Skill UI

Design and implement production-ready ML pipelines using multi-agent MLOps orchestration, covering data engineering, feature engineering, model training, experiment tracking, deployment, and serving.

This skill enables building production-ready machine learning systems using modern frameworks like PyTorch and TensorFlow. It covers model serving, feature engineering, A/B testing, monitoring, and MLOps practices. Focuses on scalable, reliable, and efficient ML infrastructure for delivering business value in production environments.

Build scalable ML infrastructure and automation pipelines using tools like MLflow, Kubeflow, and cloud platforms. Manage the full MLOps lifecycle from experimentation to production deployment, ensuring reliable and efficient machine learning systems.

A remote MCP server designed for AI agents to dynamically discover, inspect, and verify external tools, APIs, and services. It allows agents to search a curated index of AI-ready websites, check for machine-readable endpoints (like OpenAPI or MCP), and confirm if an endpoint responds correctly to JSON-RPC, enabling robust, dynamic agent workflow building.

Find, use, create, install, share, update, and migrate agent skills stored in OpenViking, including from Git or local folders, so they work across harnesses and machines.

Review supervisor, swarm, planner-worker and parallel agent workflows as distributed state machines: task ownership, shared state, branch joins, retries, cancellation, context handoffs, budgets, deadlocks and human escalation before rollout.

Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript across browsers and server-side runtimes like Node.js, Bun, and Deno, enabling text analysis, image classification, speech recognition, and multimodal AI applications without a Python backend.