[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-databricks-databricks-aibi-dashboards":3,"mdc--4tkouc-key":33,"related-org-databricks-databricks-aibi-dashboards":7623,"related-repo-databricks-databricks-aibi-dashboards":7800},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":31,"mdContent":32},"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},"databricks","Databricks","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fdatabricks.png",[12,16,19,20],{"name":13,"slug":14,"type":15},"Dashboards","dashboards","tag",{"name":17,"slug":18,"type":15},"Data Visualization","data-visualization",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Analytics","analytics",204,"https:\u002F\u002Fgithub.com\u002Fdatabricks\u002Fdatabricks-agent-skills","2026-07-12T08:04:25.314591",null,60,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":26},[],"https:\u002F\u002Fgithub.com\u002Fdatabricks\u002Fdatabricks-agent-skills\u002Ftree\u002FHEAD\u002Fplugins\u002Fdatabricks\u002Fcopilot\u002Fskills\u002Fdatabricks-aibi-dashboards","---\nname: databricks-aibi-dashboards\ndescription: \"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.\"\ncompatibility: Requires databricks CLI (>= v1.0.0)\nmetadata:\n  version: \"0.2.1\"\nparent: databricks-core\n---\n\n# AI\u002FBI Dashboard Skill\n\nCreate Databricks AI\u002FBI dashboards (formerly Lakeview dashboards).\nA dashboard should be showing something relevant for a human, typically some KPI on the top, and based on the story, some graph (often temporal), and we see \"something happens\".\n**Follow these guidelines strictly.**\n\n> **When a custom app fits better:** A managed AI\u002FBI dashboard is the right tool for read-only KPIs, charts, and filters over governed tables. If the user instead needs a *custom-code interactive app* — write-back \u002F data entry, bespoke UI or interactions beyond the dashboard grid, embedded or auth-gated workflows, or a conversational Genie\u002Fchat assistant as the primary surface — build a Databricks App instead with the `databricks-apps` skill (which brings in `databricks-app-design` for the data-screen UX). Linking an \"Ask Genie\" space to *this* dashboard stays here (see Linking a Genie Space below).\n\n## Quick Reference\n\n| Task | Command |\n|------|---------|\n| List warehouses | `databricks warehouses list` |\n| List tables | `databricks experimental aitools tools query --warehouse WH \"SHOW TABLES IN catalog.schema\"` |\n| Get schema | `databricks experimental aitools tools discover-schema catalog.schema.table1 catalog.schema.table2` |\n| Test query | `databricks experimental aitools tools query --warehouse WH \"SELECT...\"` |\n| Create dashboard | `databricks lakeview create --display-name \"X\" --warehouse-id \"WH\" --dataset-catalog CATALOG --dataset-schema SCHEMA --serialized-dashboard \"$(cat file.json)\" --json '{\"parent_path\": \"\u002FWorkspace\u002FUsers\u002F\u003Cyou>\u002Fpath\"}'` — `--dataset-catalog` \u002F `--dataset-schema` are **flag-only** (REQUIRED; CLI silently drops them if put in `--json`); `parent_path` is JSON-only (no flag). Queries must use bare table names. |\n| Update dashboard | `databricks lakeview update DASHBOARD_ID --serialized-dashboard \"$(cat file.json)\"` |\n| Publish | `databricks lakeview publish DASHBOARD_ID --warehouse-id WH` |\n| Delete | `databricks lakeview trash DASHBOARD_ID` |\n\n> **`--warehouse` flag**: if `databricks experimental aitools tools query --warehouse WH \"...\"` fails with `unknown flag: --warehouse` on your CLI version, set `DATABRICKS_WAREHOUSE_ID=WH` in the environment instead and drop the flag — the command auto-picks it from there.\n\n---\n\n## Widget Index (Version + Where Documented)\n\n> **Wrong version = broken widget!** This is the #1 cause of dashboard errors.\n\n| Widget Type | Version | Documented in |\n|-------------|---------|---------------|\n| text (markdown, no spec block) | N\u002FA | [1-widget-specifications.md#text-headersdescriptions](references\u002F1-widget-specifications.md#text-headersdescriptions) |\n| `counter` (KPI + sparkline + comparison) | **2** | [1-widget-specifications.md#counter-kpi](references\u002F1-widget-specifications.md#counter-kpi) |\n| `table` | **2** | [1-widget-specifications.md#table](references\u002F1-widget-specifications.md#table) |\n| `bar`, `line` | **3** | [1-widget-specifications.md#line--bar-charts](references\u002F1-widget-specifications.md#line--bar-charts) |\n| `pie` | **3** | [1-widget-specifications.md#pie-chart](references\u002F1-widget-specifications.md#pie-chart) |\n| `symbol-map` (lat\u002Flon point map) | **2** | [1-widget-specifications.md#symbol-map-bubble-map](references\u002F1-widget-specifications.md#symbol-map-bubble-map) |\n| `area` | **3** | [2-advanced-widget-specifications.md#area-chart](references\u002F2-advanced-widget-specifications.md#area-chart) |\n| `scatter` | **3** | [2-advanced-widget-specifications.md#scatter-plot--bubble-chart](references\u002F2-advanced-widget-specifications.md#scatter-plot--bubble-chart) |\n| `combo` (bar+line, dual-axis) | **1** | [2-advanced-widget-specifications.md#combo-chart-bar--line](references\u002F2-advanced-widget-specifications.md#combo-chart-bar--line) |\n| `choropleth-map` (regions colored by