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Use when the user wants to set up or fix one for Databricks Connect, cluster or serverless compute, `--job-task`, or a bundle target, or when setup-local fails.\"\ncompatibility: Requires databricks CLI (>= v1.12.0) and uv\nmetadata:\n  version: \"0.1.0\"\nparent: databricks-core\n---\n\n# Databricks Setup-Local\n\n**REQUIRED FIRST:** Use `databricks-core` for CLI, authentication, and profile selection. Never use a default profile. For an existing environment, use `databricks-execution-compute`.\n\n## Workflow\n\n### 1. Check CLI and authentication\n\n```bash\ndatabricks version                         # must be >= v1.12.0\ndatabricks auth describe --profile \u003CPROFILE>\n```\n\nPrefer the latest stable CLI; no online lookup is required. Compare the full semantic version: v0.299.1 is older than v1.12.0. If missing or older, or `setup-local` is absent from help, reports `unknown command`, or rejects a documented flag, stop. Use `databricks-core` to upgrade with approval and verify; never recreate `setup-local` manually.\n\nUse the selected profile for every workspace command. Do not convert another package manager without approval.\n\n### 2. Confirm the project directory\n\nConfirm the root containing (or intended to contain) `pyproject.toml`, `.venv`, and `uv.lock`; ask if multiple roots are plausible. Use it for preview and apply. It must be greenfield or `uv`-managed, but need not be writable for preview.\n\n### 3. Select one target\n\nChoose exactly one branch:\n\n- **Cluster:** use `--cluster-id \u003CID>` or `--cluster-name \u003CNAME>`. If unknown, list clusters with the selected profile and ask; see [examples](references\u002Fexamples.md).\n- **Serverless:** use `--serverless-version \u003CN>`. No version-list command exists; ask if unspecified.\n- **Job task:** use `--job-task \u003CJOB_ID>.\u003CTASK_KEY>`. If the task is unknown, run `databricks jobs get \u003CJOB_ID> --profile \u003CPROFILE> --output json`, present task keys, and ask.\n- **Bundle:** a project with `databricks.yml`. Use `databricks-dabs` to inspect its root and selected target. Omit compute flags only when that target resolves supported classic or serverless compute; otherwise ask. Add `--target \u003CBUNDLE_TARGET>` for a named target.\n\nNever combine compute flags. If no branch resolves, ask the user.\n\n### 4. Preview\n\nDry-run first; it writes and installs nothing:\n\n```bash\ndatabricks environments setup-local --profile \u003CPROFILE> \u003CTARGET_ARGS> --dry-run --output json\n```\n\nFor bundles, `\u003CTARGET_ARGS>` is empty or `--target \u003CBUNDLE_TARGET>`. Default to normal mode. Use `--constraints-only` only when the user explicitly does not want this command managing `databricks-connect`. See [JSON output](references\u002Fjson-output.md) and [examples](references\u002Fexamples.md).\n\n### 5. Obtain approval and apply\n\nBefore apply, verify the directory is writable and run `uv --version`. Ask before installing `uv`; never silently set `DATABRICKS_LOCALENV_AUTO_INSTALL_UV=1` or run a remote installer.\n\nShow the target, versions, warnings, `plan.diff`, and directory. Explain that apply may:\n\n- back up and rewrite `pyproject.toml`;\n- install Python and dependencies;\n- update `.venv` and `uv.lock`.\n\nFor `--serverless-version N` in a bundle, also disclose the post-apply job YAML synchronization described below so approval covers both mutations.\n\nApply only after the user requested provisioning or approves that plan for the named directory. Preserve the directory, profile, target, and mode.\n\nIf the directory, profile, target, mode, or project files change after preview, rerun `--dry-run`, show the new plan, and obtain approval again. Treat its resolved Python, `databricks-connect`, and managed constraints as authoritative; do not substitute guessed versions. 