Flint chart

flint-chart: pick, author, and render a chart

What you produce (and what you do NOT)

Your output is the spec: the chart_spec and semantic_types of a ChartAssemblyInput. You reference data columns by name. The host passes the resulting input to assembleVegaLite, assembleECharts, or assembleChartjs to get a backend spec.

You write the input spec, not the output spec. And critically:

• DO emit chart_spec (chart type, channel→field mapping, properties) and semantic_types (field → semantic type).
• Reference columns by name. How data itself gets bound depends on the situation — a URL, a host-side variable, or embedded rows (see "How data gets bound"). Embedding is fine for small tables; just don't re-serialize a large dataset by hand, since that risks truncation and silent value corruption and wastes tokens.
• Transform data before Flint. If the requested chart needs aggregation, filtering, joins, pivots, derived columns, or long/wide reshaping beyond Flint's built-in static-series fold, use a coding, notebook, SQL, or data tool first. Then author the Flint spec against the transformed table.
• Style after Flint, only when needed. Author structure in Flint. For a presentation tweak Flint does not express (a reference line, annotation, or shaded band), use the Vega-Lite escape hatch — see "Post-Flint style customization". Never feed edited Vega-Lite JSON back to render_chart.
• Look at what you rendered. A chart with a collapsed scale, a merged color scale, or an empty data binding renders as a valid image that tells the wrong story — validate_chart cannot catch that. Load the render-verify skill after rendering, and always after a post-Flint Vega-Lite edit.

Verify Flint is available before rendering

Before you promise to render a chart, confirm the tools exist. If they don't, install them (or ask the user to). Failing loudly early is cheaper than authoring a spec no one can render.

For MCP rendering (default in this skill)

1. Check for the flint MCP server. Look in your available tool inventory for render_chart, compile_chart, validate_chart, list_chart_types, or create_chart_view. If any of them are present, the server is registered and reachable — skip to step 4.

2. If the tools are missing, flint-chart-mcp is not registered. Ask the user to add it, or add it yourself if you can edit their workspace config.

   Put the file in the right place — this is the single most common failure.

   Host	Path	Top-level key
   VS Code (workspace)	.vscode/mcp.json	servers
   Claude Code / Claude Desktop	.mcp.json at workspace root	servers
   Cursor	.cursor/mcp.json	servers
   GitHub Copilot CLI	~/.copilot/mcp-config.json	mcpServers

   The schema is identical across the first three, which is exactly why the wrong path looks like it should work. VS Code never reads a workspace-root .mcp.json — and it reports no error, because it isn't parsing a broken file, it's reading no file at all. If a user says "I added the config and nothing happened," check the path before anything else.

   Copilot CLI differs twice over: different path and a different top-level key (mcpServers, not servers). A servers block pasted there fails just as silently. Prefer telling the user to run /mcp add inside a CLI session and let it write the file. The path is overridable via $COPILOT_HOME.

   Always merge into any existing config rather than overwriting it — clobbering the file destroys whatever other servers the user had.

   // .vscode/mcp.json (VS Code) — merge with any existing "servers" map
   {
     "servers": {
       "flint": {
         "type": "stdio",
         "command": "npx",
         "args": ["-y", "flint-chart-mcp@^0.2.2"],
       },
     },
   }
   

   • "type": "stdio" is optional in some hosts but always declare it — omitting it makes transport-related failures harder to diagnose.
   • npx -y fetches the package on first use and caches it (~5-10 MB in the npm cache; ~1-2 s cold start).
   • The ^0.2.2 pin is held deliberately, not through neglect. Public npm latest is 0.4.0, but it is unreachable from Microsoft corporate machines, whose npm mirror stops at 0.2.2. Do not advise a user to bump the pin without checking npm config get registry first — a corporate mirror can report a stale latest that is not the public one, and on such a machine a ^0.4.0 pin fails with ETARGET.
   • Corporate / air-gapped: if npx cannot reach the npm registry, ask the user to run npm install -g flint-chart-mcp once from a machine that can, then change "command": "npx", "args": ["-y", "flint-chart-mcp"] to "command": "flint-chart-mcp", "args": [].
   • Hardened deployment (only inline data.values accepted, no local data.url files): append "--disable-file-reference" to args.

3. After adding, the host must reload for MCP servers to spawn. VS Code: Ctrl+Shift+P → "Developer: Reload Window". Claude Desktop / Cursor: restart the app.

4. Verify. Call list_chart_types with { "backend": "vegalite" }. If it returns the chart catalog, the server is up.

5. If the tools still do not appear, isolate which half is broken before guessing. The server and the client fail identically from chat. If the user has the plugin repo checked out, node scripts/verify-install.mjs does this in one step; otherwise probe the server yourself — pipe a handshake plus a tools/list into the binary over stdio and read the response. A serverInfo block followed by a tools array proves the server is healthy and the fault is config, trust, or session staleness. Then work down this list:

   1. Trust prompt. VS Code will not start a local stdio server until you approve it. Ctrl+Shift+P → MCP: List Servers → pick flint → Start, and watch for the approval dialog.
   2. Server output. Same menu → Show Output. Startup crashes surface there and nowhere else.
   3. Restart the chat session. A window reload is not always enough — the agent's tool inventory can stay stale until the session itself restarts.
   4. HTTP transport only: an HTTP server needs OAuth authorization after starting, which is a separate step from trust. A server can be configured and started yet still unauthorized.

