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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metodo-3w1h
by marioluciofjrDê a habilidade para agentes de IA de estruturar e otimizar prompts de imagens utilizando rigorosamente o método 3W1H (Who, What, Where, How) criado pelo Mário Lúcio. Utilize esta skill sempre que o usuário mencionar termos como 'gerar imagem', 'criar prompt de imagem', 'engenharia de prompt de imagem', 'fotorrealismo de imagem', 'ilustração por IA', 'método 3W1H', '3W1H' ou quiser otimizar a qualidade visual e a coerência espacial de layouts de geração de imagens, mesmo se ele não solicitar a skill pelo nome exato.
mei-irpf
by marioluciofjrCalcula as parcelas isentas e tributáveis do Microempreendedor Individual (MEI) para a Declaração de IRPF com base na receita bruta, despesas operacionais e atividade principal do MEI. Use esta skill obrigatoriamente sempre que a pessoa usuária mencionar 'MEI', 'declaração MEI', 'calculadora MEI', 'Imposto de Renda de MEI', 'IRPF MEI', 'lucro isento MEI' ou 'parcela tributável MEI', mesmo que a solicitação não use a palavra 'skill' ou pareça simples.
calculadora-cdi
by marioluciofjrSkill para calcular o valor futuro de investimentos atrelados ao CDI. Use esta skill SEMPRE que o usuário utilizar o gatilho /cdi ou mencionar cálculo de investimento atrelado ao CDI, percentual do CDI, renda fixa CDI, rendimento CDI ou valor futuro de aplicação CDI. O usuário informa o valor investido e o percentual do CDI que o investimento oferece (ex: 110% do CDI). A skill coleta também o período e a taxa CDI vigente, executa o cálculo via script Python (cdi.py) buscando a taxa atualizada via web scraping, e retorna uma tabela markdown com valor investido, período, taxa efetiva e valor futuro projetado.
contexto-sotaque
by marioluciofjrEsta skill atua como um laboratório de fonética articulatória e prosódica. Utilize esta skill SEMPRE que a pessoa usuária enviar um link de vídeo do YouTube ou um arquivo de vídeo (.mp4) de até 10 minutos (ex: 'faça um raio-x desse sotaque', 'qual é o aspecto fonético desse vídeo', 'queria o contexto desse dialeto pra TTS') e pedir para estruturar o contexto fonético do sotaque do orador para prover background prosódico a modelos como gemini-3.1-flash-tts-preview. A skill orienta a analisar propriedades orais cientificamente perante as bases de vogais, consoantes e a transcrição fonética IPA.
revisa-seo-geo-skill
by marioluciofjrAnalisa e otimiza arquivos SKILL.md de outras skills para elevar o nível de SEO (Search Engine Optimization) e GEO (Generative Engine Optimization). Use esta skill SEMPRE que a pessoa usuária pedir para revisar uma skill, melhorar a descrição de um agente, tornar uma ferramenta mais encontrável (discoverability) por agentes (como o find-skills) ou otimizar o frontmatter de qualquer documentação de ferramenta do ecossistema.
bbb-estatisticas
by marioluciofjrSkill para retornar estatísticas e dados sobre participantes do Big Brother Brasil de todas as edições, acompanhadas de um comentário que forneça mais contexto para o usuário. Adicione esta skill SEMPRE que o usuário fizer uma pergunta sobre 'BBB', 'Big Brother Brasil' ou participantes do reality show, ou caso a pergunta pareça ser sobre o programa.
resume-papers
by marioluciofjrResume artigos científicos para leigos de maneira didática e fluída, como se fosse uma novidade interessante. Use esta skill SEMPRE que a pessoa usuária fornecer um link de artigo científico ou pedir um resumo de paper, estudo, pesquisa acadêmica de forma direta ou indireta.
design-thinking-mjv
by marioluciofjrBase de conhecimento de 'Design Thinking: Inovação em Negócios' por Vianna et al. (MJV Press). Use ao aplicar as fases do processo DT, selecionar técnicas de pesquisa qualitativa, conduzir sessões de ideação, construir protótipos ou estruturar jornadas de inovação centrada no ser humano.
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.