create-dashboard

name	create-dashboard
description	Create DAC dashboards by writing YAML or TSX dashboard definition files. Use when the user wants to create, modify, review, or understand DAC dashboards, widgets, filters, SQL queries, semantic models, or CLI validation workflows.
argument-hint	[dashboard request]
version	8

Create Dashboard

Use this skill to create or modify DAC dashboard projects.

DAC projects define dashboards as code and run queries through Bruin connections. Dashboards can use direct SQL or the semantic layer. Semantic widgets reference models, dimensions, metrics, and segments; DAC compiles them to SQL in the backend.

Project Layout

my-dac-project/
  .bruin.yml
  dashboards/
    sales.yml
    sales.dashboard.tsx
    queries/
      revenue.sql
  semantic/
    sales.yml
  themes/
    brand.yml

Use dashboards/ for dashboard files and semantic/ for semantic model YAML files. Regular SQL dashboards do not need semantic models.

Dashboard files:

• *.yml and *.yaml are YAML dashboards.
• *.dashboard.tsx files are TSX dashboards.
• Other TSX files can be helpers, but are not auto-discovered as dashboards.

Commands

dac init my-dashboards
dac validate --dir my-dashboards
dac check --dir my-dashboards
dac serve --dir my-dashboards --open
dac query --dir my-dashboards --dashboard "Sales" --widget "Revenue"

Use dac validate after editing structure and dac check when query execution should be verified.

Connection Config

DAC reads Bruin connections from .bruin.yml.

default_environment: default

environments:
  default:
    connections:
      duckdb:
        - name: local_duckdb
          path: data/analytics.duckdb
          read_only: true

Prefer read_only: true for DuckDB dashboards unless the project explicitly needs writes.

YAML Dashboard

name: Sales
description: Revenue and customer activity
connection: local_duckdb

filters:
  - name: region
    type: select
    default: All
    options:
      values: [All, North America, Europe, APAC]
  - name: date_range
    type: date-range
    default: last_30_days

rows:
  - widgets:
      - name: Revenue
        type: metric
        sql: |
          SELECT SUM(amount) AS value
          FROM sales
          WHERE created_at >= '{{ filters.date_range.start }}'
            AND created_at <= '{{ filters.date_range.end }}'
          {% if filters.region != 'All' %}
            AND region = '{{ filters.region }}'
          {% endif %}
        value:
          field: value
          type: number
          format: "$,.2f"
        col: 3

Widget types are metric, chart, table, text, divider, and image.

A table column takes name, label, number (value format: number, currency, or a d3-format string), align (left/center/right — overrides the type-inferred alignment of the header and body cells, e.g. to right-align a text value like £177K), like, hidden, and format. format is an ordered list of layers; for each cell the first layer that matches wins. A scalar format string (e.g. format: currency) is also accepted as a legacy alias for number — prefer number in new dashboards.

• With if (+ value), the layer styles only the cells that match. value is a scalar, [low, high] for is_between/is_not_between, { column: <name> } to compare against another column in the same row, or omitted for empty checks. Operators: is_empty, is_not_empty, text_contains/text_does_not_contain/text_starts_with/text_ends_with/text_is_exactly, date_is/date_before/date_after (by day, or exact instant with a time), greater_than/greater_than_or_equal/less_than/less_than_or_equal, is_equal_to/is_not_equal_to, is_between/is_not_between.
• With no if, the layer styles every cell — a gradient (backgroundColor is a list of 2+ colors; optional range list + unit = absolute/percent/percentile, omit range for auto min/max) or a flat fill (backgroundColor is a string). Put it last as the fallback.
• Styles on any layer: backgroundColor, textColor, bold, italic, underline, strikethrough.
• like: mirror another column's coloring, driven by that column's per-row value, while keeping this column's own number.
• hidden: true: keep the column in the result but don't render it. Optional. Coloring reads a column whether or not it's shown, so hide only to drop it from the display, e.g. a like source you must declare but don't want visible.

Each layer is a YAML object, so - { backgroundColor: [red, white, green], range: [-25, 0, 25], unit: absolute } and the same keys written as an indented block are identical — use whichever reads better.