6. For deeper MCP config — HTTP transport, allowed-host lists, deployment patterns, full CLI reference — see the canonical MCP doc: https://microsoft.github.io/flint-chart/#/mcp. Point the user there for anything beyond the stdio install path documented above.

For project code integration

Only needed if the user asked you to write code that imports flint-chart directly (not to render via MCP).

1. Check the project's package.json for flint-chart in dependencies or devDependencies. If present, skip to step 3.

2. If missing, install it and the renderer peer deps for the target backend:

   npm install flint-chart
   # Then ONE of these based on the backend you'll actually render:
   npm install vega vega-lite vega-embed   # Vega-Lite
   npm install echarts                     # ECharts
   npm install chart.js                    # Chart.js
   

3. Import in code:

   import {
     assembleChartjs,
     assembleECharts,
     assembleVegaLite,
   } from "flint-chart";
   const spec = assembleVegaLite(input); // or assembleECharts / assembleChartjs
   

For Python

Not yet published to PyPI (as of 2026-07-24). Use the npm package or MCP server for released workflows until the Python release lands.

When the user wants more than a spec

First decide which workflow the user is asking for:

• Spec authoring only: return a ChartAssemblyInput or its semantic_types + chart_spec pieces. Do not install packages or write renderer code unless asked.
• MCP chart output: if Flint MCP tools are available, default to create_chart_view whenever the user asks to see a chart — it opens an interactive, live-rendered view with a customization panel, and it validates the spec for you. Only fall back to render_chart (PNG/SVG) when the host has no App UI support or the user explicitly wants a static image. Use validate_chart to check a spec without rendering, compile_chart when the user wants backend-native JSON, and list_chart_types when you need the supported chart catalog.
• Project integration, only when the user asks for code: add Flint to an app, notebook, script, or agentic product, install/import the library, and call an assembler in code. Keep the same ChartAssemblyInput contract, then let the host render the backend result.

For MCP clients, the server can run with npx:

npx -y flint-chart-mcp

For JavaScript or TypeScript projects, install Flint first and add only the renderer peer dependencies needed by the backend you will render:

npm install flint-chart
npm install vega vega-lite vega-embed  # browser Vega-Lite rendering
npm install echarts                    # ECharts rendering
npm install chart.js                   # Chart.js rendering

Then compile with the requested backend:

import {
  assembleChartjs,
  assembleECharts,
  assembleVegaLite,
} from "flint-chart";

const vegaLiteSpec = assembleVegaLite(input);
const echartsOption = assembleECharts(input);
const chartjsConfig = assembleChartjs(input);

Python support is planned for a later release. Until the PyPI package is published, use the npm package or MCP server for released workflows.

interface ChartAssemblyInput {
  // Bound by the HOST or by you, depending on the situation (see below).
  data: { values: any[] } | { url: string };
  semantic_types?: Record<string, string>; // field → semantic type  ← you write this
  chart_spec: {
    //                        ← you write this
    chartType: string; // e.g. "Scatter Plot"
    encodings: Record<string, EncodingValue>; // channel → { field, ... } (or array)
    baseSize?: { width: number; height: number }; // target layout size, default 400×320
    canvasSize?: { width: number; height: number }; // optional hard ceiling on stretch
    chartProperties?: Record<string, any>; // per-chart tuning (optional)
  };
  options?: Record<string, any>; // global layout options (rarely needed)
}

How data gets bound

Use the binding mode that matches the runtime. Do not mix them.

1. Direct MCP rendering: embed rows. When calling render_chart, compile_chart, or validate_chart, the tool arguments are JSON. If the data is small or already transformed by another tool, pass it as data: { values: [...] }. Do not pass runtime variable names in MCP tool calls — the MCP server cannot see your local variables.
2. Direct MCP rendering: reference a local file. The flint-chart-mcp server can load data: { url: "..." } from local .json, .csv, or .tsv files. By default any local file the agent can name is readable (relative paths resolve against the working directory); a hardened deployment may reject local file references entirely via --disable-file-reference (or FLINT_MCP_DISABLE_FILE_REFERENCE), in which case pass rows inline with data.values. Remote URL fetching is disabled. If the data must be transformed first, use a coding/data tool to write a small prepared file, then reference that file.
3. Generated application or notebook code: bind runtime variables. If the user asks you to add Flint to code, write normal data-loading code first and pass a real runtime value, e.g. data: { values: rows }, to assembleVegaLite, assembleECharts, or assembleChartjs. This variable pattern is for generated code, not for MCP tool calls.

For spec-only answers, return the semantic_types and chart_spec pieces and state how the host should bind data. In the worked examples below, data is shown as { values: [] } to signal "host binds this" — focus on chart_spec and semantic_types.

Data transformation before charting

Flint is a chart compiler, not a data-wrangling layer. If the chart needs grouped totals, time buckets, filters, joins, pivots, derived ratios, or a long-form table, transform the data first with a host tool, then bind the prepared table (see "How data gets bound"). Pick semantic types and channels for the transformed columns, not for columns that no longer exist.

