[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-databricks-databricks-unity-catalog":3,"mdc-xpf2nr-key":36,"related-repo-databricks-databricks-unity-catalog":2349,"related-org-databricks-databricks-unity-catalog":2468},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":32,"sourceUrl":34,"mdContent":35},"databricks-unity-catalog","manage Unity Catalog governance and access","Unity Catalog governance, access control, and observability. Use to grant or revoke access (GRANT\u002FREVOKE), reason about the privilege model and ownership, set up row-level security and column masks, create external locations and storage credentials, define catalogs\u002Fschemas\u002Ftables\u002Fvolumes, answer \"who can read this table\", and query system tables (audit, lineage, billing) or work with volume files in \u002FVolumes\u002F.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"databricks","Databricks","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fdatabricks.png",[12,16,19,22,23],{"name":13,"slug":14,"type":15},"Security","security","tag",{"name":17,"slug":18,"type":15},"Observability","observability",{"name":20,"slug":21,"type":15},"Governance","governance",{"name":9,"slug":8,"type":15},{"name":24,"slug":25,"type":15},"Access Control","access-control",204,"https:\u002F\u002Fgithub.com\u002Fdatabricks\u002Fdatabricks-agent-skills","2026-07-12T08:04:28.262932",null,60,[],{"repoUrl":27,"stars":26,"forks":30,"topics":33,"description":29},[],"https:\u002F\u002Fgithub.com\u002Fdatabricks\u002Fdatabricks-agent-skills\u002Ftree\u002FHEAD\u002Fplugins\u002Fdatabricks\u002Fcopilot\u002Fskills\u002Fdatabricks-unity-catalog","---\nname: databricks-unity-catalog\ndescription: \"Unity Catalog governance, access control, and observability. Use to grant or revoke access (GRANT\u002FREVOKE), reason about the privilege model and ownership, set up row-level security and column masks, create external locations and storage credentials, define catalogs\u002Fschemas\u002Ftables\u002Fvolumes, answer \\\"who can read this table\\\", and query system tables (audit, lineage, billing) or work with volume files in \u002FVolumes\u002F.\"\ncompatibility: Requires databricks CLI (>= v1.0.0)\nmetadata:\n  version: \"0.3.0\"\nparent: databricks-core\n---\n\n# Unity Catalog\n\nGuidance for Unity Catalog **governance** — access control, the privilege model,\nexternal locations, securable DDL, and fine-grained access — plus system tables and\nvolume file operations.\n\n> **Before running `databricks` CLI commands, confirm the CLI and the subcommand exist.**\n> Run `databricks --version` — this skill assumes the unified CLI (**≥ v1.0.0**). Several\n> subcommands shown here (`experimental aitools`, `system-schemas`, `external-lineage`,\n> `grants`) vary by version or workspace availability; if one is missing or rejects a flag,\n> fall back to the SQL form or the Python SDK rather than guessing. Each reference notes its\n> own version floor where relevant.\n\n## When to Use This Skill\n\nUse this skill when:\n\n**Governance & access control (start here):**\n- **Granting or revoking access** — `GRANT`\u002F`REVOKE`, the UC privilege model, ownership (`ALTER … OWNER TO`), `SHOW GRANTS`, \"who can read\u002Fwrite this table?\"\n- **Row- and column-level security** — row filters, column masks, dynamic views with `current_user()` \u002F `is_account_group_member()`\n- **External locations & storage credentials** — `CREATE STORAGE CREDENTIAL`, `CREATE EXTERNAL LOCATION`, backing external tables\u002Fvolumes\n- **Securable DDL & metadata** — creating\u002Faltering catalogs, schemas, managed vs external tables, views; comments, tags, table properties, ownership\n\n**Observability & files:**\n- Working with **volumes** (upload, download, list files in `\u002FVolumes\u002F`)\n- Querying **lineage** (table dependencies, column-level lineage)\n- Analyzing **audit logs** (who accessed what, permission changes)\n- Monitoring **billing and usage** (DBU