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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insurance-agent-gap-advisor
by aliyun保险保障缺口分析与配置建议专家。基于用户画像、家庭责任、财务状况、健康信息及现有保单持仓，识别医疗、重疾、寿险、意外等保障缺口，判断配置重复与错配，生成带有完整计算过程和推理依据的可解释性保险配置建议报告（DOCX/PDF）。当用户提及保障缺口、保险分析、保单诊断、保险配置建议、保障规划时触发。 触发词：保障缺口、加什么保、缺口扫描。
insurance-agent-plan-generator
by aliyun保险计划书生成器。根据客户画像、保障缺口分析和产品组合推荐，自动生成专业Word(.docx)格式保险计划书。支持从客户画像技能和产品推荐技能导入数据，也支持独立手动录入。自动填充客户信息、产品方案、利益演示，生成合规精美的个性化计划书。当用户提到计划书、保险方案、方案书、规划书、生成计划书、制作方案、保障方案、保障规划时使用。 触发词：计划书、出方案、生成方案。
insurance-underwriting-health-verification
by aliyun对投保申请进行全面核保健康审核，涵盖客户自述语音解析、术语标准化、健康告知逐项核对与疾病核保规则匹配。对健康告知及客户自述语音进行语义解析、术语标准化与交叉矛盾校验，基于多维度风险评分（健康/财务/家庭）、风险因素加权评估，输出综合风险等级与核保结论建议。当需要审核投保申请、评估投保风险、判断是否可承保、核对健康告知、评估客户风险等级时触发。适用于核保人员新单审核、代理人投保预检、健康告知评估、客户风险画像生成场景。 触发词：健康告知、客户自述、告知矛盾、术语标准化。
insurance-underwriting-medical-assessment
by aliyun对被保险人医疗材料进行全面核保医学评估，涵盖体检报告深度解析、病历与病理材料结构化提取、异常指标核保风险判定、多疾病交叉推理及核保结论输出。当需要解读体检报告以支持核保决策、评估体检异常指标的核保影响、解析病历/病理材料、判断是否需要要求加做体检项目、输出含具体加费比例与除外责任描述的核保结论时触发。适用于健康险/寿险核保中的体检报告评估、病历风险分析、加费/除外/拒保依据分析、多疾病交叉核保推理、被保险人医疗结果核保咨询场景。 触发词：体检报告、病历风险、异常指标。
insurance-underwriting-recording-inspection
by aliyun对销售双录音视频进行全量转写、话术合规比对、说话人分离与情绪异常标记。当需要质检双录音视频、检查销售话术是否合规、识别双录中的情绪异常时触发。适用于保险销售双录质检、话术合规审核、监管合规管理场景。 触发词：双录质检、双录问题、话术合规。
fucai3d-latest
by OpenMinis查询最近一次中国福利彩票3D开奖结果时使用。适用于用户要查福彩3D最新开奖、最近一期号码、百度搜索“3d/福彩3d”第一个结果、或快速返回期号日期号码的场景。也适用于在查询后记录历史结果，并基于历史记录给出娱乐性质的下一次建议号码、冷热号分析与多策略号码推荐。
underwriting-questionnaire-review
by aifinlab当用户需要审查、分析或填写保险核保问卷时使用此 skill。适用于投保人告知义务审查、健康告知完整性检查、职业风险等级评估、问卷逻辑一致性验证等场景。
accident-insurance-underwriting-questionnaire-assistant
by aifinlab当用户需要对意外险投保问卷进行专业、结构化的核保审查，提取职业类别、工作环境、作业内容、危险活动参与情况、交通出行习惯、特殊工作暴露及其他可能影响意外险承保判断的高风险信息，并生成适合意外险核保、补问流转和风险分层处理的结构化分析结果时使用本 skill。
annuity-insurance-underwriting-questionnaire-assistant
by aifinlab当用户需要对年金险投保问卷进行专业、结构化的核保审查，提取健康告知、既往病史、职业信息、高额投保、缴费能力、资金来源、既有保单配置、投保目的及其他可能影响年金险承保判断和财务合理性判断的高风险信息，并生成适合年金险核保、补问流转和财务合理性分层处理的结构化分析结果时使用本 skill。
global-investor-suitability
by aifinlab用于投资者适当性场景。适用于金融工作中的基础任务单元。
bank-t121-corporate-finance-creditpre-screen-assistant
by aifinlab当用户需要在银行对公金融场景下，对企业授信申请做贷前初筛、材料完整性检查、准入红旗识别、补件清单整理、访谈问题设计或初筛意见输出时使用本技能。适合输出结构化初筛结论、风险提示、待核验事项和下一步推进建议。
bank-t163-retail-finance-credit-bureau-interpretation-assistant
by aifinlab用于银行零售金融场景的征信报告解读与风险要点梳理，当需要将征信指标翻译为业务可执行的关注点与追问方向时触发。
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.