Imported from credibledata/credible-plugin (codex/skills/malloy-charts/SKILL.md). Install upstream with npx skills add credibledata/credible-plugin --skill malloy-charts. Copyright stays with the author.

Chart Selection for Malloy

Malloy uses Vega-Lite under the hood. # tags control visualization. Call search_malloy_docs with topic "rendering" for the full tag reference (or see https://docs.malloydata.dev/documentation/visualizations/overview).

Tool names are written bare here - get_context, execute_query, search_malloy_docs. The exact prefixed name depends on the host surface; match each against the tools you actually have.

Decision Tree: Which Chart?

Constraints:

• ONE aggregate per chart view (charts render only the first; use y=['a','b'] for multi-measure)
• No fixed scale on measure definitions: use # currency not # currency=usd0m
• One tag per line
• Alias joined fields in group_by before order_by
• Define measures in source, not in views

Chart Types

# bar_chart

Data shape: group_by = x-axis, aggregate = y-axis, optional 2nd group_by = series.

# bar_chart
view: by_carrier is { group_by: carrier, aggregate: flight_count, order_by: flight_count desc, limit: 10 }

# bar_chart.stack
view: by_region is { group_by: category, region, aggregate: revenue }

# bar_chart { y=['revenue','cost'] }
view: rev_vs_cost is { group_by: category, aggregate: revenue, cost }

Key properties: .stack, .size (spark/xs/sm/md/lg/xl/2xl), .x, .x.limit, .y (supports y=['a','b']), .series, .series.limit (default 20), .title, .subtitle, .x.independent, .y.independent

Field role tags: # x, # y, # series on individual fields to assign roles explicitly.

# line_chart

Data shape: group_by (temporal/numeric) = x-axis, aggregate = y-axis, optional 2nd group_by = series.

# line_chart
view: trend is { group_by: order_month, aggregate: revenue, order_by: order_month }

# line_chart { size=spark }
view: mini_trend is { group_by: order_month, aggregate: revenue, order_by: order_month }

Key properties: .zero_baseline, .interpolate (e.g., step), .size, .y (supports y=['a','b']), .series.limit (default 12), .title, .subtitle

# scatter_chart

Data shape: Fields by position: x, y, color, size (bubble), shape.

# scatter_chart
view: correlation is { group_by: customer_id, aggregate: avg_price, total_quantity }

# shape_map

Choropleth. US states only. Fields: state name, value.

# shape_map
view: by_state is { group_by: state, aggregate: revenue }

# segment_map

Route map. US only. Fields: start_lat, start_lon, end_lat, end_lon, color.

Layout Types

# big_value

KPI cards. Aggregates only, no group_by.

# big_value
view: summary is {
  aggregate:
    # label="Revenue"
    # currency
    revenue
    # label="Orders"
    # number=auto
    order_count
}

Properties: .size, .sparkline=<nested_view_name>, .comparison_field, .comparison_label, .down_is_good

# dashboard

Card-based multi-tile layout. Apply to a view whose body is a nested query; the view's own fields lay out automatically:

• group_by dimensions -> a row header (repeats once per row; omit for a single block)
• aggregate measures -> KPI cards, one per measure
• each nest: -> a tile, rendered by the tag above it (# table default, or # bar_chart / # line_chart / # big_value)

Two modes. Flex (default): tiles flow and wrap; # break forces a new row. Columns: # dashboard { columns=N } lays tiles into N equal columns, # colspan=n widens a tile, # break starts a new row, overflow wraps.

// Flex: measures become KPI cards, the nest becomes a tile
# dashboard
view: overview is {
  group_by: category
  # currency
  aggregate:
    avg_retail is retail_price.avg()
    sum_retail is retail_price.sum()
  nest:
    # bar_chart
    by_brand is { group_by: brand, aggregate: avg_retail is retail_price.avg(), limit: 10 }
}

// Columns: # colspan widens tiles, # break ends a row
# dashboard { columns=12 }
view: layout is {
  group_by: category
  # currency
  aggregate:
    # colspan=4
    avg_retail is retail_price.avg()
    # colspan=4
    sum_retail is retail_price.sum()
    # colspan=4
    max_retail is retail_price.max()
  nest:
    # break
    # colspan=6
    # bar_chart
    # subtitle="Top brands"
    by_brand_chart is { group_by: brand, aggregate: avg_retail is retail_price.avg(), limit: 8 }
    # colspan=6
    by_brand_table is { group_by: brand, aggregate: product_count is count(), limit: 8 }
}

Tags: # dashboard { columns=N } (columns mode), { gap=PX } (tile spacing, default 16; never a mode), { table.max_height=PX|none } (cap table tiles). On a measure or nest: # colspan=N (columns mode only), # break (both modes), # subtitle="..." (tile), # borderless (drop card chrome), # label="..." (card title).

For rich KPI cards (sparklines, comparison deltas, several metrics on one card) nest a # big_value view instead of relying on the dashboard's own measures:

# dashboard
view: kpis is {
  group_by: category
  nest:
    # big_value
    revenue_card is {
      aggregate:
        # label="Revenue"
        # currency
        # big_value { sparkline=trend }
        total_revenue is retail_price.sum()
      # line_chart { size=spark y.independent=true }
      # hidden
      nest: trend is { group_by: bucket is floor(id / 100)::number, aggregate: total_revenue is retail_price.sum(), order_by: bucket, limit: 20 }
    }
}

Rules: # dashboard needs a nested-query view (no effect on a scalar). # colspan works only in columns mode and is ignored (warns) in flex. columns is any positive integer; a # colspan over the column count clamps to a full row. Style tiles via the instance theme, or theme the views inside with # theme.* (see Theming below).

