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...
reviewer-visit-memo
by aliyun授信走访观察纪要生成技能。将走访过程中的口述观察整理为六维度专业走访纪要(现场观感/经营判断/人物印象/资金流向/风险信号/与CM沟通结论),支持与客户经理尽调报告、行内数据三方交叉验证,识别经营场所真实性、经营规模匹配度、资金用途一致性等关键风险。支持多轮增量更新、信息修正与针对性追问,生成可直接归档的第一手审批材料。触发词包括:"走访纪要"、"走访记录"、"reviewer visit memo"、"帮我写个走访纪要"、"刚才走访的情况记一下"、"走访纪要生成"、"审批走访记录"。不适用于:贷后风险分类调整、不良资产处置、授信审批决策、客户经理访前分析(请使用pre-visit-credit-analysis)、或无具体走访背景的一般合规咨询。
ota-signal-risk-inspection
by shaoxing-xie信号+风控巡检铁律（env → strategy_config → risk_check）、Tick/分钟降级；模板输出后钉钉投递 tool_send_dingtalk_message(mode=prod)，对齐 workflows/signal_risk_inspection.yaml 与 dingtalk_delivery_contract §巡检快报。
ota-risk-assessment-brief
by shaoxing-xie单标的仓位/止损/波动率风险评估（tool_assess_risk）：ETF/指数/A 股、realized_vol 口径、config 与数据路径；与组合风控 tool_portfolio_risk_snapshot 区分。
update-card-benefits
by lifan-buildsUpdate, add, or remove benefits from existing credit cards in Perks Reminder. Use when the user asks to update card benefits, add a new benefit to a card, remove a benefit, change benefit values, or mentions annual fee updates for cards like Amex Platinum, Chase Sapphire Reserve.
subscription-audit
by WinbdaAudit personal subscriptions. TRIGGERS - Use when user needs help with subscription-audit related tasks.
fivetran-account-info
by fivetranGet a quick overview of the connected Fivetran account.
investigation-first
by AgentWorkers触发：当你准备下判断、做决策或提出建议，但事实、上下文或一手信息还不充分时优先调用；常见信号包括 unknowns、信息缺口、证据不足、领域陌生、需要先摸清现状。 English: Trigger before making claims or decisions when context is incomplete, evidence is weak, or the domain is unfamiliar. Use this skill to investigate first, gather firsthand facts, and let reality shape the conclusion.
windsurf-license-management
by HelixDevelopmentManage Windsurf licenses and seat allocation. Activate when users mention "license management", "seat allocation", "billing optimization", "user licenses", or "subscription management". Handles license administration. Use when working with windsurf license management functionality. Trigger with phrases like "windsurf license management", "windsurf management", "windsurf".
card-wallet
by kmananTrack credit card benefits (use-it-or-lose-it credits) and optimize which card to use per purchase category. Manages both household cardholders.
okx-wallet-portfolio
by pakorn269Use this skill when the user provides a specific wallet address and wants to check its balance, token holdings, portfolio value, or DeFi positions. Typical triggers: 'check balance of 0xAbc...', 'show tokens in this address', 'what tokens does 0xAbc hold', 'portfolio value of this address', address portfolio value, multi-chain balance lookup for a given address. Supports XLayer, Solana, Ethereum, Base, BSC, Arbitrum, Polygon, and 20+ other chains. Do NOT use when the user asks about their own wallet without providing an address (e.g., 'check my wallet balance', 'show my assets', '查看我的余额') — use okx-agentic-wallet instead, which queries the logged-in wallet. Do NOT use for PnL analysis, DEX history, realized/unrealized profit — use okx-dex-market. Do NOT use for signal tracking — use okx-dex-signal. Do NOT use for meme scanning — use okx-dex-trenches. Do NOT use for programming questions about balance APIs or integration.
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.