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...
afa-fb
by afadtcMeta 广告优化引擎——Facebook/Instagram 广告账户结构、受众策略、出价优化、创意测试、Advantage+ 、CBO/ABO。Use when user mentions: Facebook广告, Meta Ads, Instagram广告, FB广告, CPM, CPA, 受众定向, targeting, Advantage+, CBO, ABO, 广告组, ad set, 像素, pixel, ROAS, Meta投放.
afa-paid
by afadtc付费获客 Supervisor——统筹 Meta/Google/TikTok 广告与创意生产的跨渠道预算分配、策略协同与路由。Use when user mentions: 付费广告, paid ads, 广告投放, 预算分配, budget allocation, 跨渠道广告, 广告策略, ad strategy, 获客成本, CAC, 广告效果, 投放优化, 多渠道广告.
afa-tt
by afadtcTikTok 广告优化引擎——TikTok Ads 账户结构、受众策略、创意方向、TikTok Shop、达人合作。Use when user mentions: TikTok, 抖音广告, TikTok Ads, TikTok Shop, 短视频广告, Spark Ads, 达人广告, creator ads, TikTok投放, TikTok开店, TikTok营销.
afa-ops
by afadtcDTC 运营与供应链优化引擎——仓储物流、库存管理、3PL 选择、发货时效、供应商管理。Use when user mentions: 运营, operations, 供应链, supply chain, 仓储, warehouse, 物流, logistics, 发货, shipping, 库存, inventory, 3PL, 履约, fulfillment, 供应商, supplier, 运营效率.
afa-scale
by afadtc运营与扩张 Supervisor——统筹供应链运营与渠道扩张的路由与协同，管辖 afa-ops 与 afa-expand。Use when user mentions: 扩张, scale, 运营优化, operations, 供应链, supply chain, 新市场, 渠道扩张, channel expansion, 规模化, scaling, 多市场运营.
afa-foundation
by afadtc品牌与产品基建 Supervisor——统筹市场探索、竞争情报、品牌定位、产品策略、产品上市的全流程路由与协同。Use when user mentions: 品牌建设, brand building, 产品规划, product planning, 从零开始, from scratch, 品牌基建, foundation, 市场调研, market research, 品牌定位, brand positioning, 产品上市, product launch.
afa-launch
by afadtcDTC 产品上市与冷启动引擎——四阶段启动计划、MVP 广告测试、PMF 判定、新品冷启动策略、市场验证。Use when user mentions: 上市, launch, 冷启动, cold start, 新品, new product, PMF, product-market fit, MVP, 市场验证, validation, 启动计划, go-to-market, 新品上架, 首发.
afa-brand
by afadtcDTC 品牌定位与识别引擎——品牌定位画布、品牌声音架构、品牌故事、视觉识别系统、品牌健康审计。Use when user mentions: 品牌定位, brand positioning, 品牌声音, brand voice, 品牌故事, brand story, 视觉识别, visual identity, 品牌策略, brand strategy, 品牌画布, 品牌建设, 品牌升级, 品牌规范, brand guidelines.
afa-retain
by afadtcDTC 品牌用户留存与 LTV 增长引擎——留存健康体检、RFM+LTV 分层、微诚度计划设计、订阅防流失、召回体系、群组分析。Use when user mentions: 留存, retention, 复购率, repurchase, LTV, 客户生命周期, customer lifetime value, 流失, churn, 微诚度, loyalty, 会员, membership, 订阅, subscription, 召回, win-back, 沉睡客户, 再激活, reactivation, RFM分层.
afa-geo
by afadtcAI 搜索可见度与本地化搜索引擎——AEO/GEO 策略、AI 搜索优化、结构化数据、多语言本地化、hreflang 策略。Use when user mentions: AI搜索, AI search, GEO, AEO, 结构化数据, structured data, 本地化, localization, hreflang, 多语言, multi-language, ChatGPT搜索, Perplexity, AI推荐, AI可见度, 本地SEO, local SEO.
afa-aov
by afadtcDTC 客单价提升与利润优化引擎——捆绑策略、Upsell/Cross-sell、定价心理学、产品组合优化、订单价值分析。Use when user mentions: 客单价, AOV, 提升客单价, average order value, upsell, cross-sell, 捆绑, bundle, 定价, pricing, 利润, profit margin, 订单价值, 折扣策略, discount strategy, 免邮门槛.
afa-convert
by afadtcDTC 全链路转化率优化引擎——转化漏斗分析、结账流程优化、弃单挽回、落地页优化、A/B测试、信任元素。Use when user mentions: 转化率, conversion rate, CRO, 加购率, add to cart, 结账, checkout, 弃单, abandoned cart, 落地页, landing page, A/B测试, 信任度, trust, 购买流程, 转化漏斗, funnel.
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.