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...
create-lab-guide
by dockerClone a dockersamples Labspace repo, extract learning objectives and module structure from labspace.yaml, and produce a Hugo guide page under content/guides/ with correct frontmatter, labspace-launch shortcode, and Docker docs style compliance. Use when asked to create a lab guide, write a Labspace page, add a Docker lab tutorial, migrate a lab to docs, or document a hands-on lab.
paper-select-journal
by huangwb8当用户明确要求“推荐投稿期刊”“帮我的论文选 SCI 杂志”“这篇 manuscript 适合投哪些 journal”“期刊匹配/选刊/投稿建议”时必须使用。适用于用户提供全文、摘要、Markdown、LaTeX、PDF、Word 或混合材料的场景；本 skill 会基于 manuscript 与用户偏好，先用内置 `2023IF.xlsx` 做最小硬过滤生成候选池，再由宿主模型自主规划 Set1/Set2/Set3，并联网核验 scope / 质量 / 近 3 个月 PubMed 论文，最后输出 1 份按推荐度排序的 Markdown 选刊报告。⚠️ 不适用：用户只是想润色论文、只想翻译摘要、或只问某个单一期刊的官网信息而不需要系统选刊。
wang-lao-shi-demo
by agenmod蒸馏王老师的指导方式与核心教诲。用于延续导师的教学智慧与指导风格，辅助学习与回顾。
office-academic-skill
by zLanqingChinese-first academic Word and PowerPoint workflow for paper reading reports, thesis or group-meeting PPTs, editable DOCX/PPTX generation, Office file inspection, template matching, speaker notes, and layout quality checks. Use when the user asks to read papers into Word reports, create or polish PPT/PPTX, convert paper/thesis materials into slides, edit DOCX/PPTX, inspect Office files, or produce Chinese academic presentation/report deliverables. Preserve English paper titles, formulas, variable names, software commands, and references.
diffity-learn
by nilbuildStart a project-driven learning journey for any technical topic — programming languages, tools, frameworks, or concepts. Teaches through real projects, Diffity tours, and interactive conversation, adapting to the learner's pace.
diffity-learn
by nilbuildStart a project-driven learning journey for any technical topic — programming languages, tools, frameworks, or concepts. Teaches through real projects, Diffity tours, and interactive conversation, adapting to the learner's pace.
beamer-compile-qa
by WILLOSCARCompile the Beamer tutorial deck and write a build report. **Trigger**: beamer compile, slides compile, tutorial slides pdf, 编译幻灯片, beamer pdf. **Use when**: `source-tutorial` 的 C4，已有 `latex/slides/main.tex`，需要输出 `latex/slides/main.pdf` 和编译报告。 **Skip if**: slides scaffold 还没完成。 **Network**: none. **Guardrail**: 编译失败也要落盘可读报告，不能只返回报错。
source-manifest
by WILLOSCARBuild or validate the source manifest for the source-tutorial pipeline from user-provided URLs/files. **Trigger**: source manifest, sources list, tutorial sources, url list, 资料清单, 教程来源. **Use when**: `source-tutorial` 的 C1，需要把多源输入落成统一的 `sources/manifest.yml`，并在内容不完整时显式阻塞。 **Skip if**: 已经有完整且经过确认的 `sources/manifest.yml`。 **Network**: none. **Guardrail**: 不要伪造来源；manifest 不完整时应返回 BLOCKED，而不是假装完成。
beamer-scaffold
by WILLOSCARGenerate a Beamer slide deck from the final tutorial and approved module structure. **Trigger**: beamer scaffold, slides from tutorial, tutorial slides, 生成 beamer, 教程幻灯片. **Use when**: `source-tutorial` 的 C4，需要把 `output/TUTORIAL.md` 转成可编译的 `latex/slides/main.tex`。 **Skip if**: 还没有 tutorial 正文。 **Network**: none. **Guardrail**: slides 不能只是机械 heading dump；必须保持模块对齐并适合讲授/轻量自学。
source-ingest
by WILLOSCARFetch and normalize supported source-tutorial inputs into local, traceable text artifacts. **Trigger**: source ingest, ingest sources, normalize tutorial sources, 网页抽取, 资料归一化. **Use when**: `source-tutorial` 的 C1，需要把 `sources/manifest.yml` 中的网页/PDF/repo/docs 变成可追溯文本。 **Skip if**: source manifest 还没定，或来源尚未确认。 **Network**: required for remote URLs. **Guardrail**: 只把成功抽取的内容当作有效 source；失败来源必须落盘记录，不能默默忽略。
source-tutorial-writer
by WILLOSCARUse when approved tutorial context packs exist and the run needs the final article-first tutorial deliverable. **Trigger**: source tutorial writer, tutorial drafting, 教程正文, 从资料写教程. **Use when**: `source-tutorial` 的 C3，`outline/tutorial_context_packs.jsonl` 已就绪，且 `DECISIONS.md` 已勾选 `Approve C2`。 **Skip if**: C2 未批准，或 context packs 还没准备好。 **Network**: none. **Guardrail**: 正文必须 reader-first，但不能写出 sources 没支持的内容。
next
by ericboy0224Move to the next lesson or challenge in Learn Docker & K8s. Use when the user says "next", "continue", "move on", "what's next", or finishes a lesson/challenge.
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.