⚠️ Experimental: best-effort, not officially supported

The skills in this directory were originally imported from databricks-solutions/ai-dev-kit on a best-effort basis. ai-dev-kit is now deprecated — databricks/databricks-agent-skills is the canonical home for these skills going forward, and updates land here directly rather than via re-sync.

Most of that snapshot has since been promoted to the stable ../skills/ tree (see Promoted to stable below). The few skills that remain here are still not officially supported as part of databricks-agent-skills:

• They do not follow the same review / quality bar as the skills in ../skills/.
• They are not installed by databricks aitools install by default — you have to opt in (see the root README).

File issues against this directory in this repo; do not file issues against the deprecated ai-dev-kit repo.

---

Databricks Skills for Claude Code (experimental)

Skills that teach Claude Code how to work effectively with Databricks - providing patterns, best practices, and code examples that work with Databricks MCP tools.

Installation

These experimental skills are not installed by default. To install them via the Databricks CLI:

# Install all experimental skills at once
databricks aitools install --experimental

# Install a single experimental skill by name
databricks aitools install spark-python-data-source --experimental

See the root README for details on the stable install path.

Available Skills

🔧 Data Engineering

• spark-python-data-source - Python data sources for Spark (custom connectors)

Promoted to stable

Most of the original ai-dev-kit snapshot now lives in the stable ../skills/ tree and installs by default (no --experimental flag). See the root README for the full list. Promoted skills include databricks-ai-functions, databricks-agent-bricks, databricks-aibi-dashboards, databricks-apps-python, databricks-dbsql, databricks-docs, databricks-execution-compute, databricks-genie-agents, databricks-iceberg, databricks-lakeflow-connect, databricks-metric-views, databricks-ml-training, databricks-mlflow-evaluation, databricks-python-sdk, databricks-spark-structured-streaming, databricks-synthetic-data-gen, databricks-unity-catalog, databricks-unstructured-pdf-generation, and databricks-zerobus-ingest. databricks-data-discovery (Genie One data discovery / NL data Q&A / SQL generation) was also promoted to stable.

Earlier experimental copies of databricks-bundles, databricks-lakebase-autoscale, and databricks-config were merged into the stable databricks-dabs, databricks-lakebase, and databricks-core skills.

The experimental databricks-metric-view-advisor was folded into the stable databricks-metric-views skill — it now lives there as the references/metric-view-advisor.md reference and ships with that skill (no separate install).

Provenance

These skills are imported as a snapshot from databricks-solutions/ai-dev-kit/databricks-skills/.

Source SHA: 20a92a3 on the experimental branch of databricks-solutions/ai-dev-kit.

While ai-dev-kit is the upstream source, this directory receives periodic manual re-syncs.