Colors are named (red green blue indigo cyan purple pink amber, plus white/black, aliases positive/negative/warning) or hex. Named colors adapt to light and dark.

Worked example:

name: Regions

rows:
  - widgets:
      - name: Regions
        type: table
        col: 12
        sql: SELECT revenue, growth, score, status, actual, target, bonus, health FROM regions
        columns:
          - name: revenue
            number: currency
            format:
              - { backgroundColor: [red, white, green] }                # gradient, auto min→max
          - name: growth
            number: number
            format:
              - { backgroundColor: [blue, white, amber], range: [-25, 0, 25], unit: absolute }   # fixed anchors; unit also percent/percentile
            
             
                                                                 
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Filters

Dashboard filters are UI controls. SQL dashboards use filter values through Jinja templates.

Supported filter types:

• select
• date-range
• date
• number
• text

Date range presets include today, yesterday, last_7_days, last_30_days, last_90_days, this_month, last_month, this_quarter, this_year, year_to_date, and all_time.

Both single and multiple select filters show a searchable dropdown, so you can type to find an option quickly when the list is long.

Select filters support multiple: true for multi-select. The value is a list — render with join in Jinja and guard the empty case:

{% if filters.status and filters.status | length > 0 %}
  AND status IN ('{{ filters.status | join("','") }}')
{% endif %}

Filter values are kept in the URL query string, so you can share a filtered dashboard as a link. Each filter becomes one query parameter named after it, for example ?region=Europe&date_range=last_30_days. When a select has multiple: true the values are comma separated, and a date-range is either a preset key or start..end. Anything read from the URL is checked against the filter's type and options, and ignored if it doesn't match.

Current Viewer (bruin.user_email)

{{ bruin.user_email }} is the email of the signed-in user viewing the dashboard — a Bruin Cloud runtime feature that resolves per viewer, so one dashboard can show each person only their own rows:

SELECT * FROM orders WHERE owner_email = '{{ bruin.user_email }}'

Locally there is no signed-in user, so the value comes from the BRUIN_USER_EMAIL environment variable (empty if unset). To preview a user-scoped dashboard as a specific person, pass it inline: BRUIN_USER_EMAIL=[e-mail] dac dev. In Bruin Cloud this becomes dynamic per signed-in viewer.

Named Queries

Use named queries when multiple widgets share the same SQL or semantic query.

queries:
  revenue_by_region:
    sql: |
      SELECT region, SUM(amount) AS revenue
      FROM sales
      GROUP BY 1

rows:
  - widgets:
      - name: Revenue by Region
        type: chart
        chart: bar
        query: revenue_by_region
        x: { field: region }
        y: { field: [revenue] }
        col: 6

A chart's x and y are axis encoding objects with a required field (bare column names like x: region are invalid). field may be a single column or a list. On line/area (and combo), y also takes beginAtZero: true (anchor the value axis at 0), markers: false (hide point dots), curve (smooth/straight/stepline — chart-wide interpolation; use straight for period totals), and dash (chart-wide dash pattern every series inherits: dotted/dashed/long-dash; omit for solid). Per-series style overrides go in a widget-level series map (a sibling of x/y, not inside y), keyed by y-column: series: {column: {color: "#EC4899", curve: straight, dash: dashed}} — each key falls back to the chart-wide default / palette; store only genuine differences. Label/value charts (pie/treemap/funnel) style per slice instead, via a sibling slices map keyed by the slice's data label: slices: {Enterprise: {color: "#8B5CF6", label: "Enterprise (2026)"}} — color overrides the palette, label renames the displayed slice; both optional.

Add a second (right-hand) value axis with y2 when two series live on different scales (e.g. revenue $ and conversion %) and one would otherwise be squashed flat. A y-column plots against the right axis when it is listed in y2.field; all other series stay on the left y axis. y2 is a full axis encoding (same title/format/beginAtZero/curve/dash keys as y) and each axis formats its ticks and tooltip values independently. Supported on line/area/bar/combo; a column belongs to exactly one axis, y2.type must be number, and y2 cannot combine with stacked, horizontal bars, or color. Axis (y vs y2) and shape (bar vs line via lines) are independent — the classic combo is revenue bars on the left with a rate line on the right: chart: combo, lines: [conversion_rate], y: {field: [revenue], format: "$,.0f"}, y2: {field: [conversion_rate], format: ".1%"}.

