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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ubuntu-collective-knowledge-task-designer
by GarethManningDesign learning tasks built on Ubuntu philosophy emphasising collective knowledge-building and mutual responsibility. Use when fostering collaboration that values community over individual competition.
judith-butler
by K-Dense-AIUse this skill to apply the analytical lens of Judith Butler (American philosopher, gender theory, Gender Trouble). Trigger this skill whenever the user is discussing gender performativity, identity politics, right-wing populism, anti-gender movements, queer theory, bodily autonomy, or the intersection of democracy and marginalized lives. Reach for it when analyzing systemic power, social construction, collective vulnerability, or authoritarian rhetoric. It helps deconstruct essentialist assumptions, reframe anxieties about identity, and build ethical frameworks based on material interdependence rather than mere sympathy.
ancient-near-east-research
by tdiminoAcademic research skill for Biblical Hebrew, Semitic linguistics, cuneiform studies, and comparative Ancient Near Eastern research. Provides Sefaria API for Hebrew Bible, CDLI/ORACC for cuneiform databases, and web discovery via Omnisearch, Exa, Firecrawl, and Obscura for finding scholarly sources across JSTOR, Perseus, Persée, Google Scholar, and academia.edu. Triggers on Hebrew quotes, cuneiform, Sefaria, ANE research, Minoan, search for scholarship, find papers, literature review, scholarly search, academic search, Genesis/Tehom, Ugaritic, Talmudic sources, extract from PDF, OCR academic.
hijri-calendar
by Moshe-shipالتقويم الهجري — تحويل التواريخ بين الميلادي والهجري ومعرفة المناسبات الإسلامية. استخدم عندما يسأل المستخدم عن تاريخ هجري أو مناسبة إسلامية.
consult-natural-history
by pjt222Reference Hildegard von Bingen's Physica natural history knowledge. Covers classification of plants, stones, animals, fish, birds, elements, and trees with their medicinal, symbolic, and practical properties. Enables cross- referencing between categories and application guidance. Use when exploring a specific plant, stone, or animal from Hildegard's perspective, researching medieval natural history and cosmology, cross-referencing properties across categories, or integrating Physica knowledge into health, spiritual, or creative practice.
genealogical-method
by majiayu000Master genealogical methodology - Nietzschean and Foucauldian genealogy of concepts and practices. Use for: historical analysis, power, origins of concepts. Triggers: 'genealogy', 'genealogical', 'Nietzsche genealogy', 'Foucault', 'archaeology', 'power knowledge', 'origin of', 'history of', 'emergence', 'descent', 'Entstehung', 'Herkunft', 'moral history'.
social-group-description-and-lingua-cultural-analysis
by gabrielmoreiraAnalyzes a specific social group using a fixed 8-point demographic schema and V.I. Karasik's three-component lingua-cultural framework, including associated notions with Cambridge Dictionary definitions.
theoretical-cucuteni-trypillia-substrate-reconstruction
by gabrielmoreiraReconstructs text into the hypothetical Pre-Indo-European Cucuteni–Trypillia substrate of the West-Western Yamnaya region, utilizing multidisciplinary data and theoretical frameworks while strictly avoiding Yamnaya admixtures.
full-rebuild-bio
by learn-ukrainianAtomic rebuild for BIO (Ukrainian Biographies). Narrative Engine v5.0 (Slim Skill + Rich Phase Prompts).
full-rebuild-bio
by learn-ukrainianTier 3 structural rebuild for BIO. Targets 5000+ words, academic decolonization, and biographical agency analysis. Triggers on "/full-rebuild bio N-M".
cites-review
by WEN-JYReview, organize, and format academic references and citations following GB/T 7714-2015 and other standards. This skill should be used when users need to: (1) Check and fix reference formatting errors, (2) Standardize citation styles, (3) Extract references from CNKI (知网) using browser scripts or Playwright automation, (4) Cross-check in-text citation numbers against the reference list, (5) Reorganize and renumber references after document restructuring.
korean-dream-interpretation-tradition
by puk0806한국 전통 꿈 해몽을 민속학적·문헌학적 자료로 정리한 스킬. 『동의보감』 내경편, 『삼국유사』, 한국민족문화대백과사전, 국립민속박물관 한국민속신앙사전 등 1차/공인 학술 출처만 사용한다. 비과학적 한계를 본문 박스로 명시하며, 앱·서비스 적용 시 hedging 톤을 강제한다. <example>사용자: "한국 전통에서 돼지꿈은 어떻게 해석돼?"</example> <example>사용자: "동의보감은 꿈을 어떻게 분류해?"</example> <example>사용자: "꿈 해몽 앱 만드는데 톤 가이드 줘"</example>
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.