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	3

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.

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.

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 %}

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.

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
        chart: area
        dimension: created_at
        granularity: month
        metrics: [revenue]
        sort:
          - name: created_at
            direction: asc
        col: 9

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"
        dimension="created_at"
        granularity="month"
        metrics={["revenue"]}
        sort={[{ name: "created_at", direction: "asc" }]}
        col={9}
      />
    </Row>
  </Dashboard>
)

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.