# Databricks Docs

> Databricks documentation reference via llms.txt index. Use when other skills do not cover a topic, looking up unfamiliar Databricks features, or needing authoritative docs on APIs, configurations, or platform capabilities.

- Skill: `databrickslabs/databricks-docs` (Agent Skill)
- Install (CLI): `npx skillmds@latest add databrickslabs/databricks-docs`
- Raw SKILL.md: https://api.skillmd.com/api/skills/databrickslabs/databricks-docs/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: databrickslabs (https://skillmd.com/u/databrickslabs)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/databrickslabs/databricks-docs

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# Databricks Documentation Reference

This skill provides access to the complete Databricks documentation index via llms.txt - use it as a **reference resource** to supplement other skills and inform your use of MCP tools.

## Role of This Skill

This is a **reference skill**, not an action skill. Use it to:

- Look up documentation when other skills don't cover a topic
- Get authoritative guidance on Databricks concepts and APIs
- Find detailed information to inform how you use MCP tools
- Discover features and capabilities you may not know about

**Always prefer using MCP tools for actions** (execute_sql, manage_pipeline, etc.) and **load specific skills for workflows** (databricks-python-sdk, databricks-spark-declarative-pipelines, etc.). Use this skill when you need reference documentation.

## How to Use

Fetch the llms.txt documentation index:

**URL:** `https://docs.databricks.com/llms.txt`

Use WebFetch to retrieve this index, then:

1. Search for relevant sections/links
2. Fetch specific documentation pages for detailed guidance
3. Apply what you learn using the appropriate MCP tools

## Documentation Structure

The llms.txt file is organized by category:

- **Overview & Getting Started** - Basic concepts and tutorials
- **Data Engineering** - Lakeflow, Spark, Delta Lake, pipelines
- **SQL & Analytics** - Warehouses, queries, dashboards
- **AI/ML** - MLflow, model serving, GenAI
- **Governance** - Unity Catalog, permissions, security
- **Developer Tools** - SDKs, CLI, APIs, Terraform

## Example: Complementing Other Skills

**Scenario:** User wants to create a Delta Live Tables pipeline

1. Load `databricks-spark-declarative-pipelines` skill for workflow patterns
2. Use this skill to fetch docs if you need clarification on specific DLT features
3. Use `manage_pipeline(action="create_or_update")` MCP tool to actually create the pipeline

**Scenario:** User asks about an unfamiliar Databricks feature

1. Fetch llms.txt to find relevant documentation
2. Read the specific docs to understand the feature
3. Determine which skill/tools apply, then use them

## Related Skills

- **[databricks-python-sdk](../databricks-python-sdk/SKILL.md)** - SDK patterns for programmatic Databricks access
- **[databricks-spark-declarative-pipelines](../databricks-spark-declarative-pipelines/SKILL.md)** - DLT / Lakeflow pipeline workflows
- **[databricks-unity-catalog](../databricks-unity-catalog/SKILL.md)** - Governance and catalog management
- **[databricks-model-serving](../databricks-model-serving/SKILL.md)** - Serving endpoints and model deployment
- **[databricks-mlflow-evaluation](../databricks-mlflow-evaluation/SKILL.md)** - MLflow 3 GenAI evaluation workflows