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.
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Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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elder-system
by chess99Elder 技术交易体系入口。基于亚历山大·埃尔德《以交易为生》的完整交易系统。 触发词："用 Elder 分析 [标的]"、"三重滤网分析"、"技术面分析"、"这个位置能进吗"、 "今天有什么技术信号"、"Elder 体系"、"帮我做技术交易分析"。 输出：从信号扫描到仓位计算到交易指令的完整流程。
canslim-elder-confirm
by chess99CANSLIM 体系的技术面确认步骤。在 CANSLIM 基本面评分通过后，用于确认当前是否为最佳入场时机。 此 skill 是 elder-screen 的裁剪版，只执行第二滤网（日线动量）和第三滤网（精确入场）， 不执行第一滤网（周线趋势），因为 CANSLIM 基本面研究已替代了趋势过滤的作用。 通常由 canslim-system 在基本面评分通过后调用，不单独使用。
elder-screen
by chess99基于亚历山大·埃尔德《以交易为生》的三重滤网系统，对股票/期货/外汇进行结构化技术分析。 当用户说"帮我分析一下这只股票"、"看看这个标的值不值得买"、"用埃尔德方法分析"、 "做个技术分析"、"这只股票能买吗"、"我想买XXX，帮我看看"、"分析一下行情"、 "这个位置能进吗"、"帮我做个三重滤网分析"时触发。 输出结构化的多维度分析报告，包含趋势判断、入场信号、止损/止盈价位和仓位建议。
elder-position-monitor
by chess99Elder 体系持仓监控。追踪价格止损线，识别需要调整或平仓的头寸。 当用户说"检查 Elder 持仓"、"有没有触及止损"、"需要调整止损吗"、 "帮我追踪止损"、"更新止损线"时触发。 只处理价格止损持仓（Elder 体系），不处理逻辑止损持仓。 输出每个持仓的当前状态、止损建议，以及需要立即处理的紧急情况。
trade-executor
by chess99将分析结果和仓位计算转化为具体的交易指令清单，支持人工确认模式和（未来）自动执行模式。 当用户说"生成交易指令"、"帮我下单"、"生成今日操作清单"、"把分析结果转成指令"、 "告诉我具体怎么操作"时触发。 通常由 elder-system 在完成信号分析和仓位计算后调用。 输出清晰的交易指令清单，包括入场单、止损单、止盈单的具体参数。
canslim-position-monitor
by chess99CANSLIM 体系持仓监控。支持双模式止损：初期价格止损（7-8%），盈利后逻辑止损。 当用户说"检查 CANSLIM 持仓"、"帮我更新逻辑止损"、"持仓基本面有没有变化"时触发。 只处理 CANSLIM 体系的持仓，不处理 Elder 体系或 Value Investing 体系的持仓。
canslim-screen
by chess99基于威廉·欧奈尔《笑傲股市》的 CANSLIM 方法，对股票进行基本面评分和筛选。 当用户说"这只股票基本面怎么样"、"帮我筛选成长股"、"这股票值得长期持有吗"、 "用欧奈尔方法分析"、"CANSLIM 评分"、"基本面好不好"、"有没有真实业绩支撑"、 "这是在炒梦还是真的有基本面"时触发。 通常在 elder-screen 之前运行（先确认值不值得关注，再看入场时机）， 也可以单独使用做基本面筛选。 输出 CANSLIM 七维度评分、综合结论，以及是否建议进入技术面分析。
canslim-trading-journal
by chess99CANSLIM 体系交易记录与复盘。记录成长股交易，追踪基本面变化，评估 CANSLIM 评分的预测准确性。 当用户说"记录 CANSLIM 交易"、"更新成长股交易记录"、"CANSLIM 复盘"时触发。 与 Elder 体系的 trading-journal 独立，格式和复盘维度不同。
position-sizer
by chess99精确计算每笔交易的仓位大小，整合两套独立框架： 埃尔德的2%/6%原则（简单保护性上限）和撒普的风险/波动率百分比模型（复利增长导向）。 当用户说"帮我算仓位"、"这笔交易能买多少"、"计算仓位"、"我有X万，止损在Y，能买多少股"、 "检查一下6%原则"、"还有多少可用风险额度"、"这笔交易风险多大"、"期望收益是多少"时触发。 通常由 elder-system 在确认交易信号后调用，也可以单独使用。 输出精确的仓位数量、风险金额、月度可用风险余额，以及是否符合风险原则的判断。
signal-scanner
by chess99批量扫描候选标的池，用三重滤网第一滤网快速筛选，输出有交易信号的标的清单。 当用户说"扫描市场"、"看看今天有什么机会"、"帮我筛选标的"、"有哪些股票值得看"、 "跑一下信号扫描"、"今天哪些标的有信号"时触发（模式 A：用户提供候选池）。 当用户说"读取 Elder 研究运行结果"、"分析昨天的 Elder 候选"时触发 （模式 B：读取 artifacts/runs/{run_id}/manifest.json 和 report.md）。 通常由 elder-system 调用，也可以单独使用。 输出按信号强度排序的候选清单，供 elder-screen 做进一步深度分析。
trading-journal
by chess99记录每笔交易的完整信息，生成绩效统计报告，帮助从历史交易中学习和改进。 当用户说"记录这笔交易"、"帮我做交易复盘"、"更新交易日志"、"生成绩效报告"、 "这笔交易赚了X亏了Y"、"交易记录"、"看看我的交易统计"时触发。 通常由 elder-system 在交易完成后调用，也可以单独使用。 输出标准化的交易记录，以及定期的绩效分析报告。
trading-system
by chess99交易系统导航入口。当用户意图不明确时触发，识别用户想做什么，然后推荐对应的体系。 触发词："分析一下这只股票"、"帮我看看600000"、"今天有什么机会"、"交易系统"、 "不知道用哪个方法"、"帮我决定用哪套体系"。 注意：如果用户意图已经明确（如"用 Elder 分析"、"CANSLIM 评分"、"估值分析"）， 直接触发对应体系的入口 skill，不需要经过本 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.