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.
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linkfox-mpstats-ozon-brand-products
by linkfox-aiMPSTATS Ozon 俄罗斯站按品牌下钻商品列表。按 Ozon 品牌展示名（俄语/拉丁）返回该品牌下全部商品的销量、销售额、价格、评分、库存、周转、损失销售额等完整指标，支持多维数值筛选、排序、货币换算。用于品牌对标、竞品分析、品牌商品结构研究、ASIN 级（productId 级）爆款拆解。当用户提到 Ozon 品牌下钻、Ozon 品牌商品、Ozon 竞品品牌分析、品牌结构、品牌 SKU、品牌爆款、Ozon 品牌销售、MPSTATS brand, Ozon brand products, brand drill-down, brand competitor analysis, Russian marketplace brand SKUs, brand revenue share 时触发此技能。即使用户未明确说"MPSTATS"，只要意图是按 Ozon 品牌看该品牌下所有商品及其销量/价格/评分表现，也应触发此技能。
linkfox-mpstats-ozon-seller-products
by linkfox-aiMPSTATS Ozon 俄罗斯站按卖家 ID 下钻商品列表。返回该卖家下全部 SKU 的销量、销售额、价格、评分、库存、周转、损失销售额等完整指标，支持多维数值筛选、排序、货币换算。用于店铺结构分析、卖家爆款拆解、竞争对手店铺对标。当用户提到 Ozon 卖家商品、Ozon 店铺分析、Ozon 卖家下钻、Ozon 卖家 SKU、Ozon 店铺爆款、Ozon 竞争店铺、MPSTATS seller, Ozon seller drill-down, Ozon shop audit, Russian marketplace seller SKUs, Ozon store structure 时触发此技能。即使用户未明确说"MPSTATS"，只要意图是按 Ozon 卖家 ID 看该店铺下全部商品的销售表现，也应触发此技能。
linkfox-mpstats-ozon-product-detail
by linkfox-aiMPSTATS Ozon 俄罗斯站 SKU 全量详情批量查询。一次最多传 100 个 Ozon 商品 ID，返回每个 SKU 的价格、折扣、Ozon Card 价、评分、评论数、库存、销量、销售额、潜在销售额/损失销售额、上架日期、图片等完整商品卡。当用户提到 Ozon 商品详情、Ozon SKU 详情、Ozon 价格/评分/销量/库存核对、批量 Ozon SKU 查询、竞品 Ozon 基础数据拉取、Ozon 竞品卡片、MPSTATS Ozon detail, Ozon SKU detail, Ozon product card, Ozon batch lookup, Russian marketplace product detail 时触发此技能。即使用户未明确说"MPSTATS"，只要意图是按 Ozon SKU 拉取全量商品卡数据，也应触发此技能。
linkfox-mpstats-ozon-product-search
by linkfox-aiMPSTATS Ozon 俄罗斯站商品搜索与反查。按俄语关键词或 SKU 在 MPSTATS 数据库中检索 Ozon 商品，返回商品 ID、标题、品牌和卖家信息，是 Ozon 选品与竞品链路的起点。当用户提到 Ozon 选品、Ozon 商品搜索、俄罗斯电商选品、Ozon 关键词搜索、Ozon SKU 查询、MPSTATS Ozon、Ozon product search, MPSTATS Ozon, Russian marketplace, Ozon SKU lookup, Ozon keyword search 时触发此技能。即使用户未明确提到"MPSTATS"，只要其意图是在 Ozon 俄罗斯站按关键词或 SKU 发现或反查商品，也应触发此技能。
linkfox-mpstats-ozon-product-trend
by linkfox-aiMPSTATS Ozon 俄罗斯站单个 SKU 的分日时间序列表现。按日期粒度返回一个 Ozon 商品的销量、价格、库存、评分等指标，可选附带搜索位次/可见性数据，用于验证增长趋势、季节性、异常波动。当用户提到 Ozon 趋势、Ozon 销量趋势、Ozon 价格走势、Ozon 分日数据、Ozon 库存走势、Ozon 搜索位次、Ozon 商品历史、MPSTATS trend, Ozon daily performance, Ozon time series, Ozon search visibility, Russian marketplace product history 时触发此技能。即使用户未明确说"MPSTATS"，只要意图是看某个 Ozon 商品的分日/时间段走势，也应触发此技能。
linkfox-mpstats-ozon-category-products
