Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate across workspaces, and ask questions of a *specific* Agent via the Conversation API. For general data questions or finding data across your workspace, use databricks-data-discovery (Genie One) instead.
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See how to installReview the instructions the agent will receive before you install.
Create, manage, and query Genie Agents (formerly Genie Spaces) - natural language interfaces for SQL-based data exploration.
Genie Agents allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally.
A Genie Agent is a curated agent scoped to specific data — its tables, sample questions, and instructions are authored for a particular business area. This is distinct from Genie One / the general "ask Genie" data-discovery path (see the databricks-data-discovery skill), which answers questions across your data without a curated, per-scope agent.
| Phase | Reference | Load when | Typical CLI |
|---|---|---|---|
| Design + Create | create-genie-agent.md | Always load before creating or updating. Gather requirements, profile data, design surfaces, get approval — before any CLI | discover-schema → create-space / update-space |
| Query / validate | query-genie-agent.md | Querying via Conversation API or Agent mode API; authoring SQL for Metric View sources | start-conversation / get-message |
| Diagnose | diagnose-genie-agent.md | Agent gives wrong/empty answers — gather space ID + failing question + observed behavior first | get-space --include-serialized-space; system.query.history |
| Optimize | optimize-genie-agent.md | Benchmark-driven quality tuning — gather space ID + optimization goal + benchmark target first | genie-create-eval-run; update-space |
| Export / migrate | genie-agent-cicd.md | Export, import, cross-workspace migration, batch migration, DABs/CI-CD | get-space → remap → create-space |
| — | serialized-space.md | Constructing or debugging serialized_space payloads — field schemas, constraints, Python helper | — |
| — | uc-persistence.md | Setting up UC Delta tables for multi-pass optimization history — CREATE TABLE DDL only | — |
Typical flow: create → query/validate → diagnose → optimize.
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Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).
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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).
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Databricks AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration.
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Create Databricks AI/BI dashboards. Must use when creating, updating, or deploying Lakeview dashboards as Databricks Dashboard have a unique json structure. CRITICAL: You MUST test ALL SQL queries via CLI BEFORE deploying. Follow guidelines strictly.
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Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.
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Build apps on Databricks Apps platform. Use when asked to create data apps, analytics tools, or custom interactive visualizations. A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.
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