLI for inspecting agent execution snapshots. Use when user mentions 'agent-tracing', 'trace', 'snapshot', wants to debug agent execution, inspect LLM calls, view context engine data, or analyze agent steps. Triggers on agent debugging, trace inspection, or execution analysis tasks.
typescript
by CoooolfanTypeScript code style and type-safety guide for LobeHub. Read before writing or editing any `.ts` / `.tsx` / `.mts` — covers `interface` vs `type`, `Record<PropertyKey, unknown>` over `any`/`object`, `as const satisfies`, `@ts-expect-error` over `@ts-ignore`, `import type` (`separate-type-imports`), `async`/`await` + `Promise.all`, `for…of` over indexed `for`, and the no-silent-`.catch(() => fallback)` rule. Also use when reviewing type quality, deciding module augmentation (`declare module`) over `namespace`, or designing extensible types (e.g. `PipelineContext.metadata`). Triggers on any TypeScript file edit, 'fix the type', 'why is this `any`', 'should this be interface or type', 'eslint type-import', 'ts-expect-error'.
react
by CoooolfanUse when writing or editing any `.tsx` under `src/**`. Triggers: createStaticStyles, createStyles, cssVar, antd-style, Flexbox, Center, Select, Modal, Drawer, Button, Tooltip, DropdownMenu, Popover, Switch, ScrollArea, Link, useNavigate, react-router-dom, next/link, desktopRouter, componentMap.desktop, .desktop.tsx, new component, new page, edit layout, add styles, zustand selector, @lobehub/ui, antd import.
builtin-tool
by CoooolfanBuild a new builtin tool package under `packages/builtin-tool-<name>/`. Use when adding a new agent-callable toolset, designing its API surface (manifest / ApiName / Params / State), implementing the Executor + ExecutionRuntime, building the Inspector / Render / Placeholder / Streaming / Intervention / Portal UI, or wiring a tool into the central registries (`packages/builtin-tools/src/{index,identifiers,inspectors,renders,placeholders,streamings,interventions,portals}.ts` and `src/store/tool/slices/builtin/executors/index.ts`). Triggers on "new builtin tool", "add a tool", "tool inspector", "tool render", "tool placeholder", "tool streaming", "tool intervention", "BuiltinToolManifest", "BaseExecutor", "ExecutionRuntime".
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.