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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alpha-research
by companion-incSearch, read, and query research papers via the `alpha` CLI (alphaXiv-backed). Use when the user asks about academic papers, wants to find research on a topic, needs to read a specific paper, ask questions about a paper, inspect a paper's code repository, or manage paper annotations.
research-assistant
by heyitsnoahConduct thorough research on topics by searching the vault and synthesizing findings. Use when the user asks to research a topic, dig into something across notes, or wants a synthesis of what the vault knows about a subject.
memory-bridge
by Ar9avBrowse and compare wiki knowledge by which AI tool originally produced it. Use this skill when the user says "/memory-bridge", "browse codex memory", "what did codex know about X", "show me claude knowledge", "cross-tool memory", "what does hermes know that claude doesn't", "show me knowledge from <tool>", "compare my AI tool memories", or wants to explore knowledge gaps between tools. Works from any project. Diff mode ("what's different", "unique to codex", "gaps between tools") is the killer feature — it surfaces blind spots between tools that the user may not know exist.
citation-management
by aipochComprehensive citation management for academic research; use when you need to discover papers (Google Scholar/PubMed), extract/verify metadata (DOI/PMID/arXiv/URL), and produce validated, clean BibTeX for manuscripts.
oma-search
by first-flukeIntent-based search router with trust scoring. Routes queries to optimal channels (Context7 docs, native web search, gh/glab code search, Serena local) and attaches domain trust labels. Use for search, find, lookup, reference, docs, code search, and web research.
moai-workflow-jit-docs
by modu-aiEnhanced Just-In-Time document loading system that discovers, loads, and caches relevant documentation based on user intent and project context. Use when users need specific documentation on demand.
clipboard
by kellyvv读写系统剪贴板内容。当用户需要读取、复制或操作剪贴板时使用。
contacts
by kellyvv查询、创建、更新或删除联系人。当用户要查电话、看联系方式、存号码、补充联系人信息或删除联系人时使用。
reminders
by kellyvv创建新的提醒事项。当用户需要记得做某事、设置待办或提醒时使用。
wiki-manager
by nvkLLM-compiled knowledge base manager for OpenCode. Use it to initialize, ingest, import source collections, collect catalogs, track inventory, index datasets, archive old topics, compile, query, lint, audit, research, plan, capture or rehydrate agent session context, and generate outputs from topic-scoped wikis. Activates when the user mentions wiki workflows, knowledge-base management, ingestion, collection ingestion, import wiki, collect, catalog, curate, find all, inventory, source queue, candidate list, watch list, backlog, dataset, large data, data registry, dataset manifest, compilation, querying, linting, audit, research, librarian, scan quality, article quality, content review, output drift, provenance, archive wiki, archive topic, restore wiki, session capture, capture context, rehydrate, resume from session, lessons learned, implementation plan, or uses wiki-related shorthand in a repo with .wiki/, ~/wiki/, or a configured hub path.
wiki
by nvkLLM-compiled knowledge base manager for Codex. Use it to initialize, ingest, import source collections, collect catalogs, track inventory, index datasets, archive old topics, compile, query, lint, audit, research, plan, capture or rehydrate agent session context, and generate outputs from topic-scoped wikis. Activates when the user mentions wiki workflows, knowledge-base management, ingestion, collection ingestion, import wiki, collect, catalog, curate, find all, inventory, source queue, candidate list, watch list, backlog, dataset, large data, data registry, dataset manifest, compilation, querying, linting, audit, research, librarian, scan quality, article quality, content review, output drift, provenance, archive wiki, archive topic, restore wiki, session capture, capture context, rehydrate, resume from session, implementation plan, or uses /wiki-style shorthand in a repo with .wiki/, ~/wiki/, or a configured hub path.
lark-wiki
by eastreams飞书知识库：管理知识空间和文档节点。创建和查询知识空间、管理节点层级结构、在知识库中组织文档和快捷方式。当用户需要在知识库中查找或创建文档、浏览知识空间结构、移动或复制节点时使用。
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.