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Handle the result",{"type":43,"tag":266,"props":652,"children":653},{},[654,710,729],{"type":43,"tag":270,"props":655,"children":656},{},[657,663,665,670,672,678,680,686,688,694,696,702,704,709],{"type":43,"tag":63,"props":658,"children":660},{"className":659},[],[661],{"type":48,"value":662},"ok: true",{"type":48,"value":664},": for ",{"type":43,"tag":63,"props":666,"children":668},{"className":667},[],[669],{"type":48,"value":617},{"type":48,"value":671}," in a bundle, update every existing job ",{"type":43,"tag":63,"props":673,"children":675},{"className":674},[],[676],{"type":48,"value":677},"environments[].spec.environment_version",{"type":48,"value":679}," in its YAML sources to ",{"type":43,"tag":63,"props":681,"children":683},{"className":682},[],[684],{"type":48,"value":685},"\"N\"",{"type":48,"value":687},", then validate the bundle. Report if none exist; do not invent one. Skip this for cluster and job-task targets. Report target, versions, warnings, and ",{"type":43,"tag":63,"props":689,"children":691},{"className":690},[],[692],{"type":48,"value":693},"venvPath",{"type":48,"value":695},"; prefer ",{"type":43,"tag":63,"props":697,"children":699},{"className":698},[],[700],{"type":48,"value":701},"uv run \u003Ccmd>",{"type":48,"value":703}," or derive the platform-specific interpreter from ",{"type":43,"tag":63,"props":705,"children":707},{"className":706},[],[708],{"type":48,"value":693},{"type":48,"value":77},{"type":43,"tag":270,"props":711,"children":712},{},[713,719,721,727],{"type":43,"tag":63,"props":714,"children":716},{"className":715},[],[717],{"type":48,"value":718},"ok: false",{"type":48,"value":720},": use ",{"type":43,"tag":297,"props":722,"children":724},{"href":723},"references\u002Ftroubleshooting.md",[725],{"type":48,"value":726},"troubleshooting",{"type":48,"value":728},". Ask before diagnostic runs that mutate files and before filing an external issue.",{"type":43,"tag":270,"props":730,"children":731},{},[732],{"type":48,"value":733},"No JSON: inspect stderr for a pre-pipeline CLI, authentication, directory, or cache error.",{"type":43,"tag":735,"props":736,"children":737},"style",{},[738],{"type":48,"value":739},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":741,"total":926},[742,759,773,788,805,825,836,857,868,884,899,912],{"slug":743,"name":743,"fn":744,"description":745,"org":746,"tags":747,"stars":23,"repoUrl":24,"updatedAt":758},"databricks-agent-bricks","create Databricks Agent Bricks","Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[748,751,752,755],{"name":749,"slug":750,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":753,"slug":754,"type":15},"Knowledge Management","knowledge-management",{"name":756,"slug":757,"type":15},"Multi-Agent","multi-agent","2026-07-15T05:41:38.548954",{"slug":760,"name":760,"fn":761,"description":762,"org":763,"tags":764,"stars":23,"repoUrl":24,"updatedAt":772},"databricks-ai-functions","use Databricks built-in AI functions","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).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[765,768,769],{"name":766,"slug":767,"type":15},"Data Analysis","data-analysis",{"name":9,"slug":8,"type":15},{"name":770,"slug":771,"type":15},"LLM","llm","2026-07-31T05:53:33.562077",{"slug":774,"name":774,"fn":775,"description":776,"org":777,"tags":778,"stars":23,"repoUrl":24,"updatedAt":787},"databricks-ai-runtime","submit and manage Databricks GPU workloads","Databricks AI Runtime (`air`) CLI — the command-line tool for submitting and managing GPU training workloads on Databricks serverless compute. Use for: running `air` workloads, custom Docker image setup, environment configuration, and troubleshooting `air` jobs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[779,780,781,784],{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},{"name":782,"slug":783,"type":15},"Docker","docker",{"name":785,"slug":786,"type":15},"Engineering","engineering","2026-07-12T08:04:55.843982",{"slug":789,"name":789,"fn":790,"description":791,"org":792,"tags":793,"stars":23,"repoUrl":24,"updatedAt":804},"databricks-aibi-dashboards","create Databricks AI\u002FBI dashboards","Create Databricks AI\u002FBI 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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[794,797,800,803],{"name":795,"slug":796,"type":15},"Analytics","analytics",{"name":798,"slug":799,"type":15},"Dashboards","dashboards",{"name":801,"slug":802,"type":15},"Data