value) | **1** | [2-advanced-widget-specifications.md#choropleth-map](references\u002F2-advanced-widget-specifications.md#choropleth-map) |\n| `forecast-line` (with `AI_FORECAST` SQL) | **1** | [2-advanced-widget-specifications.md#forecast-line-with-ai_forecast](references\u002F2-advanced-widget-specifications.md#forecast-line-with-ai_forecast) |\n| `pivot` (with conditional cell rules) | **3** | [2-advanced-widget-specifications.md#pivot](references\u002F2-advanced-widget-specifications.md#pivot) |\n| `histogram` (with `bin(col, binWidth=N)`) | **3** | [2-advanced-widget-specifications.md#histogram](references\u002F2-advanced-widget-specifications.md#histogram) |\n| `sankey` | **1** | [2-advanced-widget-specifications.md#sankey](references\u002F2-advanced-widget-specifications.md#sankey) |\n| `heatmap` | **3** | [2-advanced-widget-specifications.md#heatmap](references\u002F2-advanced-widget-specifications.md#heatmap) |\n| `funnel` | **1** | [2-advanced-widget-specifications.md#funnel](references\u002F2-advanced-widget-specifications.md#funnel) |\n| `box` | **1** | [2-advanced-widget-specifications.md#box](references\u002F2-advanced-widget-specifications.md#box) |\n| `waterfall` | **1** | [2-advanced-widget-specifications.md#waterfall](references\u002F2-advanced-widget-specifications.md#waterfall) |\n| `filter-single-select`, `filter-multi-select`, `filter-date-range-picker` | **2** | [3-filters.md#filter-widget-structure](references\u002F3-filters.md#filter-widget-structure) |\n| `range-slider` | **2** | [3-filters.md#range-slider-numeric-range-filter](references\u002F3-filters.md#range-slider-numeric-range-filter) |\n\n> Cohort retention charts are built as a `pivot` with a color-scale cell style — there is no `cohort` widget type. See pivot in [2-advanced-widget-specifications.md](references\u002F2-advanced-widget-specifications.md).\n\n---\n\n## NEW DASHBOARD CREATION WORKFLOW\n\n**You MUST test ALL SQL queries via CLI BEFORE deploying. Follow the overall logic in these steps for new dashboard - Skipping validation causes broken dashboards.**\n\n### Step 1: Get Warehouse ID if not already known\n\n```bash\n# List warehouses to find one for SQL execution\ndatabricks warehouses list\n```\n\n### Step 2: Discover Table Schemas and existing data pattern\n\nA good dashboard comes from knowing the data first. Spend time here — the exploration drives design decisions in Step 4 (which widgets, which filters, which groupings).\n\nUse `discover-schema` as the default — one call returns columns, types, sample rows, null counts, and row count. If you only know the schema, list tables first with `query \"SHOW TABLES IN ...\"`.\n\n`databricks experimental aitools tools discover-schema catalog.schema.orders catalog.schema.customers`\n\nSample rows alone don't tell you what to build. you can write aggregate SQL through `databricks experimental aitools tools query --warehouse \u003CWH> \"...\"` to probe typically:\n\n- **Cardinality** of candidate grouping columns → decides chart color-group vs. table (≤8 distinct values for charts, see Cardinality & Readability below).\n- **Top categorical values** → populates filter options and chart legends meaningfully.\n- **Numeric distribution** (min\u002Fmax\u002Favg\u002Fpercentiles) → decides KPI with delta vs. trend chart (flat metrics shouldn't be line charts, see Data Variance Considerations below).\n- **Trend viability** at daily\u002Fweekly\u002Fmonthly grain → picks the right trend granularity.\n- **Story confirmation** — run the aggregations you plan to put in the dashboard and check they're not flat, empty, or uninteresting. Fix the query or adjust the story before moving on.\n\nFan out independent probes in one call — pass several positional SQLs (and\u002For repeated `--file`) and they run in parallel (default `--concurrency 8`):\n\n```bash\nDATABRICKS_WAREHOUSE_ID=\u003CWH> databricks experimental aitools tools query --output json \\\n  \"SELECT COUNT(*) FROM catalog.schema.orders\" \\\n  \"SELECT region, COUNT(*) FROM catalog.schema.orders GROUP BY region ORDER BY 2 DESC LIMIT 10\" \\\n  \"SELECT MIN(ts), MAX(ts) FROM catalog.schema.orders\"\n```\n\n- **`--output json` is mandatory** in multi-query mode. Returns one object per statement: `{sql, state, rows, error}`; failures are per-statement (`state: \"FAILED\"`), others still succeed.\n- ⚠️ **Don't trust the exit code** (a failed statement can still exit `0`) — gate on each object's `state != \"SUCCEEDED\"`.\n\n> **Dashboard queries are different** — inside the dashboard JSON, the `FROM` clause must reference ONLY the table name, with no catalog or schema prefix:\n> - ✅ Correct: `FROM trips`\n> - ❌ Wrong: `FROM nyctaxi.trips`\n> - ❌ Wrong: `FROM samples.nyctaxi.trips`\n>\n> The catalog and schema are supplied separately via the `--dataset-catalog` and `--dataset-schema` flags when you run `databricks lakeview create`. These flags do NOT rewrite the query — they only fill in the catalog\u002Fschema when the query omits them. If you hardcode a catalog or schema in the `FROM` clause, the flags are ignored for that query and the dashboard won't be portable across environments.\n\n\n### Step 3: Verify Data Matches Story\nThe datasets.querylines in the dashboard json (see example below) must be tested to ensure \n\nBefore finalizing, run the SQL Queries you intend to add in each dataset to confirm that they run properly and that the result are valid.