[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-databricks-databricks-core":3,"mdc--jvczwp-key":30,"related-repo-databricks-databricks-core":2120,"related-org-databricks-databricks-core":2238},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":20,"repoUrl":21,"updatedAt":22,"license":23,"forks":24,"topics":25,"repo":26,"sourceUrl":28,"mdContent":29},"databricks-core","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). 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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.\"\ncompatibility: Requires databricks CLI (>= v0.292.0)\nmetadata:\n  version: \"0.1.0\"\n---\n\n# Databricks\n\nCore skill for Databricks CLI, authentication, and data exploration.\n\n## Product Skills\n\nFor specific products, use dedicated skills:\n- **databricks-jobs** - Lakeflow Jobs development and deployment\n- **databricks-pipelines** - Lakeflow Spark Declarative Pipelines (batch and streaming data pipelines)\n- **databricks-apps** - Full-stack TypeScript app development and deployment\n- **databricks-lakebase** - Lakebase Postgres Autoscaling project management\n- **databricks-model-serving** - Model Serving endpoint management and inference\n\nFor **data discovery, exploration, and query generation** — finding tables,\nanswering natural-language questions about the data, or generating SQL — use\n**databricks-data-discovery** (it asks Genie One first, then falls back to manual\nexploration). If it isn't installed, use the AI-tool commands below and\n[Manual Data Exploration](manual-data-exploration.md).\n\n## Prerequisites\n\n1. **CLI installed**: Run `databricks --version` to check.\n   - **If the CLI is missing or outdated (\u003C v0.292.0): STOP. Do not proceed or work around a missing CLI.**\n   - **Read the [CLI Installation](databricks-cli-install.md) reference file and follow the instructions to guide the user through installation.**\n   - Note: In sandboxed environments (Cursor IDE, containers), install commands write outside the workspace and may be blocked. Present the install command to the user and ask them to run it in their own terminal.\n   - **Exception:** If CLI installation is blocked (sandboxed containers, restricted environments), ask the user whether to fall back to direct REST API calls using `DATABRICKS_HOST` and `DATABRICKS_TOKEN` environment variables if present in the shell. See the [Databricks REST API docs](https:\u002F\u002Fdocs.databricks.com\u002Fapi\u002Fworkspace\u002Fintroduction).\n\n2. **Authenticated**: `databricks auth profiles`\n   - If not: see [CLI Authentication](databricks-cli-auth.md)\n\n## Profile Selection - CRITICAL\n\n**NEVER auto-select a profile.**\n\n1. List profiles: `databricks auth profiles`\n2. Present ALL profiles to user with workspace URLs\n3. Let user choose (even if only one exists)\n4. Offer to create new profile if needed\n\n## Claude Code - IMPORTANT\n\nEach Bash command runs in a **separate shell session**.\n\n```bash\n# WORKS: --profile flag\ndatabricks apps list --profile my-workspace\n\n# WORKS: chained with &&\nexport DATABRICKS_CONFIG_PROFILE=my-workspace && databricks apps list\n\n# DOES NOT WORK: separate commands\nexport DATABRICKS_CONFIG_PROFILE=my-workspace\ndatabricks apps list  # profile not set!\n```\n\n## Data Exploration — Use AI Tools\n\n**Use these instead of manually navigating catalogs\u002Fschemas\u002Ftables:**\n\n```bash\n# discover table structure (columns, types, sample data, stats)\ndatabricks experimental aitools tools discover-schema catalog.schema.table --profile \u003CPROFILE>\n\n# run ad-hoc SQL queries\ndatabricks experimental aitools tools query \"SELECT * FROM table LIMIT 10\" --profile \u003CPROFILE>\n\n# find the default warehouse\ndatabricks experimental aitools tools get-default-warehouse --profile \u003CPROFILE>\n```\n\n**Names are literal.** Use catalog\u002Fschema\u002Ftable names exactly as given — never change a\nhyphen to an underscore or otherwise normalize them. In SQL, backtick-quote any name part\nwith special characters (e.g. `` `my-catalog`.schema.table ``); unquoted hyphens cause a\nparse error.\n\nThese commands are first-class for running known SQL and profiling — Genie isn't\nrequired for that. For natural-language data questions, locating data you can't\npin down, or generating a query from a question, prefer the `databricks-data-discovery`\nskill (above) if it's installed. See [Manual Data Exploration](manual-data-exploration.md) for the\nfull command surface, quoting rules, and troubleshooting.