The funnel chart shows conversion through ordered stages: one bar per stage with its share of the top of the funnel and the step-to-step conversion. Use label (stage) and value (count), and order rows top-of-funnel first in SQL. horizontal: true lays the stages left-to-right, and bar labels honor value.format (e.g. "$,.0f" for a revenue funnel).

Confidence intervals use yMin/yMax (the lower/upper bound columns): on line/area they shade a CI band behind the estimate line; on bar they become error-bar caps; the forest chart draws a point estimate + horizontal CI per category (grey when the interval spans 0, horizontal: false for a vertical dot-and-whisker). y is the estimate only. yMin/yMax are a single column, or a per-series map {seriesColumn: boundColumn} for multi-line bands. Compute the bounds in SQL (e.g. effect ± 1.96*stderr). Reference guides: refLines: [{ axis: x|y, value, label? }] (dashed line, e.g. a 0 "no-effect" mark) and refBands: [{ axis: x|y, from, to, label? }] (a shaded range, e.g. a ±MDE band).

Vega-Lite charts

Use chart: vega-lite with a spec object for advanced layered, faceted, concatenated, or transformed visualizations. DAC still owns the query and injects its result as the named dac dataset:

- name: Revenue with confidence interval
  type: chart
  chart: vega-lite
  sql: SELECT month, revenue, lower_ci, upper_ci FROM monthly_revenue ORDER BY month
  spec:
    data: { name: dac }
    encoding:
      x: { field: month, type: temporal }
    layer:
      - mark: { type: area, opacity: 0.14 }
        encoding:
          y: { field: lower_ci, type: quantitative }
          y2: { field: upper_ci }
      - mark: { type: line, strokeWidth: 2 }
        encoding:
          y: { field: revenue, type:  }

spec.data is optional and defaults to { name: dac }. If provided, it must use that name. Do not use data.url or define datasets.dac; load primary data through sql, query, semantic fields, or the widget's illustrative inline data. DAC supplies theme and responsive-size defaults, while explicit Vega-Lite config, width, height, and autosize values override them.

Every query is an inline sql: block or a named query: reference — YAML widgets do not take file paths. In TSX, include("queries/revenue.sql") reads a .sql file into an inline query at load time.

Inline (Static) Data

A metric, chart, or table widget can carry its values inline with data instead of a query. A widget with data renders without a connection or SQL — columns are the column names and rows is one positional list per row. The encoding fields (x, y, value, label, columns) reference the column names.

rows:
  - widgets:
      - name: Revenue by Quarter
        type: chart
        chart: bar
        col: 6
        data:
          columns: [quarter, revenue]
          rows:
            - [Q1, 12000]
            - [Q2, 15500]
            - [Q3, 14200]
            - [Q4, 18900]
        x: { field: quarter, type: category }
        y: { field: [revenue], type: number, format: "$,.0f" }

Use this only when there is genuinely no data connection — e.g. a brand-new project where .bruin.yml has no connections, a hardcoded illustrative example, or a layout mockup. When a connection exists, always use sql:, query:, or a semantic widget instead. Inline data is frozen: it never refreshes, ignores filters, and goes stale. Do not paste real query results into data to "cache" them, and do not present made-up numbers as real — tell the user inline values are illustrative until a warehouse is connected.

Rules:

• data is mutually exclusive with sql, query, and semantic fields (model, dimension, metrics, …). Setting both fails validation.
• Every row must have exactly one value per column.
• Not valid on text, image, or divider widgets.
• A dashboard built entirely from data widgets needs no top-level connection.

Semantic Models

Semantic models live in semantic/*.yml.

name: sales
label: Sales
source:
  table: marts.sales

dimensions:
  - name: created_at
    type: time
    granularities:
      month: date_trunc('month', created_at)
  - name: region
    type: string
  - name: channel
    type: string

metrics:
  - name: revenue
    expression: sum(amount)
    format:
      type: currency
      currency: USD
      decimals: 0
  - name: orders
    expression: count(*)
  - name: average_order_value
    expression: "{revenue} / nullif({orders}, 0)"

segments:
  - name: online
    filter: "channel = 'online'"

Metrics are aggregate SQL expressions or expressions over other metrics using {metric_name} references. Dimensions are the only fields valid for semantic filters.