Visualization","data-visualization",{"name":9,"slug":8,"type":15},"2026-09-02T07:19:02.297636",{"slug":806,"name":806,"fn":807,"description":808,"org":809,"tags":810,"stars":23,"repoUrl":24,"updatedAt":824},"databricks-app-design","design UX for Databricks AppKit applications","Design the UX of custom-code Databricks Apps (AppKit\u002FReact) data screens — KPI\u002Foverview pages, reports, charts, tables, and Genie\u002Fchat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit\u002FReact app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading\u002Fempty\u002Ferror), IBCS notation, and AI-result trust (showing generated SQL\u002Fsources for Genie\u002Fchat). A plain \"create a dashboard\" request means a managed AI\u002FBI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding\u002Fbuild\u002Fdeploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie\u002Fchat\u002FAI surface.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[811,812,815,818,821],{"name":9,"slug":8,"type":15},{"name":813,"slug":814,"type":15},"Design","design",{"name":816,"slug":817,"type":15},"Frontend","frontend",{"name":819,"slug":820,"type":15},"React","react",{"name":822,"slug":823,"type":15},"UI Components","ui-components","2026-07-12T08:04:02.02398",{"slug":826,"name":826,"fn":827,"description":828,"org":829,"tags":830,"stars":23,"repoUrl":24,"updatedAt":835},"databricks-apps","build applications on Databricks Apps","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\u002FBI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[831,832,833,834],{"name":795,"slug":796,"type":15},{"name":798,"slug":799,"type":15},{"name":766,"slug":767,"type":15},{"name":9,"slug":8,"type":15},"2026-08-29T09:27:14.582785",{"slug":837,"name":837,"fn":838,"description":839,"org":840,"tags":841,"stars":23,"repoUrl":24,"updatedAt":856},"databricks-apps-python","build Python backends for Databricks Apps","Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node\u002FTypeScript\u002FReact) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model \u002F Vector Search \u002F model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[842,843,846,849,852,853],{"name":9,"slug":8,"type":15},{"name":844,"slug":845,"type":15},"FastAPI","fastapi",{"name":847,"slug":848,"type":15},"Flask","flask",{"name":850,"slug":851,"type":15},"Gradio","gradio",{"name":20,"slug":21,"type":15},{"name":854,"slug":855,"type":15},"Streamlit","streamlit","2026-07-12T08:04:10.970845",{"slug":38,"name":38,"fn":858,"description":859,"org":860,"tags":861,"stars":23,"repoUrl":24,"updatedAt":867},"configure Databricks CLI and authentication","Databricks CLI operations and the parent\u002Fentry-point skill for Databricks CLI use: authentication, profile selection, and bundles. Load this first for CLI, auth, profile, and bundle tasks, then load the matching product skill. For finding or exploring data, answering questions about the data, or generating SQL, load the databricks-data-discovery skill (it routes to Genie One). Contains up-to-date guidelines for Databricks-related CLI tasks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[862,865,866],{"name":863,"slug":864,"type":15},"Authentication","authentication",{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},"2026-07-18T05:11:05.45506",{"slug":369,"name":369,"fn":869,"description":870,"org":871,"tags":872,"stars":23,"repoUrl":24,"updatedAt":883},"manage Databricks Declarative Automation Bundles","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[873,876,879,880],{"name":874,"slug":875,"type":15},"Automation","automation",{"name":877,"slug":878,"type":15},"Configuration","configuration",{"name":9,"slug":8,"type":15},{"name":881,"slug":882,"type":15},"Deployment","deployment","2026-07-15T05:41:35.930355",{"slug":885,"name":885,"fn":886,"description":887,"org":888,"tags":889,"stars":23,"repoUrl":24,"updatedAt":898},"databricks-data-discovery","discover and query Databricks data","Discover, explore, and query Databricks data via Genie — the CLI equivalent of the Genie One MCP. MUST be invoked whenever the user asks to find or locate data ('what tables are in X', 'where does X live', 'which catalog\u002Fschema has Y'), answer a natural-language question about the data, or write a SQL query.