consumption, cost analysis)\n- Tracking **compute resources** (cluster usage, warehouse metrics)\n- Reviewing **job execution** (run history, success rates, failures)\n- Analyzing **query performance** (slow queries, warehouse utilization)\n- Profiling **data quality** (data profiling, drift detection, metric tables)\n\n## Reference Files\n\n| Topic | File | Description |\n|-------|------|-------------|\n| **Access Control** | [references\u002F1-access-control.md](references\u002F1-access-control.md) | Privilege model, securable hierarchy, GRANT\u002FREVOKE, ownership, inheritance, `SHOW GRANTS` |\n| **External Locations** | [references\u002F2-external-locations.md](references\u002F2-external-locations.md) | Storage credentials (AWS\u002FAzure\u002FGCP), external locations, validation |\n| **Securables DDL** | [references\u002F3-securables-ddl.md](references\u002F3-securables-ddl.md) | CREATE\u002FALTER\u002FDROP catalogs\u002Fschemas\u002Ftables\u002Fviews, comments, tags, ownership |\n| **Fine-Grained Access** | [references\u002F4-fine-grained-access.md](references\u002F4-fine-grained-access.md) | Row filters, column masks, dynamic views |\n| System Tables | [references\u002F5-system-tables.md](references\u002F5-system-tables.md) | Lineage, audit, billing, compute, jobs, query history |\n| Volumes | [references\u002F6-volumes.md](references\u002F6-volumes.md) | Volume file operations, permissions, best practices |\n| Data Profiling | [references\u002F7-data-profiling.md](references\u002F7-data-profiling.md) | Data profiling, drift detection, profile metrics |\n\n## Quick Start\n\n### Create Unity Catalog Objects (CLI)\n\n**Use `--json` for `create` commands.** Positional argument order differs per command and\nhas changed across CLI versions, so `--json` is the order-independent, version-stable form\nshown throughout this skill.\n\n```bash\n# Create a catalog\ndatabricks catalogs create --json '{\"name\": \"my_catalog\"}'\n\n# Create a schema\ndatabricks schemas create --json '{\"name\": \"my_schema\", \"catalog_name\": \"my_catalog\"}'\n\n# Create a managed volume\ndatabricks volumes create --json '{\n  \"catalog_name\": \"my_catalog\",\n  \"schema_name\": \"my_schema\",\n  \"name\": \"my_volume\",\n  \"volume_type\": \"MANAGED\"\n}'\n\n# List catalogs, schemas, volumes (read commands take simple positional args)\ndatabricks catalogs list\ndatabricks schemas list my_catalog\ndatabricks volumes list my_catalog.my_schema\n```\n\nPositional `create` args still work if you prefer them, but the order is **not** uniform\nacross commands — this is the per-command order (and the reason `--json` is recommended):\n\n| Command | Positional `create` order |\n|---------|---------------------------|\n| `databricks catalogs create` | `NAME` |\n| `databricks schemas create`  | `NAME CATALOG_NAME` |\n| `databricks volumes create`  | `CATALOG_NAME SCHEMA_NAME NAME VOLUME_TYPE` |\n\n> **CLI surface varies by version.** If a `databricks` subcommand or positional signature is\n> missing in your install, prefer `--json`, the SQL form, or the Python SDK rather than\n> guessing flags.\n\n### Volume File Operations (CLI)\n\n`databricks fs` requires the `dbfs:` scheme prefix even for UC Volume paths — without it the CLI treats the path as local filesystem and errors with `no such directory`.\n\n```bash\n# List files in a volume\ndatabricks fs ls dbfs:\u002FVolumes\u002Fcatalog\u002Fschema\u002Fvolume\u002Fpath\u002F\n\n# Upload a directory's contents to a volume (-r copies contents, not the directory itself)\ndatabricks fs cp -r --overwrite \u002Ftmp\u002Fdata dbfs:\u002FVolumes\u002Fcatalog\u002Fschema\u002Fvolume\u002Fdest\n\n# Download a file from a volume\ndatabricks fs cp dbfs:\u002FVolumes\u002Fcatalog\u002Fschema\u002Fvolume\u002Ffile.csv \u002Ftmp\u002Ffile.csv\n\n# Create a directory in a volume\ndatabricks fs mkdirs dbfs:\u002FVolumes\u002Fcatalog\u002Fschema\u002Fvolume\u002Fnew_folder\n```\n\n### Grant & Revoke Access\n\n`GRANT`\u002F`REVOKE` is the core governance operation. See [references\u002F1-access-control.md](references\u002F1-access-control.md) for the full privilege model.