Sanity-read the values first — don't chart blind. Inspect the actual data with your data tool (distinct values per category column, min/max per measure), not just the column names, and watch for:

• Embedded totals. A category column may mix an aggregate level with its parts (e.g. all alongside cage-free/caged, or a Total region). Charting the total with its parts double-counts and flattens the parts — keep one or the other on a stacked/grouped/colored channel, not both.
• Units. Check whether a rate is a fraction (0–1) or already a percent (0–100) before tagging it Percentage; don't scale twice.
• One real entity. If your breakdown column has a single distinct value, the per-group chart collapses to one mark — the intended breakdown is likely a different column.

Post-Flint style customization

Stay at the Flint level for structure (data, chart type, channels, transforms, sizing, properties) — Flint specs stay portable and regenerate safely. Drop to backend JSON only after a valid Flint chart exists, and only for a narrow presentation change Flint does not expose (exact axis/legend/mark styling, titles, annotations, reference lines, layout polish). Never use it to change the data, chart type, field mappings, or transforms — fix those upstream.

For a Vega-Lite-specific style tweak:

1. Author and validate the Flint ChartAssemblyInput.
2. Render or inspect the Flint chart first, when possible.
3. Call compile_chart with backend: "vegalite".
4. Make the smallest necessary style/presentation edit to the returned Vega-Lite spec.
5. Render the edited spec in the host environment with a Vega-Lite renderer.

This edited Vega-Lite spec is no longer a portable Flint spec. Do not send it to render_chart; use render_chart only for Flint ChartAssemblyInput.

Verification is mandatory here. Once you leave the Flint level, the MCP server's validation no longer protects you — an edited spec can render a plausible-looking chart that is silently wrong. Load the render-verify skill: open the result, read its console errors, and check it against the failure catalog before declaring it done.

Attribution

Chart-selection framework (§0 below) distilled from standard visualization literature — Cole Nussbaumer Knaflic (Storytelling with Data), Andy Kirk (Data Visualisation), Stephen Few (Show Me the Numbers, Information Dashboard Design), Wexler / Shaffer / Cotgreave (Big Book of Dashboards). For per-chart design tips and the full 48-chart catalog, see The Defensible Decision chart gallery: https://www.thedefensibledecision.com/gallery/chart-gallery.html. For live examples of every Flint chartType across all backends, organized by semantic category (Bar & Column / Line & Area / Scatter & Points / Distributions / Circular & Radial / Tables & Multi-Dimensional / Maps), see the canonical Flint gallery at https://microsoft.github.io/flint-chart/#/gallery/vegalite (swap /vegalite for /echarts or /chartjs to view the other backends). flint-chart itself is a Microsoft Research + IDEAS Lab (Renmin University) project — canonical docs: https://microsoft.github.io/flint-chart/#/documentation/getting-started (getting started, API reference, architecture, chart-template extension), https://microsoft.github.io/flint-chart/#/mcp (MCP server deployment + full CLI), and https://microsoft.github.io/flint-chart/ (project home + live editor).

Step 0 — pick the chart (when the user hasn't said which one)

Skip this step if the user already named the chart type (e.g. "scatter of weight vs mpg"). Jump to Step 1 and author the spec. Otherwise, work down this list before choosing a chartType.

0.1 One-sentence message — the Big Idea

Before choosing a chart, establish the message it should carry. Load the chart-big-idea skill and run it now — look in .github/skills/local/chart-big-idea/SKILL.md first (heir-installed), then .github/skills/chart-big-idea/SKILL.md (baseline).

It does four things this step cannot do inline:

• Reads the surrounding context first — the prose next to the insertion point, the ticket, the section heading, prior captions — so you do not ask the user to re-articulate a claim they already wrote.
• Questions the intent — whether the chart should exist at all, and whether the stated purpose is the real one. If the intended message and the data disagree, that surfaces here rather than after rendering.
• Elicits the Big Idea with a three-question ladder, one question at a time, when it is not written down anywhere.
• Asks the TRADITIONAL vs INNOVATIVE style stance, which changes the chartType you pick in §0.2.

The output is a compact Chart Brief. Treat it as the constraint on everything below: §0.2 selection, §0.4 coverage, and the spec you author in Steps 1-3.

If that skill is not available, do the compact version inline (Knaflic — Storytelling with Data):

• What is your unique point of view?
• What is at stake?
• Express it as a complete sentence, not a phrase.

If you cannot write the sentence, ask the user for context before drawing. "Show sales" is a phrase; "Q4 sales dropped 18% in APAC — that's where our attention should go this quarter" is a sentence. The sentence shape drives the chart choice.