\nThis is crucial, as the widget defined in the json will use the query field output to render the visualization. The value should also make sense at a business level.\nRemember that for the filter to work, the query should have the field available (so typically group by the filter field)\n\nIf values don't match expectations, ensure the query is correct, fix the data if you can, or adjust the story before creating the dashboard.\n\n### Step 4: Plan Dashboard Structure\n\nBefore writing JSON, plan your dashboard:\n\n1. You must know the expected specific JSON structure. For this, **Read reference files**: [1-widget-specifications.md](references\u002F1-widget-specifications.md), [3-filters.md](references\u002F3-filters.md).\n\nAlways make sure you read an entire example to understand the structure, like [4-examples.md](references\u002F4-examples.md).\n\n2. Think: **What widgets?** Map each visualization to a dataset:\n   | Widget | Type | Dataset | Has filter field? |\n   |--------|------|---------|-------------------|\n   | Revenue KPI | counter | ds_sales | ✓ date, region |\n   | Trend Chart | line | ds_sales | ✓ date, region |\n   | Top Products | table | ds_products | ✗ no date | \n   ...\n\n3. **What filters?** For each filter, verify ALL datasets you want filtered contain the filter field.\n   > **Filters only affect datasets that have the filter field.** A pre-aggregated table without dates WON'T be date-filtered.\n\n4. **Build the dashboard JSON** as a local working file (intermediate step, not the deliverable).\n\n### Step 5: Deploy\n\n**Now deploy the JSON to the workspace.** Run `databricks lakeview create` (below). Your task is not complete until this command succeeds and returns a dashboard ID — the JSON file alone is an intermediate working artifact.\n\nAfter deploying, the same `lakeview` subcommands manage the dashboard's lifecycle (list, get, update, publish, trash).\n\n```bash\n# Deploy: creates the dashboard in the workspace and returns a dashboard ID.\n# Canonical form — MIX flags + --json. Each field has exactly ONE valid place:\n#   --dataset-catalog \u002F --dataset-schema : FLAG-ONLY (REQUIRED — no JSON field).\n#       The CLI silently warns \"unknown field\" and drops them if put in --json,\n#       leaving every dataset query unable to resolve its catalog.schema.\n#   parent_path : JSON-ONLY (no flag). Without it, dashboard lands at\n#       \u002FUsers\u002F\u003Cyou>\u002F\u003Cdisplay-name>.\n#   display_name \u002F warehouse_id \u002F serialized_dashboard : either form works;\n#       prefer flags for readability.\n# Queries inside dashboard.json MUST use bare table names (\"FROM trips\", never\n# \"FROM schema.trips\" or \"FROM catalog.schema.trips\") — --dataset-catalog and\n# --dataset-schema only fill in missing parts, they do NOT rewrite hardcoded\n# prefixes.\ndatabricks lakeview create \\\n  --display-name \"My Dashboard\" \\\n  --warehouse-id \"abc123def456\" \\\n  --dataset-catalog \"my_catalog\" \\\n  --dataset-schema \"my_schema\" \\\n  --serialized-dashboard \"$(cat dashboard.json)\" \\\n  --json '{\"parent_path\": \"\u002FWorkspace\u002FUsers\u002Fme@co.com\u002Fdashboards\"}'\n\n# List all dashboards\ndatabricks lakeview list\n\n# Get dashboard details\ndatabricks lakeview get DASHBOARD_ID\n\n# Update a dashboard\ndatabricks lakeview update DASHBOARD_ID --serialized-dashboard \"$(cat dashboard.json)\"\n\n# Publish a dashboard\ndatabricks lakeview publish DASHBOARD_ID --warehouse-id WAREHOUSE_ID\n\n# Unpublish a dashboard\ndatabricks lakeview unpublish DASHBOARD_ID\n\n# Delete (trash) a dashboard\ndatabricks lakeview trash DASHBOARD_ID\n\n# By default, after creation, tag dashboards to track resources created with this skill\ndatabricks workspace-entity-tag-assignments create-tag-assignment \\\n  dashboards DASHBOARD_ID aidevkit_project --tag-value ai-dev-kit\n```\n\n---\n\n## UPDATING AN EXISTING DASHBOARD\n\nTo change a dashboard that already exists, build the updated JSON with the [creation workflow](#new-dashboard-creation-workflow) above, then deploy it with `update` + `publish` on the **same** `DASHBOARD_ID` — never re-run `create`\u002Fimport.\n\n- **Don't `create`\u002Fimport to update.** That mints a new dashboard id + URL and breaks any link you've already shared; `create` is for brand-new dashboards only.\n- **`update` changes only the draft.** The `\u002Fpublished` link viewers see stays on the last snapshot until you `publish` again — so always `publish` after `update`.\n\n---\n\n## JSON Structure (Required Skeleton)\n\nEvery dashboard's `serialized_dashboard` content must follow this exact structure:\n\nImportant: ALWAYS add a space or `\\n` at the end of each `queryLines` value as they are concatenated to create the dataset.\n\n```json\n{\n  \"datasets\": [\n    {\n      \"name\": \"ds_x\",\n      \"displayName\": \"Dataset X\",\n      \"queryLines\": [\"SELECT col1, col2 \", \"FROM my_table\"]\n    }\n  ],\n  \"pages\": [\n    {\n      \"name\": \"main\",\n      \"displayName\": \"Main\",\n      \"pageType\": \"PAGE_TYPE_CANVAS\",\n      \"layout\": [\n        {\"widget\": {\u002F* INLINE widget definition *\u002F}, \"position\": {\"x\":0,\"y\":0,\"width\":2,\"height\":3}}\n      ]\n    }\n  ]\n}\n```\n\n**Structural rules (violations cause \"failed to parse serialized dashboard\"):**\n- `queryLines`: Array of strings, NOT `\"query\": \"string\"`. Elements are **joined verbatim** with no separator — end each line with ` ` or `\\n` (or strip `-- comments`). A line ending in `-- comment` with no newline swallows the next line.