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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figure-table-audit
by brycewang-stanfordAudit figures, tables, captions, cross-references, and statistical notes.
post-ocr-cleanup
by brycewang-stanfordClean post-OCR text: correction, QA, multilingual handling, provenance.
systematic-review-protocol
by revfactoryA specialized skill providing PRISMA protocols and literature search strategies for systematic reviews. Used by the literature-searcher and critic-synthesizer agents when systematically searching, screening, and synthesizing academic literature. Automatically applied in contexts involving 'systematic review,' 'PRISMA,' 'literature search strategy,' 'inclusion/exclusion criteria,' or 'Boolean search.' Note: direct access to academic databases (Scopus, WoS) and meta-analysis statistical execution are outside the scope of this skill.
systematic-review-protocol
by revfactory체계적 문헌 고찰(Systematic Review)의 PRISMA 프로토콜과 문헌 검색 전략을 제공하는 전문 스킬. literature-searcher와 critic-synthesizer가 학술 문헌을 체계적으로 검색, 선별, 종합할 때 활용한다. '체계적 문헌 고찰', 'systematic review', 'PRISMA', '문헌 검색 전략', '포함/배제 기준', 'Boolean 검색' 등의 맥락에서 자동 적용한다. 단, 학술 DB(Scopus, WoS) 직접 접속이나 메타분석 통계 실행은 이 스킬의 범위가 아니다.
research-methodology
by revfactory연구 방법론 가이드. research-designer와 statistical-analyst 에이전트가 연구 설계와 분석 방법을 선택할 때 참조. '연구 방법론', '실험 설계', '통계 분석' 요청 시 사용. 단, 실제 데이터 수집이나 IRB 심사 대행은 범위 밖.
mira-annotation-review
by stencilaReview MIRA annotations in Markdown documents for semantic correctness, Markdown dialect validity, preservation, and relation integrity. Use for md, smd, qmd, and myst files containing research objects such as claims, evidence, questions, protocols, requests, typed claims, ids, and relations. Produces actionable findings without modifying files by default.
mira-annotation
by stencilaAnnotate Markdown documents with MIRA research objects and source-local relations. Use for md, smd, qmd, and myst files when identifying claims, evidence, questions, protocols, requests, typed claims, and relation attributes while preserving the original Markdown flavor and author wording. Not for general copyediting, prose rewriting, or extracting a separate knowledge graph without annotating the source.
research-reflection
by beita6969Reflect on completed research tasks to improve future performance. Use when: a research task has just been completed and the agent should evaluate its own process, store lessons learned, or retrieve past reflections before starting new work. NOT for: active research execution or data analysis.
social-science-research
by beita6969Orchestrates a social science research workflow from literature review through data collection, text analysis, statistical modeling, and report generation. Use when conducting empirical social science research, policy analysis, or mixed-methods studies. NOT for pure natural science analysis or clinical trial data.
broad-web-search
by yogsoth-aiQuick web scanning for field landscape understanding. Strict import of web-browsing/web-search skill. Hard constraint: ~10 results per call, at least 150 total search results before completing.
de-anthropocentric-research-engine
by yogsoth-aiTop-level orchestrator for the yogsoth-ai research ecosystem. Drives the full research lifecycle from direction crystallization through experiment design.
skill-index
by yogsoth-aiFull capability landscape of the DARE skill ecosystem — 823 skills across 8 research phases. Read this to understand what campaigns, strategies, tactics, and SOPs are available. Invoke this skill whenever you need to understand the complete arsenal before generating a Research Spec or routing a customized workflow.
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.