0.2 Question → family → chart

Analytical question	Family	Primary chart	Alternates
Rank or compare categories?	Comparison	Bar Chart (2-15 items; horizontal orientation for long labels)	Grouped Bar Chart (2-4 series), Stacked Bar Chart (composition + total; use stackMode: normalize for 100% stacked), Slope Chart (before/after 2 periods), Bar Chart with row/column facet (many items, aka Small Multiples), Waterfall Chart (sequential adds/subtracts)
Change over continuous time?	Trend	Line Chart	Area Chart (volume emphasis), Bar Chart + Line Chart combo via multi-encoding y: ["bars", "line"] (dual metric with different scales), Sparkline (in-table trend)
How are values distributed?	Distribution	Histogram (one variable)	Boxplot (compare groups + stats), Violin Plot (compare + shape, Vega-Lite), Strip Plot (every point matters), Density Plot (smooth shape), ECDF Plot (cumulative)
Correlation between variables?	Relationship	Scatter Plot	Scatter Plot with size channel (3 vars, aka Bubble), Regression (with fit line), Connected Scatter Plot (trajectory over time), Parallel Coordinates (many vars, ECharts)
Part of a whole?	Proportion	Bar Chart (most accurate) or Stacked Bar Chart with stackMode: normalize	Pie Chart (only if one slice dominates ≥60% OR comparing to 50%), Pie Chart with innerRadius > 0 (Donut — use center for a KPI), Treemap (many/hierarchy, ECharts), Sunburst (interactive hierarchy, ECharts), Funnel (sequential stages, ECharts)
Flow between stages?	Flow	Sankey (linear flow, ECharts)	Streamgraph (aesthetic, precision sacrificed), Heatmap (matrix pattern), Chord-like flows → use Sankey instead
Progress toward a target?	KPI	Bullet Chart (Few's superior alternative to gauges: actual + target + qualitative ranges in one horizontal bar)	KPI Card (single number with delta), Sparkline (in-table trend), Gauge (ECharts — reserve for high-visibility single-KPI tiles only)

0.3 Anti-patterns — don't recommend

• Pie with >5 slices — humans can't compare angles; use Bar Chart or Stacked Bar Chart with stackMode: normalize
• Pie without a dominant slice — if no category is ≥60% or the story isn't "X vs the rest", use a Bar Chart
• Word cloud for real analysis — position and word length distort; use Bar Chart of top-N terms (not in Flint; export to another tool)
• Dual-axis combo without justification — dual axes mislead by aligning unrelated scales; consider two separate charts
• Truncated Y-axis on bars — exaggerates differences; always start Bar Chart at zero (Flint does this by default; don't override)
• Streamgraph when precise values matter — the flowing baseline sacrifices readability; use Area Chart instead
• Gauge over Bullet — Bullet Chart packs actual + target + qualitative bands in less space with more precision
• More than 5 series on a Line Chart — becomes a spaghetti chart; use row/column facet (Small Multiples) or highlight one series and gray out the rest

0.4 Flint coverage — substitutes when the ideal chart isn't native

Some charts from wider visualization literature aren't in Flint's registry. Recommend the substitute, not the missing chart:

Ideal chart	Flint substitute	How
Waffle Chart (10×10 grid %)	Bar Chart or Stacked Bar Chart with stackMode: normalize	Labeled percentage bar communicates the same "N out of 100"
Chord Diagram (circular flows)	Sankey (ECharts backend)	Linear flow is easier to read anyway
Pareto Chart (bars + cumulative %)	Bar Chart + Line Chart via multi-encoding	Sort bars descending, overlay cumulative-% line
Beeswarm Plot (every point)	Strip Plot with stepWidth, pointSize, opacity	Jittered points instead of packed; same "every dot is real" story
Ridgeline Plot (many densities)	Violin Plot with row facet	Density curves stacked per group
Small Multiples	any chart with row or column encoding	Native facet support
Word Cloud / Sentiment / NPS Gauge / Likert / Mind Map / Hierarchy Tree	Not in Flint's scope	Export prepared data to Power BI / Tableau / dedicated tool
Control Chart / Run Chart / Pareto / Process Capability (SPC)	Not in Flint's scope	Use a dedicated SPC / Six Sigma tool; Flint isn't built for statistical process control
Decomposition Tree / Key Influencers / Smart Narrative (AI-Powered)	Not in Flint's scope	These are Power BI features; Flint is a chart compiler, not an analytics engine
Table / Matrix (precise value lookup)	Use a data table (not Flint)	Stephen Few's rule — tables for lookup, graphs for pattern

0.5 When to fetch a deep reference

Two authoritative external references, each with a distinct role. Fetch the one that matches the question:

Chart selection — "which chart for which analytical question?"

Fetch The Defensible Decision — Complete Chart Gallery when:

• The user asks about a chart not in §0.2 or §0.4
• The user asks "what other charts could work here?"
• The user needs per-chart design tips (axis handling, color, labeling, accessibility)
• The compact table above is ambiguous for the case at hand

The gallery has 48 charts across 10 families with per-chart 💡 tips, distilled from Knaflic / Kirk / Few / Wexler.

Chart capability — "does Flint render this? which backend?"

Fetch the canonical Flint gallery (maintained by the microsoft/flint-chart team; always tracks the current release) when:

• You need to confirm Flint actually renders a specific chartType on a specific backend. Swap the trailing /vegalite → /echarts or /chartjs to view the same catalog for other backends.
• The user is deciding between Vega-Lite vs ECharts vs Chart.js and wants to see the same chart family rendered natively on each backend.
• You need a live example of a chart variant (e.g. a faceted boxplot, a dodge = local grouped bar, a sparse streamgraph) — the gallery shows multiple named variants per chartType.
• You want the canonical semantic grouping (Bar & Column / Line & Area / Scatter & Points / Distributions / Circular & Radial / Tables & Multi-Dimensional / Maps) that Flint itself uses to organize its chart registry.