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[890,891,894,895],{"name":766,"slug":767,"type":15},{"name":892,"slug":893,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":896,"slug":897,"type":15},"SQL","sql","2026-07-31T05:53:32.561877",{"slug":900,"name":900,"fn":901,"description":902,"org":903,"tags":904,"stars":23,"repoUrl":24,"updatedAt":911},"databricks-dbsql","query and script Databricks SQL warehouses","Databricks SQL (DBSQL) advanced features and SQL warehouse capabilities. This skill MUST be invoked when the user mentions: \"DBSQL\", \"Databricks SQL\", \"SQL warehouse\", \"SQL scripting\", \"stored procedure\", \"CALL procedure\", \"materialized view\", \"CREATE MATERIALIZED VIEW\", \"pipe syntax\", \"|>\", \"geospatial\", \"H3\", \"ST_\", \"spatial SQL\", \"collation\", \"COLLATE\", \"ai_query\", \"ai_classify\", \"ai_extract\", \"ai_gen\", \"AI function\", \"http_request\", \"remote_query\", \"read_files\", \"Lakehouse Federation\", \"recursive CTE\", \"WITH RECURSIVE\", \"multi-statement transaction\", \"temp table\", \"temporary view\", \"pipe operator\". SHOULD also invoke when the user asks about SQL best practices, data modeling patterns, or advanced SQL features on Databricks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[905,906,909,910],{"name":766,"slug":767,"type":15},{"name":907,"slug":908,"type":15},"Database","database",{"name":9,"slug":8,"type":15},{"name":896,"slug":897,"type":15},"2026-07-12T08:04:08.678282",{"slug":913,"name":913,"fn":914,"description":915,"org":916,"tags":917,"stars":23,"repoUrl":24,"updatedAt":925},"databricks-docs","search Databricks documentation","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[918,919,922],{"name":9,"slug":8,"type":15},{"name":920,"slug":921,"type":15},"Documentation","documentation",{"name":923,"slug":924,"type":15},"Reference","reference","2026-07-15T05:41:34.697746",32,{"items":928,"total":926},[929,936,942,949,956,964,971],{"slug":743,"name":743,"fn":744,"description":745,"org":930,"tags":931,"stars":23,"repoUrl":24,"updatedAt":758},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[932,933,934,935],{"name":749,"slug":750,"type":15},{"name":9,"slug":8,"type":15},{"name":753,"slug":754,"type":15},{"name":756,"slug":757,"type":15},{"slug":760,"name":760,"fn":761,"description":762,"org":937,"tags":938,"stars":23,"repoUrl":24,"updatedAt":772},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[939,940,941],{"name":766,"slug":767,"type":15},{"name":9,"slug":8,"type":15},{"name":770,"slug":771,"type":15},{"slug":774,"name":774,"fn":775,"description":776,"org":943,"tags":944,"stars":23,"repoUrl":24,"updatedAt":787},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[945,946,947,948],{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},{"name":782,"slug":783,"type":15},{"name":785,"slug":786,"type":15},{"slug":789,"name":789,"fn":790,"description":791,"org":950,"tags":951,"stars":23,"repoUrl":24,"updatedAt":804},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[952,953,954,955],{"name":795,"slug":796,"type":15},{"name":798,"slug":799,"type":15},{"name":801,"slug":802,"type":15},{"name":9,"slug":8,"type":15},{"slug":806,"name":806,"fn":807,"description":808,"org":957,"tags":958,"stars":23,"repoUrl":24,"updatedAt":824},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[959,960,961,962,963],{"name":9,"slug":8,"type":15},{"name":813,"slug":814,"type":15},{"name":816,"slug":817,"type":15},{"name":819,"slug":820,"type":15},{"name":822,"slug":823,"type":15},{"slug":826,"name":826,"fn":827,"description":828,"org":965,"tags":966,"stars":23,"repoUrl":24,"updatedAt":835},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[967,968,969,970],{"name":795,"slug":796,"type":15},{"name":798,"slug":799,"type":15},{"name":766,"slug":767,"type":15},{"name":9,"slug":8,"type":15},{"slug":837,"name":837,"fn":838,"description":839,"org":972,"tags":973,"stars":23,"repoUrl":24,"updatedAt":856},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[974,975,976,977,978,979],{"name":9,"slug":8,"type":15},{"name":844,"slug":845,"type":15},{"name":847,"slug":848,"type":15},{"name":850,"slug":851,"type":15},{"name":20,"slug":21,"type":15},{"name":854,"slug":855,"type":15}]