AI & ML

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".

Full video production from a single prompt. Script, shoot, stitch, score — automatically. 30s to 4-minute Instagram Reels, TikToks, Stories, and carousels with consistent characters and agentic editing. The most advanced AI video suite for social media content, powered by #1 on DeepResearch Bench (Feb 2026).

MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.

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Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

Template for creating new Agent Skills for context engineering. Use this template when adding new skills to the collection.

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Expert ReactFlow architect for building interactive graph applications with hierarchical node-edge systems, performance optimization, and auto-layout integration. Use when Claude needs to create or optimize ReactFlow applications for: (1) Interactive process graphs with expand/collapse navigation, (2) Hierarchical tree structures with drag & drop, (3) Performance-optimized large datasets with incremental rendering, (4) Auto-layout integration with Dagre, (5) Complex state management for nodes and edges, or any advanced ReactFlow visualization requirements.

Ensure thorough validation, error recovery, and transparent reasoning in research tasks with multiple tool calls

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