This is the authoritative reference for what Flint actually does; §0.2–0.4 above is the compact map, but the gallery is the source of truth for edge cases and backend-specific behavior.

Rule of thumb: Defensible Decision answers "should I use a bar or a boxplot?"; the Flint gallery answers "will Flint's Bar Chart on ECharts backend do what I need?"

0.6 Design principles (invoke, don't substitute for reading)

Short-form pointers to the underlying literature. Invoke these when justifying a choice; read the books themselves for depth.

• Trustworthy · Accessible · Elegant (Kirk) — check the chart against all three before shipping
• Tables for lookup, graphs for pattern (Few) — if the user wants exact values, a data table beats any chart; use Bar Table (Flint) only when you want compact bars with labels
• Explanatory vs exploratory (Knaflic) — for stakeholder communication, show the pearl, not the oyster bed; strip clutter aggressively
• Bullet > Gauge (Few) — always prefer Bullet Chart for KPI-vs-target; reserve Gauge for large single-KPI tiles
• Gestalt (Knaflic Ch. 3) — group with proximity, distinguish with color/shape, connect with lines, enclose with backgrounds
• Dashboard = one screen, no scrolling, reduce to essence (Few — Information Dashboard Design) — if it doesn't fit, cut, don't scroll

---

Step 1 — pick chartType

Use one of the registered names exactly. Vega-Lite is the default and broadest backend; the table below lists each Vega-Lite chart type, the channels it accepts, and its tuning properties (see "Chart-level properties"). Required channels are noted.

chartType	Channels	Notes / required
"Scatter Plot"	x, y, color, size, opacity, column, row	x + y required
"Regression"	x, y, size, color, column, row	scatter + fit line; props regressionMethod, polyOrder
"Connected Scatter Plot"	x, y, order, color, detail, column, row	x + y required; order = connection sequence (time/index), so the line traces a trajectory and may self-cross
"Ranged Dot Plot"	x, y, color	dumbbell of two x per category
"Strip Plot"	x, y, color, size, column, row	jittered points; props stepWidth, pointSize, opacity
"Bar Chart"	x, y, color, opacity, column, row	one discrete + one measure; prop cornerRadius
"Grouped Bar Chart"	x, y, group, column, row	group = the clustering category; prop dodge
"Stacked Bar Chart"	x, y, color, column, row	prop stackMode
"Pyramid Chart"	x, y, color	diverging horizontal bars
"Lollipop Chart"	x, y, color, column, row	prop dotSize
"Waterfall Chart"	x, y, color, column, row	color = Type column, values start/delta/end only; omit it for auto sign coloring; props cornerRadius, totals
"Gantt Chart"	y, x, x2, color, detail, column, row	x = start, x2 = end
"Bullet Chart"	y, x, goal, color, column, row	goal required (target)
"Histogram"	x, color, column, row	x = measure to bin; prop binCount
"Boxplot"	x, y, color, opacity, column, row	category + measure; props whiskerMethod, showOutliers, dodge
"ECDF Plot"	x, color, detail, column, row	x = measure; cumulative distribution (step line); prop showPoints
"Heatmap"	x, y, color, column, row	color = the measure
"Line Chart"	x, y, color, strokeDash, detail, opacity, column, row	props interpolate, showPoints
"Sparkline"	x, y, color, detail, row, column	x + y required; small-multiple mini trend lines, one per series (series from color or detail); props interpolate, baseline, trendWidth
"Bump Chart"	x, y, color, detail, column, row	rank-over-time lines
"Slope Chart"	x, y, color, detail, column, row	two-period value change; straight segments + end points, one line per category
"Area Chart"	x, y, color, opacity, column, row	props interpolate, opacity, stackMode
"Range Area Chart"	x, y, y2, color, column, row	x + y + y2 required; translucent band from y (low) to y2 (high), value axis fits the band (not zero)
"Violin Plot"	x, y, color, row	x (category) + y (measure) required; mirrored KDE density per category, prop bandwidth; Vega-Lite only; a genuine color subgroup splits two groups or grids 3+ groups
"Streamgraph"	x, y, color, column, row	center-stacked areas
"Density Plot"	x, color, column, row	prop bandwidth
"Pie Chart"	size, color, column, row	size = slice value (→ angle), color = category; props innerRadius, sortSlices
"Rose Chart"	x, y, color, column, row	polar bars; props alignment, padAngle, sortSlices
"Radar Chart"	x, y, color, column, row	props filled, fillOpacity, strokeWidth
"Candlestick Chart"	x, open, high, low, close, column, row	OHLC all required
"Bar Table"	y, x, color, column, row	compact bars + value labels
"KPI Card"	metric, value, goal	big-number tile; prop behindThreshold
"Map"	longitude, latitude, color, size, opacity	bubble map; props region, projection
"Choropleth"	id, color, detail	id = geographic key

Donut chart: use "Pie Chart" with chartProperties.innerRadius > 0.

