git-opensource
Git workflows, GitHub management, and open source community building
Git workflows, GitHub management, and open source community building
Audits Nixtla library usage and recommends cost-effective routing strategies. Scans TimeGPT, StatsForecast, and MLForecast patterns, identifies cost optimization opportunities, generates comprehensive usage reports, and suggests smart routing between models. Activates when user needs cost optimization, API usage audit, routing strategy design, or Nixtla cost reduction.
Use this skill whenever the user wants to design, run, or refine Cloudflare D1 schema management, migrations, and data seeding for dev/staging/production environments, especially in conjunction with Hono/Workers apps.
Production deployment patterns for the World of Darkness Django application. Use when deploying to staging/production, configuring security settings, setting up Redis cache, planning database migrations, or preparing rollback procedures. Triggers on deployment tasks, production configuration, security hardening, or environment-specific settings.
Manage Logstash pipelines deployed in OpenShift using ConfigMaps. Use for pipeline configuration, deployment, troubleshooting, and maintenance of Logstash in containerized OpenShift environments.
Architecture decision guides for Kailash SDK including framework selection (Core SDK vs DataFlow vs Nexus vs Kaizen), runtime selection (Async vs Sync), database selection (PostgreSQL vs SQLite), node selection, and test tier selection. Use when asking about 'which framework', 'choose framework', 'which runtime', 'which database', 'which node', 'architecture decision', 'when to use', 'Core SDK vs DataFlow', 'PostgreSQL vs SQLite', 'AsyncLocalRuntime vs LocalRuntime', or 'test tier selection'.
This skill should be used when the user asks about "orchestration patterns", "plan-then-execute", "hierarchical decomposition", "blackboard pattern", "event sourcing pattern", "which pattern to use", "parallel execution strategies", or needs to select an orchestration approach for complex multi-agent tasks. Provides comprehensive guidance on 4 orchestration patterns for coordinating multiple agents.
Securing AI/ML infrastructure including model storage, API endpoints, and compute resources
This skill should be used when the user asks to "deploy FastAPI to Kubernetes", "create Dockerfile", "build Docker image", "write Helm chart", "configure K8s deployment", "add health checks", "scale FastAPI", or mentions Docker, Kubernetes, K8s, containers, Helm, or deployment. Provides containerization and orchestration patterns.
Design and deploy AI workloads across AWS, Azure, GCP, and OCI with intelligent routing, cost optimization, and cross-cloud patterns
Model deployment strategies including serving infrastructure, containerization, model packaging, versioning, and production deployment patterns.
Comprehensive development guides for advanced Kailash SDK features including custom node development, MCP development, async patterns, testing strategies, production deployment, RAG systems, security patterns, monitoring, and SDK internals. Use when asking about 'development guide', 'advanced features', 'custom node development', 'async node development', 'MCP development', 'production deployment', 'testing strategies', 'RAG implementation', 'security patterns', 'monitoring setup', 'circuit breaker', 'compliance', 'edge computing', or 'SDK internals'.
KubeAI, GPU operators, and model serving patterns for AI/ML infrastructure on Kubernetes.
AI agent practices test-first development with the Red-Green-Refactor cycle for confident, well-designed code. Use when implementing features, fixing bugs, or establishing testing practices.
Build production agentic applications on OCI using Oracle Agent Development Kit with multi-agent orchestration, function tools, and enterprise patterns
Agent scaffolding and template generation for Vercel deployments
Cloud Run AI Engine deployment workflow with free tier optimization. Triggers when user requests Cloud Run deployment, AI engine deploy, or production release. Use for deploying AI Engine to Google Cloud Run.
Build AI applications on AWS using Bedrock, SageMaker, and AI/ML services with best practices for enterprise deployment
Expert in Vercel AI SDK v5 handling streaming, model integration, tool calling, hooks, state management, edge runtime, prompt engineering, and production patterns. Use PROACTIVELY for any AI SDK implementation, streaming issues, provider integration, or AI application architecture. Detects project setup and adapts approach.
Design and deploy AI workloads across AWS, Azure, GCP, and OCI with intelligent routing, cost optimization, and cross-cloud patterns
Neural network training and deployment in Flow Nexus cloud. Use for distributed ML training, model inference, and neural network lifecycle management.