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...
progressive
by FDU-INSProgressive insurance skill. Compare coverage, get quotes, file claims, and explore savings tools from the #2 auto insurer in the U.S. — known for Snapshot telematics, Name Your Price, and broad multi-product bundling.
afrexai-insurance-claims
by FDU-INSInsurance Claims Processor
analyze-latest-policy-sweep
by FDU-INSAnalyze latest policy analysis sweep runs (*_policy_analysis_*) by comparing episodes/summary metrics, diagnostics, and video artifacts; generate a concise markdown report and optional frame snapshots.
claims-fact-timeline-assistant
by FDU-INS当用户需要梳理保险理赔案件的事实时间线、重建出险到结案的事件顺序、整合病历报案材料审核记录中的时间信息、识别时间冲突或生成适合理赔审核调查复核客服使用的标准化时间轴时使用本 skill。适用于汇总理赔系统案件记录、报案记录、出险说明、门急诊记录、住院记录、手术记录、检查检验报告、病历首页、出院小结、发票或费用清单、补件记录、审核记录、调查记录、结案记录、客服工单记录、沟通备注、OCR 文本、PDF 文档和截图转写内容，提炼关键时间节点、阶段划分、时间间隔、时间冲突和后续核查建议。
claims-material-check-accident-insurance-assistant
by FDU-INS当用户需要对意外险理赔申请资料做结构化受理预审、材料完整性检查、事故相关证明核验、就医资料匹配检查、补件清单整理或受理前问题识别时使用本 skill。适用于检查理赔申请书、身份证明、银行卡信息、事故经过说明、事故证明、公安或交警证明、单位证明、急诊病历、门诊病历、住院资料、诊断证明、检查报告、伤残鉴定、费用资料等是否齐全、清晰、有效、相互一致，并输出适合理赔受理、补件沟通和内部留痕的检查结果。
coverage-liability-matching-2
by FDU-INS当用户需要评估保额与保险责任是否匹配时使用此 skill。适用于保额充足性分析、责任覆盖范围检查、家庭保障需求测算等场景。
coverage-scope-judgment-2
by FDU-INS当用户需要判断某项损失或疾病是否在保险责任范围内时使用此 skill。适用于理赔责任判定、免责条款解读、保障范围咨询等场景。
coverage
by FDU-INSGenerate Coverage and insurance resources with payer diversity including Medicare, Medicaid, commercial, Tricare, VA, and self-pay. Use when user mentions coverage, insurance, payer, Medicare, Medicaid, benefits, or coordination of benefits. For claims and EOB see the claims-eob skill.
eco-link-claims-to-observations
by FDU-INSLink claim-side evidence objects to observation-side evidence objects, score support or contradiction heuristically, and persist a compact claim-observation link artifact for board review and challenge work.
geico
by FDU-INSManage your GEICO insurance policies and claims.
geo-infer-risk
by FDU-INSGeospatial risk modeling including catastrophe models, exposure analysis, and underwriting. Use when assessing spatial risk, building catastrophe models, analyzing exposure/hazard/vulnerability, or computing portfolio risk metrics.
openclaw-0270-policy-scoped-data-mediation
by FDU-INSCollab Privacy Preserving Data Broker. Use when work requires policy-scoped data mediation for Collaboration and Negotiation with guardrails, traceable execution, and measurable outcomes.
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.