\n\n```sql\n-- Grant read access on a schema to a group\nGRANT USE CATALOG ON CATALOG analytics TO `data_readers`;\nGRANT USE SCHEMA ON SCHEMA analytics.gold TO `data_readers`;\nGRANT SELECT ON SCHEMA analytics.gold TO `data_readers`;\n\n-- Who can access this table?\nSHOW GRANTS ON TABLE analytics.gold.customers;\n\n-- Revoke\nREVOKE SELECT ON SCHEMA analytics.gold FROM `data_readers`;\n```\n\n### Enable System Tables Access\n\n```sql\n-- Grant access to system tables\nGRANT USE CATALOG ON CATALOG system TO `data_engineers`;\nGRANT USE SCHEMA ON SCHEMA system.access TO `data_engineers`;\nGRANT SELECT ON SCHEMA system.access TO `data_engineers`;\n```\n\n### Common Queries\n\n```sql\n-- Table lineage: What tables feed into this table?\nSELECT source_table_full_name, source_column_name\nFROM system.access.table_lineage\nWHERE target_table_full_name = 'catalog.schema.table'\n  AND event_date >= current_date() - 7;\n\n-- Audit: Recent permission changes\nSELECT event_time, user_identity.email, action_name, request_params\nFROM system.access.audit\nWHERE action_name LIKE '%GRANT%' OR action_name LIKE '%REVOKE%'\nORDER BY event_time DESC\nLIMIT 100;\n\n-- Billing: DBU usage by workspace\nSELECT workspace_id, sku_name, SUM(usage_quantity) AS total_dbus\nFROM system.billing.usage\nWHERE usage_date >= current_date() - 30\nGROUP BY workspace_id, sku_name;\n```\n\n## Running SQL from the CLI\n\n> **`databricks experimental aitools tools query` is an experimental command.** The\n> `experimental` namespace is not guaranteed to be stable across CLI versions and may be\n> absent in your install. Prefer running system-table SQL from a **SQL warehouse** (SQL\n> editor, scheduled query) or the **Python SDK** (`w.statement_execution.execute_statement`),\n> or a **notebook**. Use the experimental CLI only for quick ad-hoc checks.\n\n> **Getting the IDs these examples use.** `WAREHOUSE_ID` — run `databricks warehouses list`\n> (or copy it from a SQL warehouse's *Connection details* in the UI). `METASTORE_ID` (used in\n> [references\u002F5-system-tables.md](references\u002F5-system-tables.md)) — `w.metastores.current().metastore_id`\n> via the SDK, or the Catalog UI → metastore details.\n\nExperimental CLI form (convenience only):\n\n```bash\ndatabricks experimental aitools tools query --warehouse WAREHOUSE_ID \"\n  SELECT source_table_full_name, target_table_full_name\n  FROM system.access.table_lineage\n  WHERE event_date >= current_date() - 7\n\"\n```\n\nStable SDK fallback (works on any CLI version):\n\n```python\nfrom databricks.sdk import WorkspaceClient\n\nw = WorkspaceClient()\nresp = w.statement_execution.execute_statement(\n    warehouse_id=\"WAREHOUSE_ID\",\n    statement=\"\"\"\n        SELECT source_table_full_name, target_table_full_name\n        FROM system.access.table_lineage\n        WHERE event_date >= current_date() - 7\n        LIMIT 100\n    \"\"\",\n)\nfor row in resp.result.data_array or []:\n    print(row)\n```\n\n> **CLI surface varies by version.** If a `databricks` subcommand (e.g. an `experimental`\n> tool, `system-schemas`, or `external-lineage`) is missing, fall back to the SQL warehouse\n> or the Python SDK shown above rather than guessing flags.\n\n## Best Practices\n\n1. **Grant minimal access** - Apply least privilege; grant at the narrowest securable that works\n2. **Filter by date** - System tables can be large; always use date filters\n3. **Use appropriate retention** - Check your workspace's retention settings\n4. **Schedule reports** - Create scheduled queries for regular monitoring\n5. **Prefer SQL\u002FSDK over experimental CLI** - For anything beyond quick checks\n\n## Related Skills\n\nThis skill owns Unity Catalog **governance**: access control, the privilege model,\nexternal locations \u002F storage credentials, securable DDL, fine-grained access, system\ntables, and volumes. For adjacent concerns, use the sibling skill instead:\n\n- **databricks-core** (declared parent) — auth, profile selection, generic CLI, and catalog\u002Ftable *exploration*\n- **[databricks-metric-views](..