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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aris-paper-compile
by OpenLAIRCompile LaTeX paper to PDF, fix errors, and verify output. Use when user says "编译论文", "compile paper", "build PDF", "生成PDF", or wants to compile LaTeX into a submission-ready PDF.
deeppapernote
by NeverSightGenerate a high-quality deep-reading note for a single paper and write it into an Obsidian-style vault. Use when the user gives a paper title, DOI, URL, arXiv ID, Zotero item, or local PDF and wants a polished Markdown note with strong structure, evidence-based analysis, and figure placeholders.
paper-journal-style
by moonlarryCheck academic drafts against target-journal style and submission expectations. Use when the user mentions a target journal, asks for abstract or highlights formatting, wants title or keyword guidance, or needs cross-section consistency checks before submission.
paper-analysis
by InsanityByAnalyze academic paper relevance from a provided paper title and abstract using a user-defined relevance rubric. Use when an agent workflow needs repeatable relevance scoring, controlled tag selection, optional Chinese translation, PaperScout-compatible JSON, Markdown, or combined output, profile configuration, and calibration feedback. Do not use for paper search, metadata fetching, citation retrieval, bibliography generation, full-text review, generic summarization, or paper-quality judgment without a relevance rubric.
citation-management
by satnamrsmComprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation...
verify-citation-claims
by AMindToThinkAudit whether a paper's prose actually matches the content of its cited references. Use before submitting / publishing a paper, after substantial prose revisions, when adding a new citation, when reviewing someone else's paper, or any time a user asks to "check the citations" / "verify claims" / "make sure X is supported." Dispatches parallel subagents that fetch each cited paper and verify the specific claim against specific sections. Complements `bibliography-from-ids`, which prevents fabricated metadata; this skill prevents miscited claims and factual drift in surrounding prose.
paper-to-note
by liming-aiUse when reading an academic paper or paper URL and saving structured Chinese notes with metadata/assets to Obsidian.
digital-library-plan
by WinbdaPlan digital libraries. TRIGGERS - Use when user needs help with digital-library-plan related tasks.
dodder-onboarding
by amarbel-llcIntroduces dodder, a distributed zettelkasten and content-addressable blob store. Covers core concepts (zettels, object IDs, tags, types, blobs, the store), installation via Nix, a first-steps walkthrough including repository initialization and zettel creation, and the mental model for working with dodder. Activated when a user is new to dodder, asks what dodder is, wants to get started, install, set up, or learn how dodder works.
static-ontology-knowledge-graph-trap
by blas1nHard-coded note_type / category enums in a knowledge system create filing cabinets, not knowledge graphs. The trap: classification looks like success (notes neatly distributed across folders) while the actual graph value (emergent connections, surprising links) stays at zero. Static ontology + LLM classifier = sophisticated tagger, not graph thinking.
citation-assistant
by FIRE-hub911学术文献引用助手：基于 Semantic Scholar API 的语义化文献检索与引用管理。 触发场景： - 用户在 LaTeX 文稿中标记 [CITE] 占位符，需要查找合适的引用文献 - 用户粘贴论文段落，需要为其中的 [CITE] 标记寻找引用 - 用户需要根据语义上下文查找学术引用 - 用户需要验证/替换不规范的引用格式（如 "PMC Articles"） - 用户需要查询期刊/会议质量（CCF/JCR/IF/引用量） - 用户需要查询作者 H-index 和引用量 - 用户需要判断 arXiv 文章是否值得引用 - 用户提及 "文献引用"、"找引用"、"citation"、"bib"、"参考文献" - 用户需要完整的引用推荐到 BibTeX 生成的端到端服务
paper-reliability-verification
by Rycen7822Verify paper reliability through the bundled DeepScientist verifier workflow before using papers as evidence.
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.