# pivot

Pivot nested results into columns. Max 30 pivot columns.

view: sales is {
  group_by: product, aggregate: total
  nest: # pivot
    by_quarter is { group_by: quarter, aggregate: revenue }
}

# transpose

Swap rows/columns. Good for period comparisons.

# transpose
view: comparison is {
  aggregate:
    # label="This Month"
    current_revenue
    # label="Last Month"
    prior_revenue
}

# list / # list_detail

List renders as comma-separated values. List_detail shows value (detail) pairs.

# flatten

Collapse nested record into parent table as columns. Use for side-by-side filtered aggregates:

view: segments is {
  group_by: product, aggregate: total_revenue
  nest: # flatten
    enterprise is { where: segment = 'Enterprise', aggregate: # label="Enterprise" revenue }
  nest: # flatten
    smb is { where: segment = 'SMB', aggregate: # label="SMB" revenue }
}

# table

Default (implicit). Use explicitly for .size=fill property.

Field Formatting Tags

Currency codes: usd ($), eur, gbp. Scale: K/M/B/T/Q or auto. Number suffix styles: word ("42.5 million"), letter ("42.5M"), scientific.

Utility Tags

Model-Level Defaults

## viz.line_chart.defaults.y.independent=true
## viz.bar_chart.defaults.stack

Theming

Publisher styles charts and tables from one structured theme. The instance sets it (in publisher.config.json's theme block or the Settings, then Theme editor); a model overrides it per result with # theme.* annotations, or model-wide with ## theme.*. Per-chart annotations use the same nested palette.* / font.* vocabulary as the config, not flat key names, and they win over the instance theme for the keys they set. The forms:

The seven palette.* color keys each take a .light and/or .dark variant so dark mode gets its own value. palette.series, font.family, and font.size are single values shared across modes.

// Model-wide defaults (## applies to every view in the model):
## theme.palette.series = ["#14b3cb", "#e47404", "#1474a4"]
## theme.font.family = "Inter, sans-serif"

// Per-view override (# applies to this result only; beats the instance theme):
# theme.palette.background.light = "#fafafa"
# theme.palette.background.dark = "#111111"
# theme.palette.tableHeader.dark = "#94a3b8"
view: revenue_by_month is {
  group_by: month
  aggregate: revenue
}

Precedence, highest to lowest, per key: # theme.* on the view, then ## theme.* model default, then the instance theme, then Publisher's built-in defaults. A per-chart annotation overrides the instance for the keys it sets; unset keys fall through to the instance. (This is the reverse of a bare @malloydata/render embed, where the embedder wins: Publisher reads the annotation itself and layers it on top.)

Quote values that contain spaces or a leading #. The light/dark default (defaultMode) and the toggle lock (allowUserToggle) are instance-only: set them in the config theme block or the editor, not as annotations. The malloy-gotchas-rendering skill lists the annotation forms that look valid but do nothing.

Advanced Patterns

Sparklines in KPI Cards

# big_value { sparkline=trend }
view: revenue_kpi is {
  aggregate: # label="Revenue" # currency revenue
  nest: # line_chart { size=spark } # hidden
    trend is { group_by: order_date, aggregate: revenue, order_by: order_date }
}

KPIs with Comparison Deltas

# big_value { comparison_field=prior_month comparison_label="vs Last Month" }
view: rev_delta is {
  aggregate: # label="Revenue" # currency revenue, # hidden prior_month
}

Use down_is_good=true for metrics where decrease is positive (churn, defects).

Inline Mini-Charts in Table Rows

view: carriers is {
  group_by: carrier, aggregate: flight_count
  nest: # line_chart { size=spark }
    trend is { group_by: month, aggregate: flight_count, order_by: month }
}

Multi-Measure Series

# bar_chart { y=['revenue','cost'] }
view: rev_vs_cost is { group_by: quarter, aggregate: revenue, cost }

Hierarchical Drill-Down

# list_detail
view: explorer is {
  group_by: region, aggregate: revenue
  nest: # bar_chart
    by_category is { group_by: category, aggregate: revenue, order_by: revenue desc, limit: 10 }
}

Distribution (Histogram)

There is no auto-binning function: autobin(...) does not exist and fails with Unknown function 'autobin'. Bin by arithmetic, choosing the width from the column's actual range: query min, max and a few percentiles first, and say in the view's doc where the width came from. A bin width nobody derived is a business decision in disguise.

# bar_chart
view: price_dist is {
  group_by: bucket is floor(price / 20) * 20   // 20 is the bin width, from the observed range
  aggregate: order_count is count()
  order_by: bucket
}

Patterns for Missing Chart Types

Chart Annotations on Queries with nest:

A top-level chart tag (e.g., # bar_chart) renders only the outer query; any nest: views are silently hidden from the rendering (still in raw data). To show nests, use # dashboard on the outer query with chart tags on each nest. Otherwise, drop the nest:.

Common Mistakes

NOTE: The term 'constructor' is a reserved term in Vega-Lite. If the word 'constructor' appears in the query, it will cause the rendering to fail. Never use it in a query and avoid using it as a dimension in a model.

For more patterns, call search_malloy_docs with topics like "bar charts", "line charts", "dashboards", "histograms", "percent of total", "comparing timeframes", or "pivots".

Further Reading

• Visualizations Overview - Official docs
• Bar Charts - Stacked, grouped, series
• Bump Charts Blog - Ranking over time
• Dataviz is Hierarchical - Nested data visualization philosophy