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...
jz-commit-code
by jinzheio当用户要求 review 并提交本地工作区变更时使用，包括 commit this、帮我 commit、确认提交、split these changes into commits。必须优先分派 worker subagent 在独立 context window 中执行 review 与提交流程。先 review diff 并报告风险，等待用户明确确认，再按功能创建干净的 scoped commits。除非用户同时要求 push，否则不要推送。
jz-check-cloud-agent
by jinzheio诊断和运维云端 agent 部署。登录服务器检查 OpenClaw agent 和 Hermes agent 的运行状态，排查 Telegram 消息异常、cron job 故障、Codex OAuth 过期、home channel 丢失、support 邮件接收，并通过 SSH 隧道打开远程 Chrome 桌面。触发词包括：agent 不工作、agent 没回复、hermes agent、codex 用量、fallback、cron job 失败、Codex OAuth、home channel、gateway、打开远程桌面、远程 Chrome、noVNC、support 邮箱、客户邮件、clawsimplesupport 群。当问题涉及 Hermes agent 处理邮件或群内回复时，优先按 Hermes 诊断，不要默认改走 OpenClaw support-inbox cron。
jz-build-personal-context
by jinzheio当用户想通过访谈、苏格拉底提问或交互式方式创建或更新 about.md、voice.md、anti-style.md，并让 Codex、ChatGPT、Claude、Claude Code 使用这些个人上下文文件时使用。触发语包括 create about me、update aboutme、review personal context、voice profile、anti AI writing style、个人上下文、写作风格文件、全局 AI profile、-g 自动开启。用户在对话中途要求更新 aboutme 文件时，也用本 skill 回顾当前 session 并判断各文件是否需要更新。不是用于写单篇文章或普通润色。
jz-audit-vercel-cost
by jinzheio当需要读取、解释或反推 Vercel 账单、信用卡扣款、usage credit、effectiveCost、billedCost、Pro 固定费、Build Minutes 成本时使用。适用于核对 Vercel receipt/card charge、把 vercel usage 解析成准确应付金额、区分 Pro 平台费与 non-Pro 额外用量。
jz-audit-cf-cost
by jinzheio当需要读取、解释或核对 Cloudflare 账单、usage、计费周期费用、运行中资源成本时使用。适用于查 Cloudflare Dashboard Billable Usage、用 GraphQL Analytics API 查当前计费周期用量、评估 Durable Objects/Workers/D1/R2/KV/Queues 等付费资源的成本、发现异常计费。
jz-add-search-index
by jinzheio当用户想给公开 Web 应用、Next.js 站点或静态站点增加 IndexNow 支持时使用：创建 key 验证文件、URL 收集脚本、URL 提交脚本、文档和验证流程。触发语包括 add IndexNow、submit changed URLs、set up post-push URL submission。不要单独用于 Google Search Console 或 Bing Webmaster onboarding；没有公开 URL 的仓库也不要使用。
jz-add-gh-collaborator
by jinzheio当用户在本地/admin 侧要求为 OpenClaw、Hermes 或 cloud agent 增加 GitHub agent 合作者时使用。未提供 agent 账号时，直接使用本机未跟踪配置中的默认 owner 与 agent GitHub 账号；也支持用户临时指定账号。只设置 GitHub 权限边界：agent fork 可写、upstream 只读。不要用于 Vercel、Neon、服务器 clone、运行时登录、push、preview 或 PR 实作。
jz-setup-site-analytics
by jinzheio当公开站点已经在最终自定义域名上线，且用户要求接入 analytics、baseline metrics、Search Console、sitemap submission、IndexNow、Bing Webmaster Tools 或 Clarity 时使用。触发语包括 set up GSC、connect analytics、do indexing onboarding、域名好了，接搜索和统计。不要用于初始部署或 DNS cutover；先用 create-site 或 launch-domain。
jz-push-code
by jinzheio当用户要求验证并推送仓库时使用，包括 push this、发布代码、推送到远端。必须优先分派 worker subagent 在独立 context window 中执行验证、提交和推送流程。运行适用检查，确保目标变更已提交，然后 push。Cloudflare 公开站点优先使用 GitHub Actions 自动部署；只有缺 workflow 时才读取自动部署 reference 并补齐。用 [skip deploy] 跳过部署。部署完成后再执行 IndexNow。后端仓库、私有工具、API-only 改动或没有公开 URL 的改动不要运行 IndexNow。
jz-migrate-to-cf
by jinzheio将 Web 项目从 Vercel 迁移到 Cloudflare Workers、Pages 或 Cloudflare 原生托管。适用于用户要求迁移、发布、重新部署或把站点从 Vercel 移到 Cloudflare，尤其涉及自定义域名、Vercel GitHub 自动部署、Vercel 平台资源、vercel.json、Cloudflare DNS、Workers custom domains、D1、R2、Queues、Vectorize、Wrangler 或域名交接时。
jz-launch-domain
by jinzheio当用户已有可用部署、想连接自定义域名时使用，包括 connect this domain、绑定域名、set up DNS、make www redirect、enable HTTPS。覆盖 registrar nameservers、DNS provider records、hosting-platform domain binding、TLS、apex/www 跳转，以及用户要求时的邮箱转发。站点还没有可用部署时不要使用；先用 create-site。
jz-find-revenue-site
by jinzheio获取与某个网站相似的高收入网站，或获取最近出现的高收入网站。用于用户说"获取与 xxx 相似的高收入网站""找 xxx 的相似站点里收入高的网站""查看 Similarweb 相似站点""查看 TrustMRR 同类型站点""获取最新的高收入网站""找最近赚钱的网站""按流量/收入阈值筛网站"时。默认结合 Similarweb、Semrush、TrustMRR、域名注册时间和公开页面证据。
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.