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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dbs-report
by dontbesilent2025把多次 dbs-save 攒下来的诊断状态合并成一份可交付的 markdown 报告。 触发方式：/dbs-report、/出报告、「打包」「整理一份」「给合伙人看的」 Generate a deliverable diagnosis report by merging all dbs-save snapshots. Trigger: /dbs-report, "package this up", "make me a report"
dbs-learning
by dontbesilent2025dontbesilent 交互式学习。把一个课题拆成连续学习文章，根据用户在上一篇中的反馈调整下一篇的深度、角度和节奏。 触发方式：/dbs-learning、/dbs-learn、/交互式学习、「带我学一个课题」「继续下一篇」「根据我的反馈写下一篇」 Interactive learning workflow. Builds an adaptive sequence of learning articles based on user feedback. Trigger: /dbs-learning, /dbs-learn, "teach me a topic", "continue the next lesson"
dbs-xhs-title
by dontbesilent2025小红书标题公式工具。从 75 个验证过的爆款公式中，帮你挑对的、用对的、理解为什么用这个。 触发方式：/dbs-xhs-title、/小红书标题、「帮我起个小红书标题」「小红书标题公式」 Xiaohongshu title formula tool. Pick the right formula from 75 proven templates. Trigger: /dbs-xhs-title, "xiaohongshu title", "RED title formula"
dbs-restore
by dontbesilent2025把上次诊断的状态拉出来，接着用。配合 dbs-save 使用。 触发方式：/dbs-restore、/续上、「接着上次」「之前的结论」「上次诊断到哪了」 Restore the most recent diagnosis snapshot saved by dbs-save. Trigger: /dbs-restore, "continue from last time", "where did we leave off"
dbs-save
by dontbesilent2025把当前诊断的关键状态存到本地，下次回来可以接着用。 触发方式：/dbs-save、/存档、「保存这次诊断」「记下来」「这个结论留着」 Save the current diagnosis state to disk for cross-session recall. Trigger: /dbs-save, "save this diagnosis", "remember this"
dbs
by dontbesilent2025dontbesilent 商业工具箱主入口。根据你的问题自动路由到最合适的诊断工具。 触发方式：/dbs、/商业、「帮我看看」 Main entry point for dontbesilent business toolkit. Routes to the right diagnostic skill. Trigger: /dbs, "help me with my business"
dbs-slowisfast
by dontbesilent2025dontbesilent 慢就是快。帮创业者找到看起来更慢但长期更快的方法，用摩擦建造资产。 触发方式：/dbs-slowisfast、/慢就是快、「有没有更慢的方法」「我是不是太快了」 Slow-is-fast diagnosis. Help entrepreneurs find seemingly slower methods that build assets through friction. Trigger: /dbs-slowisfast, "is there a slower way", "am I going too fast"
dbs-action
by dontbesilent2025dontbesilent 执行力诊断。用阿德勒心理学框架诊断你「知道该做什么但就是不做」的真正原因。 触发方式：/dbs-action、/action、「我知道该怎么做但就是不做」「为什么我总是拖延」 Execution block diagnosis using Adlerian psychology framework. Trigger: /dbs-action, "I know what to do but can't do it", "why do I procrastinate"
dbs-agent-migration
by dontbesilent2025Agent 工作台迁移。把任意项目整理成 Claude Code / Codex / Grok 三端一致、可长期维护的 Agent 工作台：审计规则文件、识别真源、统一命名并生成 bridge。 触发方式：/dbs-agent-migration、/agent迁移、「迁移到 Codex」「迁移到 Claude Code」「迁移到 Grok」「统一 AGENTS.md」「整理 skill bridge」「我的 Agent 工作台很乱」「帮我统一 Claude 和 Codex 和 Grok」 Agent workspace migration. Turn any project into a maintainable Claude Code / Codex / Grok three-host workspace by auditing rule files, establishing source-of-truth skills, normalizing names, and generating bridges. Trigger: /dbs-agent-migration, /agent-migration, "migrate to Codex", "migrate to Claude Code", "migrate to Grok", "fix AGENTS.md", "organize skill bridges"
dbs-ai-check
by dontbesilent2025dontbesilent AI 写作特征识别。扫描文案中的 AI 生成痕迹，输出检测报告。默认只诊断不改。 触发方式：/dbs-ai-check、/AI检测、「帮我看看有没有 AI 味」「检测一下 AI 特征」 AI writing fingerprint detection. Scans copy for AI-generated patterns and outputs a diagnostic report. Diagnosis only by default. Trigger: /dbs-ai-check, "check for AI writing", "does this sound like AI"
dbs-benchmark
by dontbesilent2025dontbesilent 对标分析。用五重过滤法帮你找到值得模仿的对标，排除一切关于「我」的噪音。 触发方式：/dbs-benchmark、/对标、「帮我找对标」「我该模仿谁」 Benchmark analysis using dontbesilent's five-filter method. Trigger: /dbs-benchmark, "find me a benchmark", "who should I copy"
dbs-chatroom-austrian
by dontbesilent2025哈耶克 × 米塞斯 × Claude 三人对话。奥派经济学视角的多角色讨论。 触发方式：/dbs-chatroom-austrian、/chatroom-austrian、/奥派、「奥派聊天室」 Austrian economics chatroom. Hayek × Mises × Claude debate. Trigger: /dbs-chatroom-austrian, /chatroom-austrian, /奥派, "Austrian chat"
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.