\u002Fdatabricks-metric-views\u002FSKILL.md)** — metric view definitions \u002F DDL (`WITH METRICS LANGUAGE YAML`)\n- **[databricks-iceberg](..\u002Fdatabricks-iceberg\u002FSKILL.md)** — Managed Iceberg, External Iceberg Reads (fka Uniform), and **Iceberg REST Catalog (IRC) credential *vending*** for external engines — distinct from UC storage credentials (see [references\u002F2-external-locations.md](references\u002F2-external-locations.md))\n- **[databricks-ml-training](..\u002Fdatabricks-ml-training\u002FSKILL.md)** — UC model registration and `@prod`\u002F`@challenger` aliases\n- **[databricks-vector-search](..\u002Fdatabricks-vector-search\u002FSKILL.md)** — Vector Search indexes\n- **[databricks-pipelines](..\u002Fdatabricks-pipelines\u002FSKILL.md)**, **[databricks-jobs](..\u002Fdatabricks-jobs\u002FSKILL.md)**, **[databricks-lakeflow-connect](..\u002Fdatabricks-lakeflow-connect\u002FSKILL.md)** — *producing* tables via pipelines\u002Fjobs\u002Fmanaged ingestion\n- **[databricks-lakebase](..\u002Fdatabricks-lakebase\u002FSKILL.md)** — Lakebase \u002F synced tables (OLTP)\n- **[databricks-ai-functions](..\u002Fdatabricks-ai-functions\u002FSKILL.md)** — AI functions such as `ai_mask` \u002F `ai_classify` (AI *transforms*, **not** access control — see [references\u002F4-fine-grained-access.md](references\u002F4-fine-grained-access.md))\n- **[databricks-aibi-dashboards](..\u002Fdatabricks-aibi-dashboards\u002FSKILL.md)** — AI\u002FBI dashboards on UC data\n- **[databricks-synthetic-data-gen](..\u002Fdatabricks-synthetic-data-gen\u002FSKILL.md)** — generating data stored in UC volumes\n\n### Roadmap (not yet covered — deferred to a later version)\n\nThese governance areas are intentionally **out of scope for v0.3.0** and planned for later:\n\n- Delta Sharing \u002F Marketplace \u002F Clean Rooms\n- Lakehouse Federation (connections + foreign catalogs)\n- ABAC \u002F governed tags as policy\n\n## Resources\n\n- [Unity Catalog Privileges & Securable Objects](https:\u002F\u002Fdocs.databricks.com\u002Fdata-governance\u002Funity-catalog\u002Fmanage-privileges\u002Fprivileges.html)\n- [Unity Catalog System Tables](https:\u002F\u002Fdocs.databricks.com\u002Fadministration-guide\u002Fsystem-tables\u002F)\n- [Audit Log Reference](https:\u002F\u002Fdocs.databricks.com\u002Fadministration-guide\u002Faccount-settings\u002Faudit-logs.html)\n- [Manage External Locations and Storage Credentials](https:\u002F\u002Fdocs.databricks.com\u002Fconnect\u002Funity-catalog\u002Findex.html)\n",{"data":37,"body":42},{"name":4,"description":6,"compatibility":38,"metadata":39,"parent":41},"Requires databricks CLI (>= v1.0.0)",{"version":40},"0.3.0","databricks-core",{"type":43,"children":44},"root",[45,54,67,135,142,147,155,260,268,374,380,577,583,590,623,913,939,1030,1057,1063,1090,1254,1260,1282,1369,1375,1414,1420,1569,1575,1631,1690,1695,1776,1781,1901,1940,1946,2000,2006,2017,2257,2263,2275,2293,2299,2343],{"type":46,"tag":47,"props":48,"children":50},"element","h1",{"id":49},"unity-catalog",[51],{"type":52,"value":53},"text","Unity Catalog",{"type":46,"tag":55,"props":56,"children":57},"p",{},[58,60,65],{"type":52,"value":59},"Guidance for Unity Catalog ",{"type":46,"tag":61,"props":62,"children":63},"strong",{},[64],{"type":52,"value":21},{"type":52,"value":66}," — access control, the privilege model,\nexternal locations, securable DDL, and fine-grained access — plus system tables and\nvolume file operations.",{"type":46,"tag":68,"props":69,"children":70},"blockquote",{},[71],{"type":46,"tag":55,"props":72,"children":73},{},[74,87,89,95,97,102,104,110,112,118,119,125,127,133],{"type":46,"tag":61,"props":75,"children":76},{},[77,79,85],{"type":52,"value":78},"Before running ",{"type":46,"tag":80,"props":81,"children":83},"code",{"className":82},[],[84],{"type":52,"value":8},{"type":52,"value":86}," CLI commands, confirm the CLI and the subcommand exist.",{"type":52,"value":88},"\nRun ",{"type":46,"tag":80,"props":90,"children":92},{"className":91},[],[93],{"type":52,"value":94},"databricks --version",{"type":52,"value":96}," — this skill assumes the unified CLI (",{"type":46,"tag":61,"props":98,"children":99},{},[100],{"type":52,"value":101},"≥ v1.0.0",{"type":52,"value":103},"). 