\n- Widgets: INLINE in `layout[].widget`, NOT a separate `\"widgets\"` array\n- `pageType`: Required on every page (`PAGE_TYPE_CANVAS` or `PAGE_TYPE_GLOBAL_FILTERS`)\n- Query binding: `query.fields[].name` must exactly match `encodings.*.fieldName`\n\n### Theme & Color (always set this — it makes or breaks the dashboard)\n\nTop-level `uiSettings.theme` controls colors, fonts, and widget chrome across every widget on the dashboard. Without it, the dashboard inherits the workspace default and looks generic. **Set the full block on every dashboard you create** — a coherent palette is the single highest-impact polish item.\n\nMental model — **60\u002F30\u002F10 rule** mapped to theme keys: **60% neutral** = canvas\u002Fwidget\u002Fborder backgrounds (set `widgetBorderColor = widgetBackgroundColor` to hide borders); **30% secondary** = `fontColor` + `visualizationColors` (the content weight); **10% accent** = `selectionColor` for filters \u002F tabs \u002F active selections — pick something distinct from text and palette; a safe-blue around `#2272B4` matches the hyperlink convention and works as a default.\n\n```json\n{\n  \"datasets\": [...],\n  \"pages\": [...],\n  \"uiSettings\": {\n    \"theme\": {\n      \"canvasBackgroundColor\": {\"light\": \"#FCFCFC\", \"dark\": \"#1F272D\"},\n      \"widgetBackgroundColor\": {\"light\": \"#FFFFFF\", \"dark\": \"#11171C\"},\n      \"fontColor\":             {\"light\": \"#11171C\", \"dark\": \"#E8ECF0\"},\n      \"selectionColor\":        {\"light\": \"#2272B4\", \"dark\": \"#8ACAFF\"},\n      \"visualizationColors\": [\n        \"#FFA600\", \"#FF7054\", \"#DE5582\", \"#995495\",\n        \"#4E5185\", \"#1D425C\", \"#99DDB4\"\n      ],\n      \"widgetHeaderAlignment\": \"LEFT\"\n    }\n  }\n}\n```\n\n**Theme keys** (mechanics):\n\n- `visualizationColors`: ordered palette every chart series and category mapping cycles through. **Positions are 0-indexed**: `position: 0` = first color (`#FFA600` above), `position: 6` = seventh (`#99DDB4`). Length 5–8 is typical.\n- Background \u002F font \u002F selection colors take `light` + `dark` pairs; the dashboard auto-selects based on viewer mode.\n- `widgetHeaderAlignment`: `\"LEFT\"` (default), `\"CENTER\"`, or `\"RIGHT\"`. Optional top-level: `fontFamily` (e.g. `\"Space Grotesk\"`, `\"Inter\"` — sans-serif keeps dense data readable; don't override per widget) and `widgetCornerRadius` (integer px, e.g. `12` for rounded corners; `0` or omit = square).\n- Per-widget color references: `{\"themeColorType\": \"visualizationColors\", \"position\": N}` (0-indexed) to pin to a palette slot, or `{\"hex\": \"#FF0000\"}` for an exact color outside the palette.\n\n**Palette-design rules** (this is what separates a polished dashboard from a noisy one):\n\n1. **One coherent color family per dashboard, distinct across the suite.** Walk **across hues** (e.g., amber → coral → pink → purple → navy), not one color faded toward white — a single-hue lightness ramp reads as one color and the viewer can't tell categories apart. Adjacent stops must be visually distinct: if you squint and two blur into one, push them further apart. Single-hue ramps are for **quantitative** widgets only (`colorRamp.mode: \"custom-sequential\"`), never for `visualizationColors`.\n2. **Pin semantic colors as literal hex, outside the palette.** \"Bad\" = a warm coral (e.g. `#FF7E5C`), \"good\" = a calm teal\u002Fgreen. Use `color.scale.mappings` with a bare hex string — `{\"value\": \"Critical\", \"color\": \"#FF7E5C\"}` — **not** `{\"hex\": \"...\"}` or `themeColorType: position` (both are silently dropped on chart widgets). Reuse the good-teal that's already in the palette so it never clashes.\n3. **Color non-categorical widgets explicitly so they join the family.** Maps & heatmaps: `colorRamp.mode: \"custom-sequential\"` with `{start, end}` from the family (if directional: `start` = bad color, `end` = good color). Forecast \u002F multi-series: pin per-series via `color.scale.mappings` keyed on `displayName` (actual = solid family color, forecast = contrast\u002Falert, threshold = muted tone). Sparkline counters: set `value.color` to a family color, not grey.\n4. **\"Lighter \u002F more pastel\" tweak**: nudge all stops up in lightness *together*; don't recolor individual ones. Re-sync the pinned semantic hex values; keep enough contrast on the alert color that it still reads as a warning.\n\n**Starter palettes** (pick one and adapt — extend to 7-8 stops if needed; semantic red\u002Fgreen stay as literal hex per rule 2):\n\n```\n#094074  #3C6997  #5ADBFF  #FFDD4A  #FE9000\n#003F5C  #594E90  #BC4C96  #FF5F66  #FFA600\n#4A8CC7  #F59770  #FFD84A  #F0E09E  #6DD980\n#440154  #3B528B  #21918C  #5EC962  #FDE725\n#4E79A7  #F28E2C  #E15759  #76B7B2  #59A14F\n#0072B2  #E69F00  #009E73  #CC79A7  #D55E00\n#0D0887  #7E03A8  #CC4778  #F89441  #F0F921\n#6929C4  #1192E8  #005D5D  #9F1853  #FA4D56\n```\n\n~4-5% of viewers have color blindness (mostly red\u002Fgreen). Rows 4 and 6 above (viridis, Okabe-Ito) are CB-safe by design; verify customized palettes via simulator (Adobe Color, `colorbrewer2.org`). Don't put red and green adjacent, and rely on lightness contrast — not hue alone — between adjacent stops.