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...
googleads-data-acquisition
by palu89Google Ads 数据采集层。跨账户数据采集经验总结——通过 Composio 拉取 Google Ads 账户数据、系统分析、记录学习的完整工作流。基于 14 个账户、~$190K/月花费、96% 浪费率的经验提炼。适用于新账户接手、全量数据拉取、跨账户分析。
googleads-field-operations
by palu89Google Ads 全域运营总控。前置聚合（门4全量巡检+门2 quick-scan+账户数据拉取）→ 门4→门5→门6→门7 串联调度 → 跨账户上下文切换。
googleads-verify
by palu89Google Ads 金融服务广告主身份验证。G2 验证流程、文件准备、验证申诉、金融牌照要求。
multi-agent-googleads
by palu89Google Ads 多 Agent 协作三阶段协议。Researcher→Executor→Reviewer。T4-T7 复杂任务自动提示。
googleads-copy-generator
by palu89Google Ads 响应式搜索广告(RSA)文案生成。自动加载合规规则，生成标题≤28字符、描述≤75字符的广告文案。输出表格格式含字符数判定。用于 T1 文案任务。
googleads-account-deploy
by palu89新 Google Ads 账户快速部署。加载最佳策略、种子词库、否定词库、低成本词库，输出新账户配置清单。用于接手新账户或替代冻结账户。
googleads-keyword-review
by palu89Google Ads 搜索词/关键词审查与判定。执行硬阻断墙检查、意图簇评分、AI Max Gate 判断，输出六类判定（Protected/Seed/Observe/SoftNeg/HardNeg/LP-Gap）。用于 T2 关键词任务。
googleads-landing-audit
by palu89Google Ads 落地页质量评估。从美国本土用户审美和转化习惯出发，检查首屏清晰度、CTA明确性、文案长度、信任元素。输出按"必须删/建议删/建议保留/建议重做"四级。用于 T3 落地页任务。
googleads-appeal
by palu89Google Ads 账户违规申诉。违规类型判定(A/B/C/D四类)、申诉可行性算法、账户状态机、解封后行为约束。
googleads-appealtxt
by palu89Google Ads 账户申诉文案生成。根据违规类型和申诉模板生成符合 Google 政策的英文申诉书。
googleads-audit
by palu89Google Ads 落地页合规全维度审计。四级风险分层(禁用词/市场/业务实质/技术合规)、政策风险、页面一致性检查。
cloudweaver-landing
by palu89Google Ads 七门协同 — 门3。落地页与关键词/业务线匹配度检查。追踪字段完整性。有效客户反馈闭环。quick-match（日常）→ full-match（入库/起跑/LP-Copy Gap）。
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.