Choosing a bar chart (most common mix-up). All three take one discrete category on x (or y) plus one measure. They differ in how a second category is shown — and each reads that second category from a different channel:

• "Bar Chart" — use for a single series. When multiple rows share an x, a second category on color produces stacked segments. For side-by-side bars, use "Grouped Bar Chart" with the second category on group.
• "Stacked Bar Chart" — second category on color, drawn as stacked segments within each bar (totals matter). Tune with stackMode (stacked / normalize / layered).
• "Grouped Bar Chart" — second category on the group channel, drawn as side-by-side (dodged) bars within each x cluster (compare values directly). Put the clustering category on group, not color.

Rule of thumb: comparing parts-to-whole → Stacked; comparing values side-by-side → Grouped (use group); single series → Bar.

Waterfall color is a special "Type" column, not a free category. On a "Waterfall Chart" the color channel is reserved for a type field whose values are literally start, delta, and end — it drives which bars anchor to zero, not an arbitrary grouping. Do not bind color to an Increase/Decrease (or up/down, gain/loss) category: the up/down direction is already derived from the sign of the y value and colored automatically (green up / red down). For the common case, omit color entirely and let Flint infer the start/delta/end and per-bar sign coloring. To force which bars are anchored totals, use the totals property (first/last/both), not a color field. Only bind color when you genuinely have a start/delta/end type column.

Backend coverage. Vega-Lite supports all of the above. Other backends support a subset (verify if targeting a non-VL backend):

• ECharts adds: "Calendar Heatmap", "Gauge", "Funnel", "Treemap", "Sunburst", "Sankey", "Parallel Coordinates", "Graph", "Tree".
• Chart.js supports: Scatter, Bubble, Bar, Grouped Bar, Stacked Bar, Combo, Line, Area, Range Area, Pie, Doughnut, Histogram, Radar, Rose, Slope, Connected Scatter.

You do not need to call the library or inspect its source to author the input — pick from this table.

Step 2 — map fields to channels

Each channel maps to an encoding object { field, ... } (or a bare string shorthand, expanded to { field: "<string>" }):

"encodings": {
  "x": { "field": "weight" },
  "y": "mpg",
  "color": { "field": "origin" }
}

Encoding object fields (all optional except field):

Field	Values	Purpose
field	column name	Bind the channel to a data column
type	quantitative, nominal, ordinal, temporal	Override the inferred encoding type (rarely needed)
aggregate	count, sum, average, mean	Force an aggregation on a measure channel
sortOrder	ascending, descending	Sort direction for a discrete/sorted axis
sortBy	channel name (e.g. "y") or field	Sort a category axis by another channel's measure
scheme	Vega scheme name (e.g. viridis, redblue)	Color scheme for the color channel

You usually don't need type, aggregate, or sortOrder — they're inferred from the semantic type. Set them only with specific intent.

Multi-series (wide → long). To plot several measure columns as series, pass an array on x or y (only those two channels). The library folds them into long form and synthesizes a series/legend field:

"encodings": { "x": { "field": "month" }, "y": ["sales", "profit"] }

All array fields must be quantitative, and you cannot also bind color when using the array form (the fold owns the color/legend). This is the only built-in reshape — there is no transforms/fold property. For any other shape (long↔wide, an aggregate the encodings can't express, a derived column, a pivot, a join), reshape the data first with a host tool — pandas/polars, Arquero/Array.map/SQL, or a data/MCP tool — and pass the result as data.values. If you have no way to transform, surface the gap to the developer rather than inventing a transform property that does not exist.

Step 3 — annotate with semantic types

This is the most important step. Semantic types drive all downstream decisions — formatting, zero baseline, color scheme, scale direction, and more. Pick the most specific type for each field. Full registered set:

Family	Semantic types
Temporal (point)	DateTime, Date, Time, Timestamp
Temporal (granule)	Year, Quarter, Month, Week, Day, Hour, YearMonth, YearQuarter, YearWeek, Decade
Temporal (span)	Duration
Measure (amount)	Amount, Price, Quantity, Count, Number
Measure (proportion)	Percentage
Measure (signed/diverging)	Profit, PercentageChange, Sentiment, Correlation
Measure (physical)	Temperature
Discrete / rank	Rank, Score, ID
Geographic (coord)	Latitude, Longitude
Geographic (place)	Country, State, City, Region, Address, ZipCode
Categorical	Category, Name, Status, Boolean, Direction, Range
Fallback	Unknown

What choosing well gets you (automatically):

• Price / Amount → currency formatting, zero baseline, sequential color
• Temperature → diverging color scheme, no forced zero baseline
• Correlation → fixed [-1, 1] diverging domain
• Rank → reversed axis (1 on top), discrete color
• Date / DateTime → temporal axis with auto-granularity formatting
• Percentage → percent formatting, 0–100 domain awareness

If you don't know, use Quantity for numbers, Category for strings, Date/DateTime for date-shaped values. Do not invent type names.

Chart-level properties (chartProperties)

chartProperties is an optional per-chart tuning map. Set a property only when the user asks for that behavior — defaults are sensible. These are design choices, not styling overrides (colors/fonts/ticks are still derived). Values are clamped to the ranges shown.