\n\n### Linking a Genie Space (Optional)\n\nTo add an \"Ask Genie\" button to the dashboard, or to link a genie space\u002Froom with an ID, add `uiSettings.genieSpace` to the JSON (alongside `theme` if you have one):\n\n```json\n\"uiSettings\": {\n  \"theme\": { \u002F* ... *\u002F },\n  \"genieSpace\": {\n    \"isEnabled\": true,\n    \"overrideId\": \"your-genie-space-id-here\",\n    \"enablementMode\": \"ENABLED\"\n  }\n}\n```\n\n> **Genie is NOT a widget.** Link via `uiSettings.genieSpace` only. There is no `\"widgetType\": \"assistant\"`.\n\n---\n\n## Design Best Practices\n\nApply unless user specifies otherwise:\n- **Global date filter**: When data has temporal columns, add a date range filter. Most dashboards need time-based filtering.\n- **KPI time bounds**: Use time-bounded metrics that enable period comparison (MoM, YoY). Unbounded \"all-time\" totals are less actionable.\n- **Value formatting**: Format values based on their meaning — currency with symbol, percentages with %, large numbers compacted (K\u002FM\u002FB).\n- **Chart selection**: Match cardinality to chart type. Few distinct values → bar with color grouping (or pie if you really want a snapshot); many values → table.\n\n## Reference Files\n\n> **Before generating any dashboard JSON, read [4-examples.md](references\u002F4-examples.md) first.** It's a complete reference dashboard exercising every construct (dataset measures + `MEASURE()`, sparkline counters, forecast-line with annotations, pivot with conditional cells, symbol-map, histogram, range-slider filter, theme). Use it to learn the JSON shape; then adapt to the user's data and demo story — keep the structure, swap the tables, metrics, palette, and narrative for the case you're building.\n\n| What are you building? | Reference |\n|------------------------|-----------|\n| **Start here** — full working dashboard template | [4-examples.md](references\u002F4-examples.md) |\n| Any widget (text, counter, table, chart) | [1-widget-specifications.md](references\u002F1-widget-specifications.md) |\n| Advanced charts (area, scatter\u002FBubble, combo (Line+Bar), Choropleth map) | [2-advanced-widget-specifications.md](references\u002F2-advanced-widget-specifications.md) |\n| Dashboard with filters (global or page-level) | [3-filters.md](references\u002F3-filters.md) |\n| Debugging a broken dashboard | [5-troubleshooting.md](references\u002F5-troubleshooting.md) |\n\n---\n\n## Implementation Guidelines\n\n### 1) DATASET ARCHITECTURE\n\n- **Fewer datasets is better — aim for one dataset that backs as many widgets as possible.** Clicking a value on a chart (e.g., a bar, a slice) acts as a filter on **that dataset**, and every other widget sharing the same dataset re-renders with the click applied. Splitting widgets across many narrow datasets breaks this cross-filtering and forces users to set explicit filter widgets for what should \"just work\". Prefer one wide dataset per domain (orders, cases, customers); only split when a widget genuinely needs different grain, pre-aggregation, or a parameter the others can't tolerate.\n- **Two ways to define a dataset**:\n  - **SQL query**: `{\"name\": \"ds_x\", \"displayName\": \"...\", \"queryLines\": [\"SELECT ...\", \"FROM table\"]}` — full control, can include `WITH` \u002F `JOIN` \u002F `AI_FORECAST` \u002F etc.\n  - **UC asset shorthand**: `{\"name\": \"ds_x\", \"asset_name\": \"catalog.schema.table_or_view\"}` — no SQL needed. Works for tables, views, and metric views. You can still stack `columns` (measures + derived dimensions) on top: `{\"name\": \"ds_x\", \"asset_name\": \"...\", \"columns\": [{\"displayName\": \"Total Revenue\", \"expression\": \"SUM(\\`amount_usd\\`)\"}]}` — same `MEASURE()` pattern.\n- **Exactly ONE valid SQL query per dataset** when using `queryLines` (no multiple queries separated by `;`)\n- **Queries must use bare table names only** — no catalog, no schema prefix. Example: `FROM orders`, never `FROM gold.orders` or `FROM main.gold.orders`. The catalog and schema come from the `--dataset-catalog` and `--dataset-schema` flags at creation time. These flags only fill in missing parts — they do NOT override any catalog\u002Fschema written in the query.\n- SELECT must include all dimensions needed by widgets and all derived columns via `AS` aliases\n- Put ALL business logic (CASE\u002FWHEN, COALESCE, ratios) into the dataset SELECT with explicit aliases\n- **Contract rule**: Every widget `fieldName` must exactly match a dataset column or alias\n- **Add ORDER BY** when visualization depends on data order:\n  - Time series: `ORDER BY date` for chronological display\n  - Rankings\u002FTop-N: `ORDER BY metric DESC LIMIT 10` for \"Top 10\" charts\n  - Categorical charts: `ORDER BY metric DESC` to show largest values first\n\n#### Dataset-level measures + `MEASURE()`\n\nWidget expressions are usually inline aggregations (`{\"name\": \"sum(x)\", \"expression\": \"SUM(\\`x\\`)\"}`). But you can also **declare reusable measures on the dataset itself** and reference them by name — every widget that consumes the dataset can use the same metric without redefining it.