Headless preview — render a spec against live data

You can render a single Graphein ChartSpec to a PNG and a JSON report on this machine, with no browser, using @graphein/node. Pair it with a live fabric-app-data query result and you can check a visual's real presentation — does the data shape suit this chart, do labels clip, do colors read, is the trend what you expected — in seconds, before it ships.

This is the loop. Headless preview is the agent's validation for every visual: presentation, data fit, clipping, overlap, and contrast. Deploy with rayfin up to ship the app once you've preview-validated.

When to use it

Use headless preview when checking any visual against live data, choosing a type or encoding, catching clipping/overlap/contrast/wrong grain, and tuning formatting, labels, series, sort, or transforms. KPI/table/matrix/slicers/ dashboard rasterize to PNG too, so preview-validate them before shipping.

Graphein renders headlessly by design (the same engine draws in the browser and in Node via @graphein/node), so this works with no data connection: the bundled demo dashboard (src/demo/) is all Graphein specs over inlined rows — render any of them straight from spec.data (offline) to eyeball the starter, and use the same loop with --query/--data once you've swapped in the real model.

The loop

1. fabric-app-data query <alias> --file q.dax     → live rows (JSON)
2. author a ChartSpec (type + encoding)           → spec.json
3. npm run preview -- --spec spec.json --data rows → PNG + report JSON
4. VIEW the PNG (you have vision) + READ the report
5. not right? adjust the spec / DAX → back to 3
6. happy? drop the spec into a <ChartCard>, then deploy with `rayfin up`

Run it

# Spec with data already inlined (fully offline, the simplest check):
npm run preview -- --spec hero.json

# Author a spec WITHOUT data, fetch live rows in one shot:
npm run preview -- --spec hero.json --query sales --dax-file src/queries/hero.dax

# Or feed a pre-fetched query result you already have:
npx fabric-app-data query sales --file src/queries/hero.dax > rows.json
npm run preview -- --spec hero.json --data rows.json --theme dark

npm run preview -- forwards flags to node scripts/preview-visual.mjs. The spec is the same JSON you pass to <ChartCard spec={…} /> (a type + encoding, optionally with data). --spec - and --data - read stdin.

Flags

Data precedence: --query (live) or --data (pre-fetched) overrides any inlined spec.data; with neither, the spec's own data is used. Rows are mapped exactly like toChartData — column short names (Sales[Month] → Month), numeric DAX types coerced to numbers — so the keys you reference in encoding match. Pass --columns to alias them (and to disambiguate two columns that share a short name), just like toChartData({ columns }).

Read the report

The script prints one JSON object. Treat it as a critique checklist:

{
  "ok": true,                 // false ⇒ the report found a presentation problem
  "rendered": true,
  "type": "line",
  "out": "…/line.png",        // VIEW this image
  "theme": "light",
  "fontParity": true,         // Inter registered → matches the deployed look
  "dataRows": 12,             // how many rows actually rendered (0 ⇒ empty tile!)
  "marks": 12, "series": 1, "colors": 1,
  "summary": "Revenue rose 92% from 120 to 230 between Jan and May.",
  "diagnostics": [            // clipping / overlap / contrast / axis issues
    { "code": "label-overlap", "severity": "warning", "message": "…", "axis": "x" }
  ],
  "lint": [ … ],              // validateSpec warnings (soft issues)
  "repaired": [ … ]           // JSON-Patch ops auto-applied to fix the spec
}

• View the PNG at out — that is the real deployed-look render (app theme + Inter font). Judge it like a reviewer: legibility, clipping, color, grain.
• ok: false or any diagnostics → fix before shipping. Common codes: label/axis overlap, clipped marks, low contrast, too many colors, empty plot.
• dataRows: 0 (or marks: 0) → your encoding fields don't match the mapped row keys, or the query returned nothing. Fix the field names / --columns / the DAX — never ship an empty tile.
• summary is Graphein's own read of the data — a quick sanity check that the chart says what you think (e.g. confirms the trend direction).

Validate → repair (built in)

Before rendering, the script runs validateSpec(spec); if it's invalid it tries repairSpec(spec) and re-validates. You get:

• A clean render plus any repaired patch ops (apply the same fixes to your source spec) when repair succeeds.
• Exit 1 with errors (path-pointed) when the spec can't be auto-repaired — read the path/message, fix the spec, re-run.

You can also call these directly while authoring (all re-exported from the kit): validateSpec(spec) → { valid, errors, warnings }, repairSpec(spec) → { spec, applied, remaining }, summarize(spec) → string.

Exit codes

What renders headlessly

Supported: line, area, bar, scatter, box, pie, heatmap, sankey, choropleth, combo, histogram, funnel, treemap, gauge, bullet, calendarHeatmap, waterfall, slope, dumbbell — plus kpi, table, matrix, slicers (dropdown, list, search, range, dateRange), and dashboard. Chart specs also support transform, annotations, insights, trendline, and facet.

Notes

• The PNG defaults to a temp path so nothing lands in the repo; pass --out only if you want it somewhere specific (don't commit it).
• Theme/font parity is automatic: the script parses src/global.css tokens (the same --color-* bridge as lib/graphein-theme.ts) and registers the bundled Inter, so a preview looks like the deployed chart. Edit global.css to recolor — never hardcode hex in a spec.
• This is the validation loop for every visual; preview-validate before you deploy with rayfin up.