Joins

A model can join to other models so a query can group, filter, or sort by dimensions on a related model. Declare a joins block on the model and a primary_key on the join target, then reference joined dimensions as relation.dimension.

# semantic/orders.yml
name: orders
source:
  table: marts.orders
primary_key: order_id
joins:
  - name: customers          # relation name; also the target model name unless `model:` is set
    relationship: many_to_one
    foreign_key: customer_id # column on this model pointing at customers.primary_key
dimensions:
  - name: category
    type: string
metrics:
  - name: revenue
    expression: sum(amount)

# semantic/customers.yml
name: customers
source:
  table: marts.customers
primary_key: customer_id
dimensions:
  - name: country
    type: string

A widget or named query on orders then references the joined dimension by relation.dimension:

- name: Revenue by Country
  type: chart
  chart: bar
  dimension: customers.country   # dimension from the joined customers model
  metrics: [revenue]

Relationships: one_to_one, many_to_one, one_to_many, many_to_many. Use target_key to override the joined column, or sql for a custom join condition.

Semantic Dashboard

name: Semantic Sales
connection: local_duckdb
model: sales

filters:
  - name: region
    type: select
    default: North America
    options:
      values: [North America, Europe, APAC]

rows:
  - widgets:
      - name: Revenue
        type: metric
        metric: revenue
        filters:
          - dimension: region
            operator: equals
            value: "{{ filters.region }}"
        value:
          field: revenue
          type: number
          format: "$,.0f"
        col: 3

      - name: Revenue by Month
        type: chart
         
         
         
         []
        
            
             
         

A widget can set model directly, or inherit the dashboard-level model. For multiple models, use a dashboard-level models map and reference the model alias on widgets or named queries.

Semantic filter operators include equals, not_equals, gt, gte, lt, lte, in, not_in, between, is_null, and is_not_null.

TSX Dashboard

Use TSX when the dashboard needs variables, loops, reusable components, conditionals, or generated layouts.

export default (
  <Dashboard name="Semantic Sales" connection="local_duckdb" model="sales">
    <Filter
      name="region"
      type="select"
      default="North America"
      options={{ values: ["North America", "Europe", "APAC"] }}
    />

    <Row>
      <Metric
        name="Revenue"
        metric="revenue"
        filters={[
          { dimension: "region", operator: "equals", value: "{{ filters.region }}" },
        ]}
        value={{ field: "revenue", type: "number", format: "$,.0f" }}
        col={3}
      />
      <Chart
        name="Revenue by Month"
        chart="area"
        =
        =
        =""]}
        =  "",  "" }]}
        =
      />
    
  
)

TSX supports the same dashboard model as YAML. Keep semantic logic declarative; do not manually compile semantic metrics to SQL in TSX.

Deprecated Fields

These fields were removed from the DAC schema. Never emit them in new dashboards. If you encounter any of them while reading or editing an existing dashboard, refactor them to the current form — preserving the original column, formatting, and labels — and re-run dac validate to confirm the dashboard still loads.

Deprecated	Replacement
Chart x: col / y: [col] (bare column names)	x: { field: col } / y: { field: [col] } — axis encoding objects with a required field
Widget or named-query file: path.sql	Inline sql: or a named query: reference. In TSX, include("path.sql") reads a .sql file into inline SQL at load time
Metric widget column, prefix, suffix, format (flat fields)	value: { field: <column>, type: number, format: "<d3-format>" }
Dashboard inline semantic: block (source / metrics / dimensions)	Define the model in semantic/*.yml and reference it with model:

Authoring Rules

• Keep dashboard files focused on presentation and query intent.
• Prefer semantic widgets when metrics or dimensions are reused.
• Use direct SQL for one-off custom queries or non-semantic dashboards.
• Use inline data only when there is no connection; prefer sql/query/semantic whenever one exists, since inline data never refreshes.
• Validate both YAML and TSX dashboards after changes.
• Do not require semantic models for regular SQL dashboards.
• Do not put secrets in dashboard files; use Bruin connection config.