Chart type	Property	Type / range (default)	Effect
Bar Chart	cornerRadius	0–15 (0)	Round bar corners (px)
Area / Stacked Bar	stackMode	stacked | normalize | center | layered (unset)	Stacking behavior; normalize = 100%, center = streamgraph
Grouped Bar / Boxplot	dodge	auto | local | global (auto)	local compacts sparse groups per category; global preserves aligned group lanes; leave auto unless the user requests one
Line / Area / Sparkline	interpolate	linear | monotone | step | step-before | step-after | basis | cardinal | catmull-rom (linear)	Curve shape
Line / ECDF Plot	showPoints	boolean (false)	Draw point markers on the line
Sparkline	baseline	mean | zero | median | none (mean)	Reference line per spark row
Sparkline	trendWidth	80–600 (240)	Mini line-plot width (px)
Boxplot	whiskerMethod	iqr | minmax (iqr)	Whisker rule (Tukey 1.5×IQR vs min–max)
Boxplot	showOutliers	boolean (true)	Show outlier points (Tukey only)
Area	opacity	0.1–1 (0.7)	Fill opacity
Scatter	opacity	0.1–1 (1)	Point opacity
Strip Plot	stepWidth	10–100 (20)	Jitter spread
Strip Plot	pointSize	0–150 (0=auto)	Point size
Strip Plot	opacity	0–1 (0=auto)	Point opacity
Histogram	binCount	5–50 (10)	Number of bins
Density Plot	bandwidth	0.05–2 (0=auto)	Kernel bandwidth
Pie Chart	innerRadius	0–100 (0)	Donut hole size (>0 → donut)
Pie / Rose	sortSlices	none | descending | ascending (none)	Order wedges and their legend by slice value
Rose Chart	alignment	left | center (left)	Wedge alignment
Rose Chart	padAngle	0–0.1 (0)	Gap between slices
Lollipop	dotSize	20–300 (80)	Circle size (px)
Waterfall	cornerRadius	0–8 (0)	Round bar corners
Waterfall	totals	auto | none | first | last | both (auto)	Which bars anchor to zero as totals (only when no Type column)
Waterfall	showTextLabels	boolean (false)	Render value labels on bars
Regression	regressionMethod	linear | log | exp | pow | quad | poly (linear)	Fit method
Regression	polyOrder	1–5 (3)	Polynomial order (when poly)
Radar	filled	boolean (true)	Fill the polygon
Radar	fillOpacity	0–0.5 (0.15)	Polygon fill opacity
Radar	strokeWidth	0.5–4 (1.5)	Line width
KPI Card	behindThreshold	0–1 (0.5)	Value/goal ratio cutoff for color
Map	region	us | world | auto (auto)	Geographic scope
Map	projection	mercator | equalEarth | orthographic | stereographic | conic | mollweide	Map projection

Cross-cutting properties (apply to position/faceted charts when relevant; set only to force non-default behavior):

• independentYAxis (boolean) — faceted charts: give each panel its own y-scale.
• logScale_x / logScale_y (boolean) — force a logarithmic axis.
• includeZero_x / includeZero_y (boolean) — force the axis to include 0.
• xAxisType / yAxisType (temporal | nominal) — force a temporal field to render as discrete bands (or vice-versa).

Parameter overrides — when to reach for them

Overrides exist, but prefer letting semantic types drive decisions. Reach for an override only when the user's intent genuinely conflicts with the default:

• Force an aggregation: encodings.y = { field: "sales", aggregate: "sum" }.
• Sort a category axis by its measure: encodings.x = { field: "name", sortBy: "y", sortOrder: "descending" }.
• Pick a color scheme: encodings.color = { field: "region", scheme: "tableau10" }.
• Override an inferred type: encodings.x = { field: "year", type: "ordinal" } (e.g. treat a year as discrete bands).
• Resize the chart: Flint sizes from two numbers — baseSize (the target it aims for, default 400×320) and canvasSize (a hard ceiling it may never exceed). With dense data the chart stretches from base toward the ceiling.
  • Want a comfortable size that may grow for dense data → set chart_spec.baseSize = { width, height }.
  • Want a fixed slot it must fit inside → set chart_spec.canvasSize = { width, height } alone; the chart fills it and shrinks to fit, never overflowing. What you ask for is what you get.
  • Both → aims for baseSize, grows toward canvasSize, never beyond.
• Force log / zero baseline: the logScale_* / includeZero_* chart properties above.

Global layout tuning lives in the top-level options object (e.g. addTooltips, band padding, facet sizing). It is rarely needed for authoring — omit it unless asked.

Worked examples

In each example data is a placeholder — the host binds real rows or a URL. You author only chart_spec and semantic_types.

Scatter plot

User: "Plot car weight vs fuel economy, colored by origin."

{
  "data": { "values": [] },
  "semantic_types": {
    "weight": "Quantity",
    "mpg": "Quantity",
    "origin": "Country"
  },
  "chart_spec": {
    "chartType": "Scatter Plot",
    "encodings": {
      "x": { "field": "weight" },
      "y": { "field": "mpg" },
      "color": { "field": "origin" }
    },
    "baseSize": { "width": 400, "height": 300 }
  }
}

Revenue bar chart with facets, sorted by value

User: "Show revenue by product line, biggest first, one panel per region."

