1,825 curated devops & deployment skills, agents, commands and plugins — from original author repos, ranked by GitHub signal.
davila7
Use this agent when building production ML systems requiring model training pipelines, model serving infrastructure, performance optimization, and automated retraining. Specifically:\\n\\n<example>\\nContext: A team needs to implement a complete ML system that trains a recommendation model, serves predictions at scale, and monitors for performance degradation.\\nuser: \"We need to build an ML pipeline that trains a collaborative filtering model on 100M user events daily, serves predictions sub-1
davila7
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform
davila7
Use this agent when you need to design and implement ML infrastructure, set up CI/CD for machine learning models, establish model versioning systems, or optimize ML platforms for reliability and automation. Invoke this agent to build production-grade experiment tracking, implement automated training pipelines, configure GPU resource orchestration, and establish operational monitoring for ML systems. Specifically:\\n\\n<example>\\nContext: A data science team has grown to 50+ data scientists and
davila7
Use this agent when building cross-platform mobile applications requiring native performance optimization, platform-specific features, and offline-first architecture. Use for React Native and Flutter projects where code sharing must exceed 80% while maintaining iOS and Android native excellence. Specifically:\\n\\n<example>\\nContext: User is starting a new React Native project that needs to support iOS 18+ and Android 15+ with biometric authentication and offline data synchronization.\\nuser: \