\n\n## Quick Reference\n\n**⚠️ CRITICAL: Some commands use positional arguments, not flags**\n\n```bash\n# current user\ndatabricks current-user me --profile \u003CPROFILE>\n\n# list resources\ndatabricks apps list --profile \u003CPROFILE>\ndatabricks jobs list --profile \u003CPROFILE>\ndatabricks clusters list --profile \u003CPROFILE>\ndatabricks warehouses list --profile \u003CPROFILE>\ndatabricks pipelines list --profile \u003CPROFILE>\ndatabricks serving-endpoints list --profile \u003CPROFILE>\n\n# ⚠️ Unity Catalog — POSITIONAL arguments (NOT flags!)\ndatabricks catalogs list --profile \u003CPROFILE>\n\n# ✅ CORRECT: positional args\ndatabricks schemas list \u003CCATALOG> --profile \u003CPROFILE>\ndatabricks tables list \u003CCATALOG> \u003CSCHEMA> --profile \u003CPROFILE>\ndatabricks tables get \u003CCATALOG>.\u003CSCHEMA>.\u003CTABLE> --profile \u003CPROFILE>\n\n# ❌ WRONG: these flags\u002Fcommands DON'T EXIST\n# databricks schemas list --catalog-name \u003CCATALOG>    ← WILL FAIL\n# databricks tables list --catalog \u003CCATALOG>           ← WILL FAIL\n# databricks sql-warehouses list                       ← doesn't exist, use `warehouses list`\n# databricks execute-statement                         ← doesn't exist, use `experimental aitools tools query`\n# databricks sql execute                               ← doesn't exist, use `experimental aitools tools query`\n\n# When in doubt, check help:\n# databricks schemas list --help\n\n# get details\ndatabricks apps get \u003CNAME> --profile \u003CPROFILE>\ndatabricks jobs get --job-id \u003CID> --profile \u003CPROFILE>\ndatabricks clusters get --cluster-id \u003CID> --profile \u003CPROFILE>\n\n# bundles\ndatabricks bundle init --profile \u003CPROFILE>\ndatabricks bundle validate --profile \u003CPROFILE>\ndatabricks bundle deploy -t \u003CTARGET> --profile \u003CPROFILE>\ndatabricks bundle run \u003CRESOURCE> -t \u003CTARGET> --profile \u003CPROFILE>\n```\n\n## Troubleshooting\n\n| Error | Solution |\n|-------|----------|\n| `cannot configure default credentials` | Use `--profile` flag or authenticate first |\n| `configuration does not support OAuth tokens` | The command requires OAuth (e.g., `databricks apps logs`). Re-authenticate with `databricks auth login --host \u003CURL> --profile \u003CPROFILE>`. 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profile",{"type":39,"tag":52,"props":1985,"children":1987},{"id":1986},"required-reading-by-task",[1988],{"type":44,"value":1989},"Required Reading by Task",{"type":39,"tag":1860,"props":1991,"children":1992},{},[1993,2009],{"type":39,"tag":1864,"props":1994,"children":1995},{},[1996],{"type":39,"tag":1868,"props":1997,"children":1998},{},[1999,2004],{"type":39,"tag":1872,"props":2000,"children":2001},{},[2002],{"type":44,"value":2003},"Task",{"type":39,"tag":1872,"props":2005,"children":2006},{},[2007],{"type":44,"value":2008},"READ BEFORE proceeding",{"type":39,"tag":1883,"props":2010,"children":2011},{},[2012,2027,2042,2066],{"type":39,"tag":1868,"props":2013,"children":2014},{},[2015,2020],{"type":39,"tag":1890,"props":2016,"children":2017},{},[2018],{"type":44,"value":2019},"First time setup",{"type":39,"tag":1890,"props":2021,"children":2022},{},[2023],{"type":39,"tag":139,"props":2024,"children":2025},{"href":197},[2026],{"type":44,"value":200},{"type":39,"tag":1868,"props":2028,"children":2029},{},[2030,2035],{"type":39,"tag":1890,"props":2031,"children":2032},{},[2033],{"type":44,"value":2034},"Auth issues \u002F new workspace",{"type":39,"tag":1890,"props":2036,"children":2037},{},[2038],{"type":39,"tag":139,"props":2039,"children":2040},{"href":269},[2041],{"type":44,"value":272},{"type":39,"tag":1868,"props":2043,"children":2044},{},[2045,2050],{"type":39,"tag":1890,"props":2046,"children":2047},{},[2048],{"type":44,"value":2049},"Exploring tables\u002Fschemas",{"type":39,"tag":1890,"props":2051,"children":2052},{},[2053,2057,2059,2064],{"type":39,"tag":139,"props":2054,"children":2055},{"href":141},[2056],{"type":44,"value":144},{"type":44,"value":2058}," (or ",{"type":39,"tag":168,"props":2060,"children":2062},{"className":2061},[],[2063],{"type":44,"value":135},{"type":44,"value":2065}," if