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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maturity-client-alert
by aliyun针对理财产品到期或即将到期的客户，生成承接方案， 包含到期产品分析、替代产品推荐和承接话术。 当用户提到到期承接、产品到期、客户续投、封闭期结束、理财到期时触发。 不用于一般产品推荐（由smart-product-matching处理）。
hot-product-briefing
by aliyun将主推产品转化为卖点话术和FAQ应答，包含产品亮点、 适配客群画像、常见异议应对。 当用户提到主推产品、产品介绍、怎么跟客户讲这个产品、卖点提炼、产品话术时触发。 不用于产品筛选推荐（由smart-product-matching处理）。
market-event-interpreter
by aliyun将市场突发事件翻译成客户听得懂的语言，生成事件简析、 对持仓影响评估和建议行动话术。 当用户提到市场大跌怎么说、政策怎么解读、波动话术、热点事件影响、 突发事件怎么跟客户解释时触发。 不用于常规行情查询（由market-insight-qa处理）， 不用于生成完整早报（由market-morning-brief处理）。
outreach-script-generator
by aliyun结合市场行情和客户持仓情况，生成个性化电话触达话术， 覆盖资讯同步、风险提醒、产品到期承接、服务邀约等场景。 当用户提到打电话话术、触达话术、通联话术、电话联系客户、怎么跟客户打电话时触发。 不用于客户安抚挽留场景（由client-care-script处理）。
wechat-moments-creator
by aliyun为理财经理创作专业且有温度的企微/朋友圈营销内容， 支持市场点评、产品解读、投教科普、节日关怀四种风格。 当用户提到朋友圈文案、企微内容、私域运营、营销内容、发条朋友圈时触发。 不用于电话触达话术（由outreach-script-generator处理）。
sales-sop-navigator
by aliyun基于客户所处投资阶段，提供标准化销售SOP步骤引导， 包括开场白、需求挖掘、方案呈现、异议处理、促成话术的完整流程指导。 当用户提到销售流程、SOP、成交技巧、面谈流程、怎么谈客户、 需求挖掘、异议处理时触发。 不用于生成具体产品话术（由hot-product-briefing处理）。
smart-product-matching
by aliyun从客户配置缺口出发筛选匹配产品，经尽调验证后输出推荐方案和 "为什么适合您"的销售话术。支持基金、理财产品、债券等多品类匹配。 当用户提到推荐产品、该买什么、产品匹配、有什么好产品、推荐基金、理财产品推荐时触发。 不用于已持有产品的诊断分析（由portfolio-health-check处理）， 不用于资产配置方案设计（由asset-allocation-optimizer处理）。
polymarket
by CoWork-OSQuery Polymarket prediction markets — search events, check odds and prices, view trending markets, track price momentum, get orderbook depth, analyze volume, and monitor market resolution timelines. Use when the user asks about prediction markets, betting odds, event probabilities, or Polymarket data.
twitter-posting
by datagridSolutionPost trading signals, market analysis, and performance updates to Twitter/X automatically
polymarket-fast-loop
by dvcrnTrade Polymarket BTC 5-minute and 15-minute fast markets using CEX price momentum signals via Simmer API. Default signal is Binance BTC/USDT klines. Use when user wants to trade sprint/fast markets, automate short-term crypto trading, or use CEX momentum as a Polymarket signal.
polyclawster-agent
by dvcrnTrade on Polymarket prediction markets. Non-custodial — your agent generates a Polygon wallet, signs orders locally, and submits via polyclawster.com relay (geo-bypass). Private key never leaves your machine. Fund with POL — agent auto-swaps to USDC.e.
search-investors
by cartaSearches for and retrieves investor records from the Carta CRM. Use this skill when the user says things like "find an investor", "search investors", "look up an investor", "show me investor details for [name]", "get investor by ID", "list investors", "what investors do we have", or "/search-investors". Returns investor details including ID, name, and custom fields. The investor ID returned can be used with the update-investor skill.
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.