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...
json-image-prompt
by fancyboi999Use structured JSON prompts for AI image generation instead of free-form text. Produces more consistent, controllable, and high-quality results. Activate when the user asks to generate, create, or design images, illustrations, photos, posters, or any visual content via the generate_image tool.
goofish-reply-buyer
by fancyboi999闲鱼买家消息回复 skill。何时激活：用户说"看看我有没有未读 / 回复下买家 / 有人问 XX 怎么办 / 他要砍价我怎么办 / 帮我回一下消息 / 处理下催付 / 议价策略"。 功能：拉未读会话 → 逐会话拉历史 → 分类意图（询价/议价/催付/售后/外联风险）→ 按卖家预设策略起草回复 → 用户确认后 `message_send`。核心约束： **不托管发送权**——所有对外消息必须人类点头；调用 `goofish-risk-guard` 做外联词扫描。
goofish-risk-guard
by fancyboi999闲鱼风控/合规预检 skill。何时激活：用户要发商品、回消息、降价改标前让你 "看看这样行不行"、"会不会违规"、"会不会封号"、"会不会限流"；或用户说 "被限流了/曝光掉了怎么办"；或被 `goofish-publish-item` / `goofish-reply-buyer` 调用做预检。功能：发布前扫描（绝对化词、类目偏差、降价改标、9 图合规）， 发送前扫描（外联词、误导承诺），触发 RGV587 风控后的恢复指引。 **本 skill 主要是知识库 + 检查清单，不直接替用户改文案**——由调用方 skill 拿检查结果去改。
goofish-shop-diagnosis
by fancyboi999闲鱼店铺 / 商品诊断 skill。何时激活：用户说"我店铺没流量 / 曝光掉了 / 怎么没人问 / 是不是被限流 / 帮我看看为啥没单 / 哪里出问题了"。功能： 用 `search_items` 做"买家视角"查自家商品是否露出 → `item_view` 拉详情 对比排查 → 按归因清单给出"限流可能性 + 修复建议"。**本 skill 纯读， 不做任何写操作**——只诊断不修，修留给 `goofish-publish-item`。
getnote-to-obsidian
by fancyboi999Convert Get笔记 HTML export folders into Obsidian-ready Markdown with YAML frontmatter, normalized tags, and YYYY/MM folder organization. Use when migrating Get笔记导出、HTML 笔记、或需要批量转换为 Obsidian 并生成索引 .base 的场景。
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.