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-weekly-report
by alongor666华安保险车险周报自动生成器（麦肯锡风格）。将周度车险数据（支持Excel/CSV/JSON/DuckDB）转化为12-13页董事会级经营分析报告。采用问题导向标题、16:9宽屏、四象限/气泡图可视化、深红#a02724配色。报告结构：经营概览、保费进度、变动成本、损失暴露、费用支出，每个章节分机构和分客户类别双维度分析。支持自定义阈值配置和保费计划。触发场景：用户上传车险周报数据文件（.xlsx/.csv/.json/.db），要求生成董事会汇报PPT。
staff-mapping-management
by alongor666Manage staff-institution mapping table for vehicle insurance platform. Use when updating mapping files, resolving name conflicts, converting Excel to JSON, or checking mapping coverage. Mentions "update mapping", "staff conflicts", "mapping table", or "institution assignment".
weekly-kpi-report
by alongor666Generate McKinsey-style board presentation PPTs from weekly auto insurance data. Automatically calculates 16+ KPIs, creates executive-level slides with actionable insights, and supports week-over-week comparisons. Use when user uploads insurance cost data (Excel/CSV) and requests board report, weekly presentation, executive briefing, or mentions keywords like 董事会汇报, 周报PPT, 经营分析演示, McKinsey-style reports.
css-design-tokens
by alongor666CSS design tokens and color system for vehicle insurance platform. Use when defining colors, spacing, typography, or design variables. Keywords: eye-care colors #5B8DEF/#8B95A5/#C5CAD3, CSS variables, spacing system, color palette, typography scale, design tokens, variables.css, theme colors.
meta-prompt-mentor
by alongor666AI导师框架，通过10+顶级提示工程方法论（C.R.E.A.T.E.、Perfect Prompt Framework、修辞学方法、结构化方法、元提示词理论、迭代优化、个性化定制、逆向思维、系统思维、多模态应用）引导用户创建高效元提示词。当用户需要：(1) 学习提示工程最佳实践，(2) 创建特定任务的元提示词，(3) 优化现有提示词效果，(4) 掌握提示设计思维模式，(5) 建立个性化提示词库，(6) 应用框架解决实际问题，或 (7) 发展AI协作技能时触发。通过'创建元提示词'、'优化提示'、'提示工程指导'、'AI导师'等关键词激活。
chexian-pricing-decision
by alongor666Use when deciding how to price auto insurance business — including commercial vehicle premium quotes, underwriting acceptance decisions, rate level judgments, or evaluating whether specific business segments are worth writing at current market conditions. 当用户问"这单该不该接 / 报多少 / 这价能不能成交 / 某车型某渠道定价决策 / 核保该不该接 / 报这个价合不合适"时触发。面向前瞻报价决策；与 chexian-pricing-redline（已成交业务的反事实定价复盘）相区分。
company-vortex-card
by alongor666把 company-vortex 产出的「{公司}_{代码}_结构诊断.md」做成中国国家地理风格的 12 页视觉卡片 HTML—— 屏幕一屏一页横向翻页，打印为一页一张 A4 PDF。涡旋三才结构 + 物理隐喻 SVG + 印刷级排版铁律。 触发词：视觉卡片、做成卡片、结构诊断卡片、涡旋卡片、xcl_html2pdf、html2pdf 公司、诊断做成 PDF、把诊断报告做成卡片、company vortex card。 机制层（A4 版面/横向翻页/打印/验收）复用 xcl-html2pdf；本 skill 在其之上提供涡旋诊断专属的 12 页模板与填充规范。
chexian-channel
by alongor666Use when evaluating whether to invest in, continue, or exit a distribution channel for auto insurance — including 4S dealers, sub-dealers, brokers, agents, and any channel where fee levels, customer quality, or renewal retention need to be assessed; 或当用户问某渠道"还要不要做 / 能不能进 / 该不该退 / 值不值得投"时。
crystallize-skill
by alongor666把重复性流程 / 动作沉淀（固化）为一个可复用 skill 的元流程编排。 当用户说"沉淀成 skill"、"把这个流程做成 skill"、"固化成技能"、"封装成 skill"、 "crystallize skill"，或当某操作已重复多次、明显值得固化为常态资产时使用。 自动完成：判归属（Git 共享仓库 vs 项目级）→ 查重叠 → 写在唯一事实源 → 发布安装 → 登记。面向 AI-Agent native 用户：用户只下一句指令，纪律由本 skill 自动执行。
chexian-ir-diagnosis
by alongor666Use when diagnosing auto insurance incident rate deterioration, investigating why 出险率 is worsening, or performing root cause analysis on loss frequency (ir = Incident Rate 出险率). Trigger phrases — 分析出险率, 出险率恶化, 出险率诊断, 为什么出险率上升, incident rate drill-down.
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.