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.
Querying local SQLite index...
reactive-hooks-audit
by evertonfernandes3321-wqAuditoria de hooks/primitivas reativas em frameworks de UI (React hooks, Vue Composition API, Svelte runes, Solid/Angular signals) — regras de hooks, dependencias de efeitos corretas, extracao para hooks/composables reutilizaveis e escolha entre estado simples vs reducer/maquina de estado. Torna o codigo mais previsivel e testavel. Use ao revisar componentes, custom hooks/composables, lifecycle, efeitos, memos, callbacks, stores reativas ou bugs de re-render/stale-closure/loop infinito.
https-security-headers-audit
by evertonfernandes3321-wqAuditoria de transporte seguro e security headers para qualquer stack e servidor (Nginx/Apache/Caddy/IIS/Traefik/CDN/load balancer/PaaS) — mixed content (API/scripts/imagens/websocket via HTTP), redirecionamento forcado de HTTP para HTTPS (301 + porta 80), HSTS com includeSubDomains e preload, CSP (com nonce/hash e upgrade-insecure-requests), X-Frame-Options/frame-ancestors, X-Content-Type-Options, Referrer-Policy, Permissions-Policy, cookies Secure/HttpOnly/SameSite, TLS minimo 1.2+ e anti-downgrade. Entrega configuracao concreta por servidor/CDN/framework e validacao empirica (curl -I, observatorios/scanners). Use para garantir que nada trafegue em claro e que downgrade de protocolo seja bloqueado.
paranoid-execution-mode
by evertonfernandes3321-wqModo de execucao paranoica para operacoes criticas em qualquer stack — validar estado com output nao-falsificavel (hash/count/exit-code) e nunca por palavra, reconciliacao memoria-vs-realidade, transacoes atomicas com meta-validacao, backup-first + rollback explicito, e disciplina anti-workaround. Use ao mexer em banco/deploy/infra/migracao/auth/billing onde estado errado causa dano irreversivel.
security-audit-full
by evertonfernandes3321-wqAuditoria de seguranca defensiva e exaustiva (nivel sub-atomico) de qualquer aplicacao/stack — autenticacao, autorizacao/IDOR, injecoes, XSS, SSRF, CSRF, uploads, secrets, cripto, supply chain, CI/CD, cloud/IaC, privacidade, business logic, concorrencia e IA/LLM. Use para pentest defensivo autorizado, revisao de seguranca pre-deploy ou hardening abrangente.
database-performance-audit
by evertonfernandes3321-wqAuditoria de performance de banco e camada de acesso a dados para qualquer stack — RLS/policies lentas (auth-function por linha, caching/wrap em SELECT, helpers SECURITY DEFINER, indices de autorizacao, indice composto tenant+filtro), N+1 e batching (DataLoader por request), indices ausentes (FK sem indice, full scan, funcao sobre coluna), EXPLAIN/ANALYZE e pg_stat_statements (e equivalentes), paginacao keyset/cursor, estruturas nao-limitadas (arrays/documentos), connection pooling e transacoes. Mais profunda e especifica de dados que a auditoria de performance geral. Use quando o gargalo for o banco, o ORM, a query, a policy de seguranca em linha, ou a camada de acesso a dados.
database-tenant-isolation-audit
by evertonfernandes3321-wqAuditoria de isolamento multi-tenant no nivel de dados para qualquer RDBMS — RLS (row-level) vs schema-per-tenant e trade-offs, propagacao de contexto de tenant, FORCE RLS, teste por matriz (usuarios x tabelas x operacoes), deteccao de vazamento cross-tenant (views/triggers/SECURITY DEFINER/service-role) e menor privilegio de roles/grants. Use para garantir que um tenant nunca veja dados de outro.
type-safety-audit
by evertonfernandes3321-wqAuditoria de seguranca de tipos para qualquer linguagem tipada (TypeScript, Python typing, Go, Java/Kotlin, C#, Rust) — abuso de any/escape hatches, parametros/retornos sem tipo, tipos frouxos, validacao runtime de dados externos (schemas) e tipos que reflitam o dominio. Aumenta a seguranca antes do runtime sem overengineering.
pre-ship-smoke-checklist
by evertonfernandes3321-wqSmoke test pre/pos-deploy com criterios observaveis cravados, em qualquer stack (web/mobile/API/infra) — matriz de cenarios numerados (T1..Tn) com passos, esperado e pre-condicao, comandos para forcar edge cases, e relato reproduzivel. Use logo antes e logo depois de subir algo para producao.
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.