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(MAS).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2357,2360,2361,2364],{"name":2358,"slug":2359,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":2362,"slug":2363,"type":15},"Knowledge Management","knowledge-management",{"name":2365,"slug":2366,"type":15},"Multi-Agent","multi-agent","2026-07-15T05:41:38.548954",{"slug":2194,"name":2194,"fn":2369,"description":2370,"org":2371,"tags":2372,"stars":26,"repoUrl":27,"updatedAt":2380},"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},[2373,2376,2377],{"name":2374,"slug":2375,"type":15},"Data Analysis","data-analysis",{"name":9,"slug":8,"type":15},{"name":2378,"slug":2379,"type":15},"LLM","llm","2026-07-31T05:53:33.562077",{"slug":2382,"name":2382,"fn":2383,"description":2384,"org":2385,"tags":2386,"stars":26,"repoUrl":27,"updatedAt":2397},"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},[2387,2390,2391,2394],{"name":2388,"slug":2389,"type":15},"CLI","cli",{"name":9,"slug":8,"type":15},{"name":2392,"slug":2393,"type":15},"Docker","docker",{"name":2395,"slug":2396,"type":15},"Engineering","engineering","2026-07-12T08:04:55.843982",{"slug":2240,"name":2240,"fn":2399,"description":2400,"org":2401,"tags":2402,"stars":26,"repoUrl":27,"updatedAt":2413},"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},[2403,2406,2409,2412],{"name":2404,"slug":2405,"type":15},"Analytics","analytics",{"name":2407,"slug":2408,"type":15},"Dashboards","dashboards",{"name":2410,"slug":2411,"type":15},"Data Visualization","data-visualization",{"name":9,"slug":8,"type":15},"2026-07-12T08:04:25.314591",{"slug":2415,"name":2415,"fn":2416,"description":2417,"org":2418,"tags":2419,"stars":26,"repoUrl":27,"updatedAt":2433},"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},[2420,2421,2424,2427,2430],{"name":9,"slug":8,"type":15},{"name":2422,"slug":2423,"type":15},"Design","design",{"name":2425,"slug":2426,"type":15},"Frontend","frontend",{"name":2428,"slug":2429,"type":15},"React","react",{"name":2431,"slug":2432,"type":15},"UI Components","ui-components","2026-07-12T08:04:02.02398",{"slug":2435,"name":2435,"fn":2436,"description":2437,"org":2438,"tags":2439,"stars":26,"repoUrl":27,"updatedAt":2444},"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},[2440,2441,2442,2443],{"name":2404,"slug":2405,"type":15},{"name":2407,"slug":2408,"type":15},{"name":2374,"slug":2375,"type":15},{"name":9,"slug":8,"type":15},"2026-07-12T08:03:59.061458",{"slug":2446,"name":2446,"fn":2447,"description":2448,"org":2449,"tags":2450,"stars":26,"repoUrl":27,"updatedAt":2466},"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},[2451,2452,2455,2458,2461,2463],{"name":9,"slug":8,"type":15},{"name":2453,"slug":2454,"type":15},"FastAPI","fastapi",{"name":2456,"slug":2457,"type":15},"Flask","flask",{"name":2459,"slug":2460,"type":15},"Gradio","gradio",{"name":2462,"slug":1785,"type":15},"Python",{"name":2464,"slug":2465,"type":15},"Streamlit","streamlit","2026-07-12T08:04:10.970845",31,{"items":2469,"total":2467},[2470,2477,2483,2490,2497,2505,2512,2521,2532,2549,2563,2576],{"slug":2352,"name":2352,"fn":2353,"description":2354,"org":2471,"tags":2472,"stars":26,"repoUrl":27,"updatedAt":2367},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2473,2474,2475,2476],{"name":2358,"slug":2359,"type":15},{"name":9,"slug":8,"type":15},{"name":2362,"slug":2363,"type":15},{"name":2365,"slug":2366,"type":15},{"slug":2194,"name":2194,"fn":2369,"description