\n\nTwo ways to define measures:\n\n1. **Dashboard-level `columns`** (works on any dataset — SQL query or `asset_name`):\n   ```json\n   {\n     \"name\": \"ds_support\",\n     \"queryLines\": [\"SELECT * FROM support_cases\"],\n     \"columns\": [\n       {\"displayName\": \"Total Cases\",     \"description\": \"Count of cases\",\n        \"expression\": \"COUNT(`case_id`)\"},\n       {\"displayName\": \"Reopen Rate %\",   \"description\": \"% of reopened cases\",\n        \"expression\": \"SUM(CASE WHEN `reopened_flag` THEN 1 ELSE 0 END) * 100.0 \u002F COUNT(`case_id`)\"},\n       {\"displayName\": \"Priority Level\",  \"description\": \"Sorted priority label\",\n        \"expression\": \"CASE WHEN `priority`='Critical' THEN '1-Critical' ELSE '4-Low' END\"}\n     ]\n   }\n   ```\n\n2. **Metric-view source** — if the dataset's `asset_name` (or `FROM` clause) is a UC metric view, its YAML-defined measures are already queryable. **Do not redeclare them** in `columns`. See [databricks-metric-views](..\u002Fdatabricks-metric-views\u002FSKILL.md).\n\nEither way, widgets reference the measure by name:\n\n```json\n\"fields\": [{\"name\": \"measure(Total Cases)\", \"expression\": \"MEASURE(`Total Cases`)\"}],\n\"encodings\": {\"value\": {\"fieldName\": \"measure(Total Cases)\", \"displayName\": \"Total Cases\"}}\n```\n\n`MEASURE(\\`...\\`)` works in counter, table, bar, line, pie, pivot — any widget that takes a field expression. Mix it with inline aggregations freely.\n\n### 2) WIDGET FIELD EXPRESSIONS\n\n> **CRITICAL: Field Name Matching Rule**\n> The `name` in `query.fields` MUST exactly match the `fieldName` in `encodings`.\n> If they don't match, the widget shows \"no selected fields to visualize\" error!\n\n**Correct pattern for aggregations:**\n```json\n\u002F\u002F In query.fields:\n{\"name\": \"sum(spend)\", \"expression\": \"SUM(`spend`)\"}\n\n\u002F\u002F In encodings (must match!):\n{\"fieldName\": \"sum(spend)\", \"displayName\": \"Total Spend\"}\n```\n\n**WRONG - names don't match:**\n```json\n\u002F\u002F In query.fields:\n{\"name\": \"spend\", \"expression\": \"SUM(`spend`)\"}  \u002F\u002F name is \"spend\"\n\n\u002F\u002F In encodings:\n{\"fieldName\": \"sum(spend)\", ...}  \u002F\u002F ERROR: \"sum(spend)\" ≠ \"spend\"\n```\n\nAllowed expressions in widget queries (you CANNOT use CAST or other SQL in expressions):\n\n```json\n{\"name\": \"(sum|avg|count|countdistinct|min|max)(col)\", \"expression\": \"(SUM|AVG|COUNT|COUNT(DISTINCT)|MIN|MAX)(`col`)\"}\n{\"name\": \"(daily|weekly|monthly)(date)\", \"expression\": \"DATE_TRUNC(\\\"(DAY|WEEK|MONTH)\\\", `date`)\"}\n{\"name\": \"field\", \"expression\": \"`field`\"}\n```\n\nIf you need conditional logic or multi-field formulas, compute a derived column in the dataset SQL first.\n\n### 3) SPARK SQL PATTERNS\n\n- Date math: `date_sub(current_date(), N)` for days, `add_months(current_date(), -N)` for months\n- Date truncation: `DATE_TRUNC('DAY'|'WEEK'|'MONTH'|'QUARTER'|'YEAR', column)`\n- **AVOID** `INTERVAL` syntax - use functions instead\n\n### 4) LAYOUT (12-Column Grid, NO GAPS)\n\n**Every page must include `\"layoutVersion\": \"GRID_V1\"`** alongside `pageType`.\n\n```json\n{\n  \"name\": \"overview\",\n  \"displayName\": \"Overview\",\n  \"pageType\": \"PAGE_TYPE_CANVAS\",\n  \"layoutVersion\": \"GRID_V1\",\n  \"layout\": [...]\n}\n```\n\nEach widget has a position: `{\"x\": 0, \"y\": 0, \"width\": 4, \"height\": 4}`\n\n**Pick the subdivision based on the audience.** The 12-column grid divides cleanly into 3, 4, or 6 columns: a 3-column layout (each widget `width: 4`) reduces cognitive load and fits an executive overview; a 4-column (`width: 3`) is the all-rounder; a 6-column (`width: 2`) packs the most density for technical \u002F operations dashboards where the reader is hunting through many metrics at once.\n\n**Default rule**: each row should fill width=12 exactly — no gaps. Once you're confident with the grid, you can stagger heights across columns (a tall widget on the left paired with several shorter ones on the right) so the two halves don't share row boundaries — see [4-examples.md](references\u002F4-examples.md#layout-12-col-grid) for the pattern. Start with strict rows; relax only when the stagger reads better visually.\n\n```\nCORRECT:                            WRONG:\ny=0: [w=12]                         y=0: [w=8]____  ← gap!\ny=1: [w=4][w=4][w=4]  ← fills 12    y=1: [w=2][w=2][w=2][w=2]__  ← gap!\ny=4: [w=6][w=6]       ← fills 12\n```\n\n**Recommended widget sizes:**\n\n| Widget Type | Width | Height | Notes |\n|-------------|-------|--------|-------|\n| Text header | 12 | 1 | Full width; use SEPARATE widgets for title and subtitle |\n| Counter\u002FKPI | 4 | **3-4** | **NEVER height=2** - too cramped! |\n| Line\u002FBar\u002FArea chart | 6 | **5-6** | Pair side-by-side to fill row |\n| Pie chart | 6 | **5-6** | Needs space for legend |\n| Full-width chart | 12 | 5-7 | For detailed time series |\n| Table | 12 | 5-8 | Full width for readability |\n\n**Standard dashboard structure:**\n```text\ny=0:  Title (w=12, h=1) - Dashboard title (use separate widget!)\ny=1:  Subtitle (w=12, h=1) - Description (use separate widget!)