{
  "data": { "values": [] },
  "semantic_types": {
    "product_line": "Category",
    "revenue": "Amount",
    "region": "Region"
  },
  "chart_spec": {
    "chartType": "Bar Chart",
    "encodings": {
      "x": {
        "field": "product_line",
        "sortBy": "y",
        "sortOrder": "descending"
      },
      "y": { "field": "revenue" },
      "column": { "field": "region" }
    }
  }
}

Time series, multiple series (wide → long via array)

User: "Line chart of monthly sales and profit."

{
  "data": { "values": [] },
  "semantic_types": {
    "month": "YearMonth",
    "sales": "Amount",
    "profit": "Profit"
  },
  "chart_spec": {
    "chartType": "Line Chart",
    "encodings": {
      "x": { "field": "month" },
      "y": ["sales", "profit"]
    },
    "chartProperties": { "interpolate": "monotone", "showPoints": true }
  }
}

Donut chart (Pie + innerRadius), value on size

User: "Show market share by vendor as a donut."

Pie/donut maps the slice value to size (rendered as angle) and the category to color. Data is already long (one row per vendor).

{
  "data": { "values": [] },
  "semantic_types": {
    "vendor": "Category",
    "share": "Percentage"
  },
  "chart_spec": {
    "chartType": "Pie Chart",
    "encodings": {
      "size": { "field": "share" },
      "color": { "field": "vendor" }
    },
    "chartProperties": { "innerRadius": 60 }
  }
}

Bullet chart (KPI vs target)

User: "Show each rep's sales against their quota."

{
  "data": { "values": [] },
  "semantic_types": {
    "rep": "Name",
    "sales": "Amount",
    "quota": "Amount"
  },
  "chart_spec": {
    "chartType": "Bullet Chart",
    "encodings": {
      "y": { "field": "rep" },
      "x": { "field": "sales" },
      "goal": { "field": "quota" }
    }
  }
}

What you should NOT do

• Don't re-emit the data. Reference columns by name; let the host bind data (url, variable, or small literal). Never paste large datasets.
• Don't write backend specs directly — write the ChartAssemblyInput, then call the assembler. That's the whole point.
• Don't invent transforms. The only built-in reshape is the array form on x/y. If the data shape is wrong for the chart, say so and ask the host to reshape it.
• Don't invent field names. Reference only columns that exist, spelled exactly. If the data is the wrong shape for the chart, reshape it upstream rather than guessing column names that aren't there.
• Don't set type/aggregate/sortOrder unless intent conflicts with the default.
• Don't pass colors, font sizes, axis tick counts — the compiler derives these. Users fine-tune the output spec.
• Don't invent semantic type names. If none fit, use the family default (Quantity, Category, Date).
• Don't call the library to discover channels/types — this document is the authoring reference.

Validation checklist

Before returning, verify:

1. chartType is an exact registered name supported by the target backend.
2. Every field referenced in encodings is a real column name.
3. Every encoded field has an entry in semantic_types (specific type).
4. Required channels for the chart type are present (e.g. Bullet→goal, Candlestick→open/high/low/close, Pie→size+color).
5. Any chartProperties keys are valid for that chart type and in range.
6. You did not inline large data or hand-tune derived styling.
7. The data carries no embedded total/subtotal level (e.g. an all / total row) mixed with its components on a stacked, grouped, or colored channel.

Would Revise If

Revise this skill by 2026-10-22 (90 days) or sooner if any of the following fires:

• The Defensible Decision chart gallery 404s or restructures such that the §0.5 deep-reference URLs no longer resolve — refresh the escalation targets or fold the needed tips into §0 directly.
• flint-chart-mcp ships a breaking change (chart-type rename, ChartAssemblyInput shape change, tool signature change) that this skill doesn't account for — sync §0.4 (Flint coverage) and the worked examples to the new version.
• Any recommendation in §0.2 (question → family → chartType) is refuted by a source we trust (a case study, a Knaflic/Kirk/Few/Wexler update, or field feedback from ≥2 heir workspaces) — retire or rework that row.
• The plugin gets ≥3 heir installs and none of them exercise §0.5 (deep-reference escalation) — that signals the compact table alone is sufficient and §0.5 is decorative; prune it.
• The upstream fork base (microsoft/flint-chart/agent-skills/flint-chart-author/SKILL.md) publishes a materially revised body — decide whether to rebase §1-N onto the new upstream or hold on the current fork point.
• Upstream absorbs chart selection itself. flint-chart 0.3.0 shipped backend-neutral chart-type recommendations and transformations — the capability §0 exists to provide. If those recommendations become reachable through the MCP tools and match or beat §0.2 on the same question, §0 is redundant: call the upstream recommender and keep only the framing this plugin adds on top. Re-test when the pin moves past 0.2.2.
• The backend list changes. §0.4 and the worked examples assume Vega-Lite / ECharts / Chart.js. 0.4.0 adds Plotly (38 chart types, including Funnel, Gauge, and Density Contour) and Excel (18 native Office.js templates), which expands what "Flint can't express this" means. Re-check the coverage rules whenever list_chart_types reports a backend this skill does not name.