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...
about-atlantic-home-mortgage
by davepoonBackground information about Lendtrain powered by Atlantic Home Mortgage — company history, credentials, founder bio, and contact information for borrower trust-building.
insurance-agent-activity-planner
by aliyun为保险代理人提供每日/每周/每月展业活动计划制定的专业技能，帮助代理人科学管理拜访量、跟进节点和业绩目标。当代理人需要制定展业计划、管理客户拜访节奏、追踪业绩达成进度、分解月度目标时触发。适用于月初目标分解、周计划制定、每日行程安排、业绩落后时的追赶计划等场景。 触发词：今日复盘、明日计划、拜访安排。
insurance-agent-client-consulting
by aliyun为保险代理人提供客户咨询与需求分析的专业指导。当进行客户初次沟通、需求挖掘、家庭情况了解、风险偏好评估时使用。帮助代理人以专业、温暖的方式建立信任，准确识别客户真实需求。 触发词：客户咨询、挖掘需求、客户想了解。
insurance-agent-health-disclosure-guide
by aliyun为保险代理人提供健康告知填写指导与疑难解析的专业技能，帮助代理人引导客户如实、正确地完成健康告知，避免隐瞒重要信息导致日后理赔纠纷。当客户有既往病史或健康状况需要告知、健康告知问卷有疑难问题不知如何填写、担心健康告知影响核保结果时触发。适用于有既往病史的客户投保前健康告知填写、健康告知疑难问题解答、如实告知原则宣导等场景。 触发词：健康告知、结节怎么告知、告知引导。
insurance-agent-objection-handler
by aliyun为保险代理人提供客户异议处理话术与策略的专业技能，帮助代理人有效应对常见拒绝理由，化解疑虑并推进成交。当代理人遇到客户提出保费太贵、不需要、再考虑考虑、没时间等异议，或在方案呈现后遭遇沉默、推拖时触发。适用于需求分析阶段的初期抵触、方案呈现后的价格异议、促成阶段的最后疑虑处理等场景。 触发词：太贵了、再考虑、不需要。
insurance-agent-product-wiki
by aliyun保险代理人产品知识库与销售助手。上传产品文档自动入库，以LLM Wiki理念为每个产品建立独立知识页。集成学习问答、模拟演练、话术推荐三大模式。当用户提到保险产品、产品入库、话术推荐、模拟演练、学习产品、保险问答时使用此技能。 触发词：产品保障什么、话术演练、重疾和医疗区别。
insurance-agent-renewal-assist
by aliyun为保险代理人提供保单续期提醒、续期维护话术和客户防流失策略的专业技能，帮助代理人提高客户续期率，减少因欠费导致的保单失效。当客户保单即将到期需要续期、客户迟迟未缴纳续期保费、需要做续期前保单健康检查时触发。适用于续期前30天提醒、欠费催缴、考虑退保时的挽留，以及续期时借机加保等场景。 触发词：保单到期、续期话术、退保挽留。
insurance-agent-social-media
by aliyun扮演保险社交媒体营销专家。为保险代理人自动获取热点新闻，生成高质量内容，一键确认后通过Playwright自动发布，帮助代理人打造专业且有温度的个人IP。 触发词：保险热点、营销文案、小红书笔记。
insurance-underwriting-follow-up-outreach
by aliyun主动联系客户进行回访，支持电话拨打与消息发送，基于客户反馈识别真实意图（确认/咨询/投诉/退保/加保），输出结构化标签与跟进建议。当需要回访客户、拨打回访电话、发送回访消息、识别客户意图时触发。适用于保险核保后送达回访、客户意图识别、客户关怀跟进场景。 触发词：回访客户、打电话、发消息、客户意图。
client-care-script
by aliyun生成客户关怀话术，覆盖生日祝福、节日问候、亏损安抚、流失挽留场景。 侧重情感维护和客户关系保持。 当用户提到安抚客户、亏损沟通、生日祝福、节日问候、流失挽留、客户情绪不好时触发。 不用于主动营销触达（由outreach-script-generator处理）。
agent-business-development
by aifinlab当保险代理人需要展业支持时使用此 skill。适用于客户开拓、产品讲解、方案制作、异议处理、成交促成等场景。
auto-insurance-recommendation
by FDU-INS顧客の年齢・年収・プロフィールに基づき、最適な自動車保険プランを提案する。
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.