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...
lyt-traffic-logic
by YYYYYZhaolyt 系列跨境电商流量分配逻辑讲解 skill。仅处理跨境电商、TikTok Shop、电商平台、店铺、商品、商品卡、搜索、推荐、广告、内容、达人相关的流量机制问题；非业务、非电商、生活百科或专业护理问题要拒绝。用于用户提出「为什么店铺没流量」「为什么商品没曝光」「平台为什么不给流量」「流量是怎么分配的」「新店为什么没人看」「为什么别人有流量我没有」等问题时，帮用户补齐商品卡搜索、短视频推荐、广告/GMV Max、冷启动和成交链路相关的流量分配知识。本 skill 只讲逻辑，不做具体诊断，不追问补充字段。 触发方式：/lyt-traffic-logic、/流量逻辑、/流量分配、/没流量逻辑
lyt-data-analysis
by YYYYYZhaolyt 系列业务/电商数据分析诊断 skill。仅处理业务、电商、TikTok Shop、投放、GMV Max、商品、商品卡、短视频、直播、达人、内容、交付、客户、供应链、利润、成本、店铺经营相关数据；非业务、非电商、纯生活百科或专业护理问题要拒绝。根据用户反馈、业务描述、截图、CSV/XLSX 表格、竞品数据或后台数据，先做数据体检和口径校验，再分清事实、推理和假设，拆解 GMV、漏斗、广告、成本和利润变量，并给出可执行优化动作。 触发方式：/lyt-data-analysis、/Lyt-data-analysis
lyt-problem-clarifier
by YYYYYZhaolyt 系列跨境电商问题追问 skill。仅处理跨境电商、TikTok Shop、店铺、商品、商品卡、短视频、直播、广告、GMV Max、数据、内容、达人、物流、供应链、合规相关经营问题；非业务、非电商问题要拒绝。用于用户提出「为什么不出单」「为什么没流量」「为什么广告跑不出去」「为什么突然下滑」「为什么爆款视频不出单」等模糊问题时，先把宽泛问题界定成具体、可操作的问题，再追问成交场景、最近动作、数据断点和最小证据。 触发方式：/lyt-problem-clarifier、/问题追问、/追问定位、/不出单追问
lyt-product-selection
by YYYYYZhaolyt 系列电商选品诊断 skill。仅处理跨境电商、TikTok Shop、电商商品销售、类目选择、选品验证、供应链、货盘、内容、广告或达人分销相关选品问题；非业务、非电商、纯生活百科或专业护理问题要拒绝。用户还没想好产品时，先引导用户建立选品边界、用结果倒推类目、找候选品池和采集必要数据；用户已有商品想法、候选品清单、平台数据、竞品截图、供应链信息或 CSV/XLSX 表格时，评估商品是否值得进入测试池，并给出低成本验证方案。 触发方式：/lyt-product-selection、/Lyt-product-selection
lyt
by YYYYYZhaolyt 系列跨境电商主路由 skill。用户调用 /lyt、$lyt、Lyt 或提出跨境电商、TikTok Shop、选品、店铺、商品、广告、数据、内容、供应链、达人分销、流量分配逻辑相关问题时，先判断是否属于 lyt 业务范围；非业务、非电商、生活百科或专业护理问题要拒绝；相关问题自动路由到 lyt-product-selection、lyt-problem-clarifier、lyt-traffic-logic 或 lyt-data-analysis。
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.