Skills
El registro abierto de Skills para agentes de IA: prompts estructurados y flujos de trabajo con modelos recomendados, ejemplos y herramientas compatibles.
Skills
9
Categorías
9
Herramientas compatibles
6
Colaboradores
1
Mostrando 1–9 de 9 Skills
Designs traces, metrics, structured events, privacy controls, dashboards, and alerts for LLM applications so teams can diagnose quality, cost, latency, and tool-use behavior.
Guides containment, evidence collection, root-cause analysis, customer communication, and remediation when an AI agent takes an unsafe, incorrect, or unauthorized action.
Reviews cloud and AI infrastructure spend to identify measurable savings while preserving reliability, performance, security, ownership, and product requirements.
Optimizes prompts and agent workflows for finite context windows by prioritizing evidence, compressing history, managing retrieval budgets, and measuring token-cost trade-offs.
Builds disaster recovery and business continuity plans for systems and data. Defines RTO and RPO targets, maps critical dependencies, chooses backup and replication strategies, documents failover and restore procedures, and designs a testing cadence with game days. Produces a plan that balances resilience against cost and operational complexity.
Designs chaos engineering experiments to validate system resilience. Defines steady-state hypotheses, blast-radius limits, fault injections (latency, errors, instance/zone loss, resource exhaustion), abort conditions, and observability checks. Produces a safe, incremental experiment plan and success criteria for game days and automated chaos.
Generates production-ready infrastructure as code (IaC) configurations for cloud deployments. Supports Terraform, Pulumi, CloudFormation, and CDK. Creates modular, reusable infrastructure components with proper networking, security groups, IAM policies, and monitoring configurations.
Assists with deploying applications to cloud platforms including AWS, GCP, Azure, and Vercel/Netlify. Generates infrastructure-as-code (Terraform, Pulumi, CDK), Dockerfiles, CI/CD pipelines, and deployment scripts. Handles environment configuration, secrets management, and production readiness checks.
Plans and generates migration strategies for framework upgrades, language versions, database changes, and architecture shifts. Produces step-by-step migration guides with rollback plans, risk assessment, and automated codemods where possible.
Skills frente a servidores MCP
¿cuál es la diferencia?Skillsel «qué hacer»
Una Skill reúne conocimientos prácticos —instrucciones, un prompt de ejemplo y modelos recomendados— para que un agente realice una tarea de forma consistente. Las Skills aportan conocimiento, no nuevas conexiones.
Servidores MCPel «cómo conectarse»
Un servidor MCP proporciona nuevas capacidades a un agente conectándolo con sistemas reales —bases de datos, API o archivos— mediante un transporte. MCP añade conexiones y acciones, no instrucciones de tarea.
Regla práctica: usa una Skill cuando necesites que el modelo realice bien una tarea y un servidor MCP cuando necesites conectarlo con una herramienta o sistema. Se complementan: una Skill puede utilizar las herramientas que proporciona un servidor MCP.
¿Has creado una Skill útil?
Envía un archivo SKILL.md: es de código abierto y está mantenido por la comunidad.