by linkfox-aiMPSTATS Ozon 俄罗斯站按俄语类目路径下钻该类目全部商品。返回每个 SKU 的销量、销售额、价格、评分、库存、周转、损失销售额等完整指标，支持多维数值筛选、排序、货币换算。用于类目爆款挖掘、蓝海洞察、类目排名分析、品牌格局观察。当用户提到 Ozon 类目下钻、Ozon 类目商品、Ozon 蓝海挖掘、Ozon 品类爆款、Ozon 类目排名、Ozon 子类目结构、Ozon 赛道 SKU、MPSTATS category, Ozon category drill-down, Russian marketplace niche, Ozon niche mining, Ozon subcategory bestseller 时触发此技能。即使用户未明确说"MPSTATS"，只要意图是按 Ozon 类目路径查看该类目下所有商品的销量/价格/排名表现，也应触发此技能。
linkfox-1688-search-by-image
by linkfox-ai1688平台以图搜图，通过商品图片精准检索外观相似或同款的1688货源，返回标题、价格、起批量、月销量、复购率、交易评分等核心数据。当用户提到1688以图搜图、1688找货源、以图找同款、跨境找工厂、1688识图、图片找货源、找相似货源、image search 1688、find supplier by image时触发此技能。即使用户未明确提及"以图搜图"，只要用户提供了图片URL并希望在1688上查找匹配或相似的货源商品，也应触发此技能。
linkfox-amazon-opportunity-screener
by linkfox-ai亚马逊反向选品：基于历史商业洞察报告沉淀的指标数据池，按 30+ 项商业维度（市场规模与增长、价格区间与档位份额、竞争密度与头部集中度、人群画像如年龄/性别/收入、评论卖点与痛点等）反向筛选亚马逊赛道与关键词。当用户提到反向选品、指标筛选、细分市场反查、蓝海赛道挖掘、低竞争赛道、新人友好赛道、品牌分散市场、痛点切入、卖点反查、定价档位机会、人群画像选品、Amazon niche reverse search, niche metrics filter, low-competition niche, blue ocean niche, demographic-based selection, pain-point niche, price tier opportunity, sweet spot pricing, brand fragmentation时触发此技能。即使用户未明确说"反向选品"，只要其需求是按商业维度筛选符合条件的亚马逊赛道，也应触发此技能。
linkfox-ehunt-temu-category-search
by linkfox-ai通过 EHunt Temu 品类检索（网关路由 `ehunt/temu/temuCategorySearch`）在已同步到本地库的 EHunt Temu 类目数据中按关键词检索类目中文名、英文名与类目 id，用于商品/店铺筛选的类目 id。当用户提到 EHunt Temu 类目、Temu category id、Temu 类目树、Temu 后台类目、temu 品类、syncTemuCategory（Temu 品类同步）后查类目、Temu category search 时触发。即使用户未写 EHunt，只要在本地已同步的 Temu 类目库里按关键词找类目 id，也应触发此技能。
linkfox-ehunt-temu-product-query
by linkfox-ai通过 EHunt Temu 商品查询（网关路由 `ehunt/temu/productQuery`）按多维度筛选 Temu 商品（关键词/商品 ID/店铺 ID、前后台类目、价格、评分、评论、总/周/日销量、上架时间、全托管/半托管、半托管地区、标签等）。当用户提到 EHunt Temu 商品、Temu 选品、拼多多跨境、Temu 爆款、Temu 半托管、全托管商品、Temu product query、temu items 时触发。即使用户未写 EHunt，只要在 Temu 上搜商品、看销量/评分/价格或筛品，也应触发此技能。
linkfox-ehunt-temu-store-query
by linkfox-ai通过 EHunt Temu 店铺查询（网关路由 `ehunt/temu/storeQuery`）按多维度筛选 Temu 店铺（店名/ID、国家站点、后台类目、全托管/半托管、总/周/月销量与销售额、评分、评论、粉丝、商品数、开店时间等）。当用户提到 EHunt Temu 店铺、Temu 店铺分析、Temu seller、Temu 店铺排行、Temu 半托管店铺、Temu 销售额、temu stores、Temu store query 时触发。即使用户未写 EHunt，只要在 Temu 上找店铺、筛店铺数据或分析店铺表现，也应触发此技能。
linkfox-ehunt-etsy-product-query
by linkfox-ai通过 EHunt MCP 工具 `_ehunt_productQuery`（展示名「Etsy商品查询」）按多维度筛选 Etsy 商品（关键词/URL、价格、销量、收藏、评论、上架时间、类目、手工/复古等类型、Pick/Bestsell/Raving 等）。当用户提到 EHunt Etsy 商品、Etsy listing、Etsy 选品、Etsy 爆款、Etsy handmade、Etsy vintage、ehunt items、Etsy商品查询、_ehunt_productQuery 时触发。即使用户未写 EHunt，只要在 Etsy 上搜商品、看销量/价格/标签或筛品，也应触发此技能。
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.