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Found 796 Skills
Google Agent Development Kit (ADK) for Python. Capabilities: AI agent building, multi-agent systems, workflow agents (sequential/parallel/loop), tool integration (Google Search, Code Execution), Vertex AI deployment, agent evaluation, human-in-the-loop flows. Actions: build, create, deploy, evaluate, orchestrate AI agents. Keywords: Google ADK, Agent Development Kit, AI agent, multi-agent system, LlmAgent, SequentialAgent, ParallelAgent, LoopAgent, tool integration, Google Search, Code Execution, Vertex AI, Cloud Run, agent evaluation, human-in-the-loop, agent orchestration, workflow agent, hierarchical coordination. Use when: building AI agents, creating multi-agent systems, implementing workflow pipelines, integrating LLM agents with tools, deploying to Vertex AI, evaluating agent performance, implementing approval flows.
Workflows for generating terraform solution that are the composition of one or several Terraform IBM Modules (TIM). Use when working with IBM Cloud infrastructure as code, Terraform modules, infrastructure automation, or cloud resource provisioning. Provides workflows for module discovery, composition patterns, code generation, and validation. Essential for tasks involving IBM Cloud VPC, compute, networking, security, databases, observability, or any IBM Cloud service deployment. Triggers on keywords like "terraform", "IBM Cloud", "infrastructure", "IaC", "modules", "deploy", "provision", or specific IBM Cloud services (VPC, VSI, OpenShift, etc.).
Modern Python API development with FastAPI covering async patterns, Pydantic validation, dependency injection, and production deployment
Complete guide for Hasura GraphQL Engine including instant GraphQL APIs, permissions, authentication, event triggers, actions, and production deployment
Complete guide for Apache Airflow orchestration including DAGs, operators, sensors, XComs, task dependencies, dynamic workflows, and production deployment
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
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.
Create modern, interactive web presentations powered by Reveal.js. Supports both single-file HTML presentations AND multi-presentation repository management with GitHub Pages deployment. Use for creating slides, presentations, slide decks, presentation repositories with auto-generated index pages. Features advanced animations, speaker notes, overview mode, and full Reveal.js API access.
Execute deployment through Makefile targets with ENV_MODE and optional VERSION overrides. Use when running real deployment or dry-run preview in Makefile-first workflow.
Configure deployment files with a common baseline file plus environment override files. Use when setting up or adjusting Makefile-first deployment for test/prod/custom environments and non-default SSH/SCP ports.
Decide deployment success or rollback from smoke-check and optional metrics data. Use when gating final rollout and rollback decisions after deployment.
Normalize and validate deployment version under Makefile-first workflow. Use when reading or validating version for test/prod/custom environments before make-based deployment.