":2370,"org":2478,"tags":2479,"stars":26,"repoUrl":27,"updatedAt":2380},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2480,2481,2482],{"name":2374,"slug":2375,"type":15},{"name":9,"slug":8,"type":15},{"name":2378,"slug":2379,"type":15},{"slug":2382,"name":2382,"fn":2383,"description":2384,"org":2484,"tags":2485,"stars":26,"repoUrl":27,"updatedAt":2397},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2486,2487,2488,2489],{"name":2388,"slug":2389,"type":15},{"name":9,"slug":8,"type":15},{"name":2392,"slug":2393,"type":15},{"name":2395,"slug":2396,"type":15},{"slug":2240,"name":2240,"fn":2399,"description":2400,"org":2491,"tags":2492,"stars":26,"repoUrl":27,"updatedAt":2413},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2493,2494,2495,2496],{"name":2404,"slug":2405,"type":15},{"name":2407,"slug":2408,"type":15},{"name":2410,"slug":2411,"type":15},{"name":9,"slug":8,"type":15},{"slug":2415,"name":2415,"fn":2416,"description":2417,"org":2498,"tags":2499,"stars":26,"repoUrl":27,"updatedAt":2433},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2500,2501,2502,2503,2504],{"name":9,"slug":8,"type":15},{"name":2422,"slug":2423,"type":15},{"name":2425,"slug":2426,"type":15},{"name":2428,"slug":2429,"type":15},{"name":2431,"slug":2432,"type":15},{"slug":2435,"name":2435,"fn":2436,"description":2437,"org":2506,"tags":2507,"stars":26,"repoUrl":27,"updatedAt":2444},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2508,2509,2510,2511],{"name":2404,"slug":2405,"type":15},{"name":2407,"slug":2408,"type":15},{"name":2374,"slug":2375,"type":15},{"name":9,"slug":8,"type":15},{"slug":2446,"name":2446,"fn":2447,"description":2448,"org":2513,"tags":2514,"stars":26,"repoUrl":27,"updatedAt":2466},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2515,2516,2517,2518,2519,2520],{"name":9,"slug":8,"type":15},{"name":2453,"slug":2454,"type":15},{"name":2456,"slug":2457,"type":15},{"name":2459,"slug":2460,"type":15},{"name":2462,"slug":1785,"type":15},{"name":2464,"slug":2465,"type":15},{"slug":41,"name":41,"fn":2522,"description":2523,"org":2524,"tags":2525,"stars":26,"repoUrl":27,"updatedAt":2531},"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},[2526,2529,2530],{"name":2527,"slug":2528,"type":15},"Authentication","authentication",{"name":2388,"slug":2389,"type":15},{"name":9,"slug":8,"type":15},"2026-07-18T05:11:05.45506",{"slug":2533,"name":2533,"fn":2534,"description":2535,"org":2536,"tags":2537,"stars":26,"repoUrl":27,"updatedAt":2548},"databricks-dabs","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},[2538,2541,2544,2545],{"name":2539,"slug":2540,"type":15},"Automation","automation",{"name":2542,"slug":2543,"type":15},"Configuration","configuration",{"name":9,"slug":8,"type":15},{"name":2546,"slug":2547,"type":15},"Deployment","deployment","2026-07-15T05:41:35.930355",{"slug":2550,"name":2550,"fn":2551,"description":2552,"org":2553,"tags":2554,"stars":26,"repoUrl":27,"updatedAt":2562},"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},[2555,2556,2559,2560],{"name":2374,"slug":2375,"type":15},{"name":2557,"slug":2558,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":2561,"slug":1286,"type":15},"SQL","2026-07-31T05:53:32.561877",{"slug":2564,"name":2564,"fn":2565,"description":2566,"org":2567,"tags":2568,"stars":26,"repoUrl":27,"updatedAt":2575},"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},[2569,2570,2573,2574],{"name":2374,"slug":2375,"type":15},{"name":2571,"slug":2572,"type":15},"Database","database",{"name":9,"slug":8,"type":15},{"name":2561,"slug":1286,"type":15},"2026-07-12T08:04:08.678282",{"slug":2577,"name":2577,"fn":2578,"description":2579,"org":2580,"tags":2581,"stars":26,"repoUrl":27,"updatedAt":2589},"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},[2582,2583,2586],{"name":9,"slug":8,"type":15},{"name":2584,"slug":2585,"type":15},"Documentation","documentation",{"name":2587,"slug":2588,"type":15},"Reference","reference","2026-07-15T05:41:34.697746"]