\ny=2:  KPIs (w=4 each, h=3) - 3 key metrics side-by-side\ny=5:  Section header (w=12, h=1) - \"Trends\" or similar\ny=6:  Charts (w=6 each, h=5) - Two charts side-by-side\ny=11: Section header (w=12, h=1) - \"Details\"\ny=12: Table (w=12, h=6) - Detailed data\n```\n\n### 5) CARDINALITY & READABILITY (CRITICAL)\n\n**Dashboard readability depends on limiting distinct values:**\n\n| Dimension Type | Max Values | Examples |\n|----------------|------------|----------|\n| Chart color\u002Fgroups | **3-8** | 4 regions, 5 product lines, 3 tiers |\n| Filters | 4-15 | 8 countries, 5 channels |\n| High cardinality | **Table only** | customer_id, order_id, SKU |\n\n**Before creating any chart with color\u002Fgrouping:**\n1. Check column cardinality via discover-schema or a COUNT DISTINCT query\n2. If >10 distinct values, aggregate to higher level OR use TOP-N + \"Other\" bucket\n3. For high-cardinality dimensions, use a table widget instead of a chart\n\n### 6) QUALITY CHECKLIST\n\nBefore deploying, verify:\n1. All widget names use only alphanumeric + hyphens + underscores\n2. **Every page has `\"layoutVersion\": \"GRID_V1\"`**\n3. All rows sum to width=12 with no gaps\n4. KPIs use height 3-4, charts use height 5-6\n5. Chart dimensions have reasonable cardinality (≤8 for colors\u002Fgroups)\n6. All widget fieldNames match dataset columns exactly\n7. **Field `name` in query.fields matches `fieldName` in encodings exactly** (e.g., both `\"sum(spend)\"`)\n8. Counter datasets: use `disaggregated: true` for 1-row datasets, `disaggregated: false` with aggregation for multi-row\n9. **Percent values must be 0-1 for `number-percent` format** (0.865 displays as \"86.5%\", don't forget to set the format). If data is 0-100, either divide by 100 in SQL or use `number` format instead.\n10. SQL uses Spark syntax (date_sub, not INTERVAL)\n11. **All SQL queries tested via CLI and return expected data**\n12. **Every dataset you want filtered MUST contain the filter field** — filters only affect datasets with that column in their query\n\n---\n\n## Data Variance Considerations\n\nBefore creating trend charts, check if the metric has enough variance to visualize meaningfully:\n\n```sql\nSELECT MIN(metric), MAX(metric), MAX(metric) - MIN(metric) as range FROM dataset\n```\n\nIf the range is very small relative to the scale (e.g., 83-89% on a 0-100 scale), the chart will appear nearly flat. Consider:\n- Showing as KPI with delta\u002Fcomparison instead of chart\n- Using a table to display exact values\n- Adjusting the visualization to focus on the variance\n\n---\n\n## Related Skills\n\n- **[databricks-apps](..\u002F..\u002Fskills\u002Fdatabricks-apps\u002FSKILL.md)** - when the user needs a custom-code interactive app (write-back, bespoke UI, in-app chat \u002F Genie) instead of a managed dashboard\n- **[databricks-unity-catalog](..\u002Fdatabricks-unity-catalog\u002FSKILL.md)** - for querying the underlying data and system tables\n- **[databricks-pipelines](..\u002Fdatabricks-pipelines\u002FSKILL.md)** - for building the data pipelines that feed dashboards\n- **[databricks-jobs](..\u002Fdatabricks-jobs\u002FSKILL.md)** - for scheduling dashboard data refreshes\n",{"data":34,"body":39},{"name":4,"description":6,"compatibility":35,"metadata":36,"parent":38},"Requires databricks CLI (>= v1.0.0)",{"version":37},"0.2.1","databricks-core",{"type":40,"children":41},"root",[42,51,63,109,116,321,364,368,374,387,1022,1053,1056,1062,1070,1077,1119,1125,1130,1150,1159,1172,1227,1248,1381,1443,1533,1539,1544,1549,1554,1560,1565,1595,1607,1768,1774,1791,1804,2434,2437,2443,2495,2565,2568,2574,2587,2608,3171,3179,3300,3306,3326,3397,4092,4102,4269,4279,4454,4464,4474,4487,4493,4513,4709,4736,4739,4745,4750,4793,4799,4826,4930,4933,4939,4945,5215,5227,5247,5252,5781,5786,5999,6010,6016,6056,6064,6239,6247,6407,6412,6657,6662,6668,6720,6726,6748,6944,6955,6989,7006,7015,7023,7200,7208,7217,7223,7231,7318,7326,7344,7350,7355,7497,7500,7506,7511,7527,7532,7550,7553,7559,7617],{"type":43,"tag":44,"props":45,"children":47},"element","h1",{"id":46},"aibi-dashboard-skill",[48],{"type":49,"value":50},"text","AI\u002FBI Dashboard Skill",{"type":43,"tag":52,"props":53,"children":54},"p",{},[55,57],{"type":49,"value":56},"Create Databricks AI\u002FBI dashboards (formerly Lakeview dashboards).\nA dashboard should be showing something relevant for a human, typically some KPI on the top, and based on the story, some graph (often temporal), and we see \"something happens\".\n",{"type":43,"tag":58,"props":59,"children":60},"strong",{},[61],{"type":49,"value":62},"Follow these guidelines strictly.",{"type":43,"tag":64,"props":65,"children":66},"blockquote",{},[67],{"type":43,"tag":52,"props":68,"children":69},{},[70,75,77,83,85,92,94,100,102,107],{"type":43,"tag":58,"props":71,"children":72},{},[73],{"type":49,"value":74},"When a custom app fits better:",{"type":49,"value":76}," A managed AI\u002FBI dashboard is the right tool for read-only KPIs, charts, and filters over governed tables. 