installed)",{"type":39,"tag":1868,"props":2067,"children":2068},{},[2069,2074],{"type":39,"tag":1890,"props":2070,"children":2071},{},[2072],{"type":44,"value":2073},"Deploying jobs\u002Fpipelines",{"type":39,"tag":1890,"props":2075,"children":2076},{},[2077,2078],{"type":44,"value":1903},{"type":39,"tag":168,"props":2079,"children":2081},{"className":2080},[],[2082],{"type":44,"value":2083},"\u002Fdatabricks-dabs",{"type":39,"tag":52,"props":2085,"children":2087},{"id":2086},"reference-guides",[2088],{"type":44,"value":2089},"Reference Guides",{"type":39,"tag":64,"props":2091,"children":2092},{},[2093,2100,2107],{"type":39,"tag":68,"props":2094,"children":2095},{},[2096],{"type":39,"tag":139,"props":2097,"children":2098},{"href":197},[2099],{"type":44,"value":200},{"type":39,"tag":68,"props":2101,"children":2102},{},[2103],{"type":39,"tag":139,"props":2104,"children":2105},{"href":269},[2106],{"type":44,"value":272},{"type":39,"tag":68,"props":2108,"children":2109},{},[2110],{"type":39,"tag":139,"props":2111,"children":2112},{"href":141},[2113],{"type":44,"value":144},{"type":39,"tag":2115,"props":2116,"children":2117},"style",{},[2118],{"type":44,"value":2119},"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":2121,"total":1460},[2122,2139,2153,2168,2185,2205,2215],{"slug":2123,"name":2123,"fn":2124,"description":2125,"org":2126,"tags":2127,"stars":20,"repoUrl":21,"updatedAt":2138},"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},[2128,2131,2132,2135],{"name":2129,"slug":2130,"type":15},"Agents","agents",{"name":9,"slug":8,"type":15},{"name":2133,"slug":2134,"type":15},"Knowledge Management","knowledge-management",{"name":2136,"slug":2137,"type":15},"Multi-Agent","multi-agent","2026-07-15T05:41:38.548954",{"slug":2140,"name":2140,"fn":2141,"description":2142,"org":2143,"tags":2144,"stars":20,"repoUrl":21,"updatedAt":2152},"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},[2145,2148,2149],{"name":2146,"slug":2147,"type":15},"Data Analysis","data-analysis",{"name":9,"slug":8,"type":15},{"name":2150,"slug":2151,"type":15},"LLM","llm","2026-07-31T05:53:33.562077",{"slug":2154,"name":2154,"fn":2155,"description":2156,"org":2157,"tags":2158,"stars":20,"repoUrl":21,"updatedAt":2167},"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},[2159,2160,2161,2164],{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},{"name":2162,"slug":2163,"type":15},"Docker","docker",{"name":2165,"slug":2166,"type":15},"Engineering","engineering","2026-07-12T08:04:55.843982",{"slug":2169,"name":2169,"fn":2170,"description":2171,"org":2172,"tags":2173,"stars":20,"repoUrl":21,"updatedAt":2184},"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},[2174,2177,2180,2183],{"name":2175,"slug":2176,"type":15},"Analytics","analytics",{"name":2178,"slug":2179,"type":15},"Dashboards","dashboards",{"name":2181,"slug":2182,"type":15},"Data Visualization","data-visualization",{"name":9,"slug":8,"type":15},"2026-07-12T08:04:25.314591",{"slug":2186,"name":2186,"fn":2187,"description":2188,"org":2189,"tags":2190,"stars":20,"repoUrl":21,"updatedAt":2204},"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},[2191,2192,2195,2198,2201],{"name":9,"slug":8,"type":15},{"name":2193,"slug":2194,"type":15},"Design","design",{"name":2196,"slug":2197,"type":15},"Frontend","frontend",{"name":2199,"slug":2200,"type":15},"React","react",{"name":2202,"slug":2203,"type":15},"UI Components","ui-components","2026-07-12T08:04:02.02398",{"slug":96,"name":96,"fn":2206,"description":2207,"org":2208,"tags":2209,"stars":20,"repoUrl":21,"updatedAt":2214},"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},[2210,2211,2212,2213],{"name":2175,"slug":2176,"type":15},{"name":2178,"slug":2179,"type":15},{"name":2146,"slug":2147,"type":15},{"name":9,"slug":8,"type":15},"2026-07-12T08:03:59.061458",{"slug":2216,"name":2216,"fn":2217,"description":2218,"org":2219,"tags":2220,"stars":20,"repoUrl":21,"updatedAt":2237},"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},[2221,2222,2225,2228,2231,2234],{"name":9,"slug":8,"type":15},{"name":2223,"slug":2224,"type":15},"FastAPI","fastapi",{"name":2226,"slug":2227,"type":15},"Flask","flask",{"name":2229,"slug":2230,"type":15},"Gradio","gradio",{"name":2232,"slug":2233,"type":15},"Python","python",{"name":2235,"slug":2236,"type":15},"Streamlit","streamlit","2026-07-12T08:04:10.970845",{"items":2239,"total":1460},[2240,2247,2253,2260,2267,2275,2282,2291,2297,2314,2328,2341],{"slug":2123,"name":2123,"fn":2124,"description":2125,"org":2241,"tags":2242,"stars":20,"repoUrl":21,"updatedAt":2138},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2243,2244,2245,2246],{"name":2129,"slug":2130,"type":15},{"name":9,"slug":8,"type":15},{"name":2133,"slug":2134,"type":15},{"name":2136,"slug":2137,"type":15},{"slug":2140,"name":2140,"fn":2141,"description":2142,"org":2248,"tags":2249,"stars":20,"repoUrl":21,"updatedAt":2152},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2250,2251,2252],{"name":2146,"slug":2147,"type":15},{"name":9,"slug":8,"type":15},{"name":2150,"slug":2151,"type":15},{"slug":2154,"name":2154,"fn":2155,"description":2156,"org":2254,"tags":2255,"stars":20,"repoUrl":21,"updatedAt":2167},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2256,2257,2258,2259],{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},{"name":2162,"slug":2163,"type":15},{"name":2165,"slug":2166,"type":15},{"slug":2169,"name":2169,"fn":2170,"description":2171,"org":2261,"tags":2262,"stars":20,"repoUrl":21,"updatedAt":2184},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2263,2264,2265,2266],{"name":2175,"slug":2176,"type":15},{"name":2178,"slug":2179,"type":15},{"name":2181,"slug":2182,"type":15},{"name":9,"slug":8,"type":15},{"slug":2186,"name":2186,"fn":2187,"description":2188,"org":2268,"tags":2269,"stars":20,"repoUrl":21,"updatedAt":2204},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2270,2271,2272,2273,2274],{"name":9,"slug":8,"type":15},{"name":2193,"slug":2194,"type":15},{"name":2196,"slug":2197,"type":15},{"name":2199,"slug":2200,"type":15},{"name":2202,"slug":2203,"type":15},{"slug":96,"name":96,"fn":2206,"description":2207,"org":2276,"tags":2277,"stars":20,"repoUrl":21,"updatedAt":2214},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2278,2279,2280,2281],{"name":2175,"slug":2176,"type":15},{"name":2178,"slug":2179,"type":15},{"name":2146,"slug":2147,"type":15},{"name":9,"slug":8,"type":15},{"slug":2216,"name":2216,"fn":2217,"description":2218,"org":2283,"tags":2284,"stars":20,"repoUrl":21,"updatedAt":2237},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2285,2286,2287,2288,2289,2290],{"name":9,"slug":8,"type":15},{"name":2223,"slug":2224,"type":15},{"name":2226,"slug":2227,"type":15},{"name":2229,"slug":2230,"type":15},{"name":2232,"slug":2233,"type":15},{"name":2235,"slug":2236,"type":15},{"slug":4,"name":4,"fn":5,"description":6,"org":2292,"tags":2293,"stars":20,"repoUrl":21,"updatedAt":22},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2294,2295,2296],{"name":18,"slug":19,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},{"slug":2298,"name":2298,"fn":2299,"description":2300,"org":2301,"tags":2302,"stars":20,"repoUrl":21,"updatedAt":2313},"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},[2303,2306,2309,2310],{"name":2304,"slug":2305,"type":15},"Automation","automation",{"name":2307,"slug":2308,"type":15},"Configuration","configuration",{"name":9,"slug":8,"type":15},{"name":2311,"slug":2312,"type":15},"Deployment","deployment","2026-07-15T05:41:35.930355",{"slug":135,"name":135,"fn":2315,"description":2316,"org":2317,"tags":2318,"stars":20,"repoUrl":21,"updatedAt":2327},"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},[2319,2320,2323,2324],{"name":2146,"slug":2147,"type":15},{"name":2321,"slug":2322,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":2325,"slug":2326,"type":15},"SQL","sql","2026-07-31T05:53:32.561877",{"slug":2329,"name":2329,"fn":2330,"description":2331,"org":2332,"tags":2333,"stars":20,"repoUrl":21,"updatedAt":2340},"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},[2334,2335,2338,2339],{"name":2146,"slug":2147,"type":15},{"name":2336,"slug":2337,"type":15},"Database","database",{"name":9,"slug":8,"type":15},{"name":2325,"slug":2326,"type":15},"2026-07-12T08:04:08.678282",{"slug":2342,"name":2342,"fn":2343,"description":2344,"org":2345,"tags":2346,"stars":20,"repoUrl":21,"updatedAt":2354},"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},[2347,2348,2351],{"name":9,"slug":8,"type":15},{"name":2349,"slug":2350,"type":15},"Documentation","documentation",{"name":2352,"slug":2353,"type":15},"Reference","reference","2026-07-15T05:41:34.697746"]