Skill UI

Guides through the Lindy AI agent lifecycle: define specifications, instantiate via the SDK, configure required tools, and run sample tests to verify readiness before deployment.

Guides Claude Code plugin authors through Model Context Protocol server integration, covering dedicated .mcp.json setup, inline plugin configuration, server types (stdio, SSE, HTTP, WebSocket), authentication flows, tool naming, and lifecycle management so external services appear as structured tools.

MLflow provides a framework-agnostic ML lifecycle toolkit for tracking experiments, logging models/artifacts, managing registries with versions/stages, and deploying across platforms so teams can reproduce and collaborate on PyTorch, TensorFlow, and Scikit-Learn workflows.