By rsram27
Databricks development toolkit with skills for data engineering, ML, and AI agents plus MCP tools for direct Databricks operations
Generate PDF documents from HTML and upload to Unity Catalog volumes. Use for creating test PDFs, demo documents, reports, or evaluation datasets.
Create and manage Databricks Agent Bricks: Knowledge Assistants (KA) for document Q&A, Genie Spaces for SQL exploration, and Supervisor Agents (MAS) for multi-agent orchestration. Use when building conversational AI applications on Databricks.
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.
A brief one-sentence description of what this skill helps with.
Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).
Admin access level
Server config contains admin-level keywords
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📣 Heads up: a major evolution is coming
This will be the last release where AI Dev Kit skills are installed from the skill files in this repository. AI Dev Kit skills are becoming part of the official, engineering-supported Databricks skills set, and the skill files in this repo will soon be deprecated. In the next release you'll install these skills directly from the official Databricks set — either straight from the CLI, or through the AI Dev Kit installer, which will continue to guide you through the process.
What stays: The MCP server and Builder App will remain in this repository. The Builder App will keep being developed and improved, and the MCP server will be maintained and updated on a best-effort basis as GitHub issues are filed.
What's next: AI Dev Kit will continue to guide you through setting up your AI coding environment and be a place to find experimental tools developed by Field Engineering. Beyond the skills installs, we plan to add several tutorials to help you get started using coding agents for building on Databricks, including getting started with Genie Code or Omnigent.
A few skills will be renamed or merged in the official install. Most names are unchanged; the exceptions are:
Today (AI Dev Kit) Official Databricks skills databricks-bundlesdatabricks-dabsdatabricks-spark-declarative-pipelinesdatabricks-pipelinesdatabricks-lakebase-autoscale,databricks-lakebase-provisioneddatabricks-lakebase(merged)databricks-configfolded into databricks-core
🔒 Proactive Dependency Security
As part of our commitment to supply chain integrity, we continually monitor our dependency tree against known vulnerabilities and industry advisories. In response to a recently disclosed supply chain incident affecting litellm versions 1.82.7–1.82.8, we have audited our packages and removed the litellm dependency for most usage. It is solely used in the test directory for skills evaluation and optimization, and has been pinned to a safe version.
For full third-party attribution, see NOTICE.txt.
Databricks offers two paths for AI-assisted coding. Choose the one that matches your environment.
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Free, first-party AI coding inside Databricks Built into every Databricks workspace at no extra cost, with deep native product context — your notebooks, jobs, and Unity Catalog data are already in scope. Ideal for users who have not started using AI-driven development tools or that are comfortable in Databricks. |
Databricks expertise, in the editor you already use Curated by Databricks field experts. Brings the patterns, skills, and 75+ executable tools your AI assistant needs to build on Databricks — wherever you're already coding. + Antigravity · Windsurf · OpenCode · and more! |
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