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Found 1,596 Skills
Gemini 3 Pro API/SDK integration for text generation, reasoning, and chat. Covers setup, authentication, thinking levels, streaming, and production deployment. Use when working with Gemini 3 Pro API, Python SDK, Node.js SDK, text generation, chat applications, or advanced reasoning tasks.
Debug failed Render deployments by analyzing logs, metrics, and database state. Identifies errors (missing env vars, port binding, OOM, etc.) and suggests fixes. Use when deployments fail, services won't start, or users mention errors, logs, or debugging.
Configure CI/CD, Docker, and cloud deployments. Use for deployment setup, containers, or infrastructure automation.
Comprehensive CI/CD pipeline patterns skill covering GitHub Actions, workflows, automation, testing, deployment strategies, and release management for modern software delivery
Complete guide for Apache Kafka stream processing including producers, consumers, Kafka Streams, connectors, schema registry, and production deployment
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Complete knowledge of the runpod-flash framework - SDK, CLI, architecture, deployment, and codebase. Use when working with runpod-flash code, writing @remote functions, configuring resources, debugging deployments, or understanding the framework internals. Triggers on "flash", "runpod-flash", "@remote", "serverless", "deploy", "LiveServerless", "LoadBalancer", "GpuGroup".
Safe database migration strategies for zero-downtime deployments. Covers backward-compatible changes, data migrations, and rollback procedures.
Architect a full-stack application on Eve Horizon — manifest-driven services, managed databases, build pipelines, deployment strategies, secrets, and observability. Use when designing a new app, planning a migration, or evaluating your architecture.
ML inference latency optimization, model compression, distillation, caching strategies, and edge deployment patterns. Use when optimizing inference performance, reducing model size, or deploying ML at the edge.
Deploy Databricks jobs and pipelines with Asset Bundles. Use when deploying jobs to different environments, managing deployments, or setting up deployment automation. Trigger with phrases like "databricks deploy", "asset bundles", "databricks deployment", "deploy to production", "bundle deploy".
Deploy Customer.io integrations to production. Use when deploying to cloud platforms, setting up production infrastructure, or automating deployments. Trigger with phrases like "deploy customer.io", "customer.io production", "customer.io cloud run", "customer.io kubernetes".