lakebase-for-ai-developers

Run the full cold-start end-to-end deploy test in an isolated git worktree with always-safe teardown. Use when: (1) verifying the one-shot `make deploy` works on a FRESH workspace/catalog (cold-start), (2) the user says 'integration test', 'cold-start test', 'test the deploy end to end', or 'does deploy still work on a new workspace', (3) validating a change to deploy.sh / databricks.yml / the seed before opening a PR. PROJECT-AUTHORED (not a vendored skill).

Keep this repo's architecture diagrams and prose docs in sync with the code as it evolves. Use this whenever the user asks to update/refresh the architecture diagrams, sync docs with the code, audit docs for staleness, or check that docs/architecture.md still matches the implementation — and proactively after any significant change (a new graph node, tool, Lakebase schema, data-pipeline step, auth change, or frontend swap), since the diagrams and READMEs drift silently. Covers refreshing the four Mermaid diagrams in docs/architecture.md, reconciling the known stale-doc patterns, and verifying with greps + Mermaid validity.

Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. IMPORTANT - Always also load the instrumenting-with-mlflow-tracing skill before starting any work. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).

Build apps on Databricks Apps platform. Use when asked to create dashboards, data apps, analytics tools, or visualizations. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.

Databricks CLI operations: auth, profiles, data exploration, and bundles. Contains up-to-date guidelines for Databricks-related CLI tasks.

Create, configure, validate, deploy, run, and manage Declarative Automation Bundles (DABs, formerly Databricks Asset Bundles). Use when working with Databricks resources via DABs including dashboards, jobs, pipelines, alerts, volumes, and apps.

Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks.

Manage Databricks Model Serving endpoints via CLI. Use when asked to create, configure, query, or manage model serving endpoints for LLM inference, custom models, or external models.

Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns

Instruments Python and TypeScript code with MLflow Tracing for observability. Must be loaded when setting up tracing as part of any workflow including agent evaluation. Triggers on adding tracing, instrumenting agents/LLM apps, getting started with MLflow tracing, tracing specific frameworks (LangGraph, LangChain, OpenAI, Gemini, DSPy, CrewAI, AutoGen), or when another skill references tracing setup. Examples - "How do I add tracing?", "Instrument my agent", "Trace my LangChain app", "Set up tracing for evaluation"