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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},[7685,7686,7689,7692,7695],{"name":9,"slug":8,"type":15},{"name":7687,"slug":7688,"type":15},"Design","design",{"name":7690,"slug":7691,"type":15},"Frontend","frontend",{"name":7693,"slug":7694,"type":15},"React","react",{"name":7696,"slug":7697,"type":15},"UI Components","ui-components","2026-07-12T08:04:02.02398",{"slug":91,"name":91,"fn":7700,"description":7701,"org":7702,"tags":7703,"stars":23,"repoUrl":24,"updatedAt":7708},"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},[7704,7705,7706,7707],{"name":21,"slug":22,"type":15},{"name":13,"slug":14,"type":15},{"name":7649,"slug":7650,"type":15},{"name":9,"slug":8,"type":15},"2026-07-12T08:03:59.061458",{"slug":7710,"name":7710,"fn":7711,"description":7712,"org":7713,"tags":7714,"stars":23,"repoUrl":24,"updatedAt":7731},"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},[7715,7716,7719,7722,7725,7728],{"name":9,"slug":8,"type":15},{"name":7717,"slug":7718,"type":15},"FastAPI","fastapi",{"name":7720,"slug":7721,"type":15},"Flask","flask",{"name":7723,"slug":7724,"type":15},"Gradio","gradio",{"name":7726,"slug":7727,"type":15},"Python","python",{"name":7729,"slug":7730,"type":15},"Streamlit","streamlit","2026-07-12T08:04:10.970845",{"slug":38,"name":38,"fn":7733,"description":7734,"org":7735,"tags":7736,"stars":23,"repoUrl":24,"updatedAt":7742},"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},[7737,7740,7741],{"name":7738,"slug":7739,"type":15},"Authentication","authentication",{"name":7663,"slug":7664,"type":15},{"name":9,"slug":8,"type":15},"2026-07-18T05:11:05.45506",{"slug":7744,"name":7744,"fn":7745,"description":7746,"org":7747,"tags":7748,"stars":23,"repoUrl":24,"updatedAt":7759},"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},[7749,7752,7755,7756],{"name":7750,"slug":7751,"type":15},"Automation","automation",{"name":7753,"slug":7754,"type":15},"Configuration","configuration",{"name":9,"slug":8,"type":15},{"name":7757,"slug":7758,"type":15},"Deployment","deployment","2026-07-15T05:41:35.930355",{"slug":7761,"name":7761,"fn":7762,"description":7763,"org":7764,"tags":7765,"stars":23,"repoUrl":24,"updatedAt":7773},"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},[7766,7767,7770,7771],{"name":7649,"slug":7650,"type":15},{"name":7768,"slug":7769,"type":15},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":15},{"name":7772,"slug":7515,"type":15},"SQL","2026-07-31T05:53:32.561877",{"slug":7775,"name":7775,"fn":7776,"description":7777,"org":7778,"tags":7779,"stars":23,"repoUrl":24,"updatedAt":7786},"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},[7780,7781,7784,7785],{"name":7649,"slug":7650,"type":15},{"name":7782,"slug":7783,"type":15},"Database","database",{"name":9,"slug":8,"type":15},{"name":7772,"slug":7515,"type":15},"2026-07-12T08:04:08.678282",{"slug":7788,"name":7788,"fn":7789,"description":7790,"org":7791,"tags":7792,"stars":23,"repoUrl":24,"updatedAt":7799},"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},[7793,7794,7797],{"name":9,"slug":8,"type":15},{"name":7795,"slug":7796,"type":15},"Documentation","documentation",{"name":4844,"slug":7798,"type":15},"reference","2026-07-15T05:41:34.697746",{"items":7801,"total":2253},[7802,7809,7815,7822,7829,7837,7844],{"slug":7626,"name":7626,"fn":7627,"description":7628,"org":7803,"tags":7804,"stars":23,"repoUrl":24,"updatedAt":7641},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7805,7806,7807,7808],{"name":7632,"slug":7633,"type":15},{"name":9,"slug":8,"type":15},{"name":7636,"slug":7637,"type":15},{"name":7639,"slug":7640,"type":15},{"slug":7643,"name":7643,"fn":7644,"description":7645,"org":7810,"tags":7811,"stars":23,"repoUrl":24,"updatedAt":7655},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7812,7813,7814],{"name":7649,"slug":7650,"type":15},{"name":9,"slug":8,"type":15},{"name":7653,"slug":7654,"type":15},{"slug":7657,"name":7657,"fn":7658,"description":7659,"org":7816,"tags":7817,"stars":23,"repoUrl":24,"updatedAt":7672},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7818,7819,7820,7821],{"name":7663,"slug":7664,"type":15},{"name":9,"slug":8,"type":15},{"name":7667,"slug":7668,"type":15},{"name":7670,"slug":7671,"type":15},{"slug":4,"name":4,"fn":5,"description":6,"org":7823,"tags":7824,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7825,7826,7827,7828],{"name":21,"slug":22,"type":15},{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":9,"slug":8,"type":15},{"slug":99,"name":99,"fn":7681,"description":7682,"org":7830,"tags":7831,"stars":23,"repoUrl":24,"updatedAt":7698},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7832,7833,7834,7835,7836],{"name":9,"slug":8,"type":15},{"name":7687,"slug":7688,"type":15},{"name":7690,"slug":7691,"type":15},{"name":7693,"slug":7694,"type":15},{"name":7696,"slug":7697,"type":15},{"slug":91,"name":91,"fn":7700,"description":7701,"org":7838,"tags":7839,"stars":23,"repoUrl":24,"updatedAt":7708},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7840,7841,7842,7843],{"name":21,"slug":22,"type":15},{"name":13,"slug":14,"type":15},{"name":7649,"slug":7650,"type":15},{"name":9,"slug":8,"type":15},{"slug":7710,"name":7710,"fn":7711,"description":7712,"org":7845,"tags":7846,"stars":23,"repoUrl":24,"updatedAt":7731},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7847,7848,7849,7850,7851,7852],{"name":9,"slug":8,"type":15},{"name":7717,"slug":7718,"type":15},{"name":7720,"slug":7721,"type":15},{"name":7723,"slug":7724,"type":15},{"name":7726,"slug":7727,"type":15},{"name":7729,"slug":7730,"type":15}]