Selects and configures Malloy chart annotations for data visualization rendering.
npx skills add https://github.com/mathisdrn/orca --skill malloy-charts-mathisdrn
Please help me install this Agent Skill. Skill: malloy-charts Source: https://github.com/mathisdrn/orca/tree/main/.agents/skills/malloy-charts Command: npx skills add https://github.com/mathisdrn/orca --skill malloy-charts-mathisdrn
# tag matches your data shape, and misconfigured annotations silently fail or render incorrectly. This Skill provides a decision tree and complete renderer reference so you pick the correct chart type and apply valid tags the first time. ## Core Features & Use Cases - Chart Selection Decision Tree: Maps data shapes (time series, categories, geographic, KPIs) to the correct renderer such as # bar_chart, # line_chart, # shape_map, or # big_value. - Renderer Reference: Documents properties, field role tags, and layout options for charts, dashboards, pivots, transposes, and sparkline KPI cards. - Formatting and Theming: Covers field formatting tags (# currency, # percent, # number), model-level defaults, and light/dark theme palette annotations. - Use Case: You have a Malloy view grouping revenue by month and want a trend chart with a KPI card. The Skill tells you to use # line_chart for the trend and # big_value with a sparkline nest for the KPI, including the exact annotation syntax. ## Quick Start Ask the AI which Malloy chart to use for your query result and how to annotate the view with the right tags.High-intent search queries and answers about installing and using this skill.
Match the chart to your data shape: use `# line_chart` for one time column plus a measure, `# bar_chart` for a category plus a measure, `# scatter_chart` for two numeric columns, and `# big_value` for aggregates without group_by. The decision tree in this Skill maps each shape to a default renderer.
Place the tag on its own line directly above the view definition, for example `# bar_chart` before `view: by_carrier is {...}`. Tags on the same line or on a `nest:` line instead of the view definition will not render correctly.
Charts render only the first aggregate by default. To display multiple measures, use the `y=['a','b']` property on the chart tag, such as `# bar_chart { y=['revenue','cost'] }`, or use `# flatten` for side-by-side filtered aggregates.
Common causes include putting the tag on a `nest:` line instead of the view definition, placing multiple tags on one line, or adding a chart tag to a query with nests, which hides the nested views. Use `# dashboard` on the outer query to render nested chart tiles.
Malloy has no native pie, donut, treemap, or heatmap renderer. Approximate a pie chart with a `# bar_chart` sorted by value, and a heatmap with `# pivot` using color values. Funnels map to ordered bar charts and gauges to `# big_value` with a comparison field.
Malloy has no auto-binning function; `autobin()` does not exist. Bin manually with arithmetic such as `floor(price / 20) * 20` in a group_by, choosing the bin width from the column's observed min, max, and percentiles, then render with `# bar_chart`.
malloydataGuide chart type selection and rendering for Malloy views.
data-goblinCreates complete Power BI reports from scratch using the pbir CLI workflow.
malloydataProvides guidance on writing Malloy queries for data analysis.
Amazon-QuickCompute TAM, SAM, and SOM with triangulated methods and sensitivity analysis.