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-claim-case-registration
by aliyun"理赔报案受理与案件登记专家。整合报案信息结构化提取、保单状态核验、重复报案检测能力，提取出险时间、事故经过、伤亡情况、保单信息等关键要素，生成标准化报案记录并分配案件编号。当客户拨打报案电话、提交报案申请或上传报案材料时触发。适用于理赔受理岗报案登记、报案信息结构化录入、案件初始档案建立场景。触发词："报案登记"、"案件登记"、"报案受理"、"录入案件"。 触发词：理赔报案、案件录入、重复报案。"
insurance-claim-notification
by aliyun理赔结案通知与客户沟通专家。生成标准化的理赔通知书（赔付/拒赔/补件），并配套口头沟通话术与情绪安抚指导，确保书面函件合规、口头沟通专业。当需要出具理赔结果通知、生成拒赔说明书、通知客户补充材料、告知赔付金额，或需要与客户进行拒赔解释、情绪安抚时触发。适用于理赔结案通知生成、拒赔函件起草、补件通知书制作、客户沟通话术辅助场景。触发词：结案通知、通知客户、理赔通知书。
insurance-agent-policy-explanation
by aliyun为保险代理人提供保单条款解读与产品讲解的专业指导。当需要向客户解释保险条款、解答保障范围疑问、处理理赔相关咨询时使用。帮助代理人用通俗语言解释复杂条款，建立客户信任。 触发词：条款解读、等待期、免责条款。
insurance-underwriting-conclusion-interpretation
by aliyun将专业核保结论转化为客户可理解的通俗话术，并生成标准化的核保结果通知书。内置合规过滤与易混淆点预警，确保客户准确理解核保结论。覆盖通俗话术生成、易混淆点预警、标准体承保通知、加费/除外承保通知、延期核保通知、拒保通知等全流程。当核保评估完成需要向客户解释结论、出具正式核保结论、生成加费/除外条款说明书、通知被保险人补充材料或告知拒保原因时触发。适用于核保结论通俗解读、核保结案通知生成、拒保函件起草、加费/除外承保条件确认书制作场景。 触发词：核保结论、结论解释、结论解读、除外说明、通俗话术。
claims-material-check-accident-insurance-assistant
by aifinlab���û���Ҫ�����������������������ṹ������Ԥ���������Լ�顢�¹����֤�����顢��ҽ����ƥ���顢�����嵥����������ǰ����ʶ��ʱʹ�ñ� skill�������ڼ�����������顢����֤�������п���Ϣ���¹ʾ���˵�����¹�֤����������֤������λ֤�������ﲡ�������ﲡ����סԺ���ϡ����֤������鱨�桢�˲м������������ϵ��Ƿ���ȫ����������Ч���һ�£�������ʺ�����������������ͨ���ڲ����۵ļ������
claims-material-check-critical-illness-assistant
by aifinlab当用户需要对重疾险理赔申请资料进行结构化受理检查时使用本 skill。它用于识别理赔申请书、身份证明、银行卡信息、诊断证明、出院记录、住院病历、病理报告、手术记录、影像报告、专项检查报告等与重大疾病责任认定相关的关键材料是否齐全、清晰、有效、相互一致，并输出补件清单、受理前检查结果和跟进建议。
claims-material-check
by aifinlab当用户需要检查理赔材料是否齐全时使用此 skill。适用于理赔申请前材料预审、缺失材料提醒、材料规范性检查等场景。
Use this skill whenever the user wants to do anything with PDF files — reading, extracting, creating professional documents, filling forms.
file-intel
by julianobarbosaRun the Gemini file processor on any folder — extracts content from PDF, PPTX, XLSX, DOCX, CSV, JSON, and any text format, then generates Obsidian-ready summaries. Use when asked to "summarise this folder", "run file intel", "process these files", or a folder path is provided and summaries are needed.
claim
by FDU-INSComplete insurance and legal claims intelligence system. Trigger whenever someone needs to file, manage, negotiate, or dispute any type of claim: insurance claims, warranty claims, compensation claims, workers compensation, personal injury, property damage, or consumer rights disputes. Also triggers on phrases like "file a claim", "my insurer denied me", "how do I get compensated", "they won't pay", "what am I entitled to", or any scenario where someone has suffered a loss and needs to recover it.
israeli-bituach-leumi
by FDU-INSNavigate Israeli National Insurance (Bituach Leumi) benefits, eligibility, and contributions. Use when user asks about "bituach leumi", national insurance, retirement pension (kiztavat zikna), unemployment (dmei avtala), maternity leave (dmei leida), child allowance (kiztavat yeladim), disability benefits, work injury, reserve duty compensation, or NI contributions. Covers all 15+ Bituach Leumi programs. Do NOT use for private insurance or health fund (kupat cholim) questions.
life-insurance-contract-cancel
by FDU-INS既存の生命保険契約を解約する。解約前に代替プランの提案も行う。
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.