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.
Scanned 9/3/2026
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---
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: 7
---
# 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
```text
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
```shell
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`.
```yaml
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
```yaml
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), `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:
```yaml
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
- name: score
number: number
format: # conditions, first match wins
- { if: greater_than_or_equal, value: 80, backgroundColor: green }
- { if: is_between, value: [50, 79], backgroundColor: amber }
- { if: less_than, value: 50, textColor: red, strikethrough: true }
- name: status
format:
- { if: text_contains, value: urgent, backgroundColor: amber, bold: true }
- { if: is_empty, backgroundColor: "#F3F4F6", italic: true } # flat fill (string)
- name: actual
number: number
format: # cross-column, same row
- { if: greater_than, value: { column: target }, backgroundColor: green }
- name: target
hidden: true # in the result for the rule above, not rendered
- name: bonus
number: currency
like: score # mirror score's colors, keep own number
- name: health
number: number
format: # a condition wins over the gradient base below
- { if: is_equal_to, value: 0, backgroundColor: red, bold: true }
- { backgroundColor: [red, white, green] } # base, last (always matches)
```
## 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:
```sql
{% 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:
```sql
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=someone@example.com 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.
```yaml
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.
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).
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.
```yaml
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`.
```yaml
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`.
```yaml
# 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)
```
```yaml
# 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`:
```yaml
- 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
```yaml
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.
```tsx
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.
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