| name | semiotic-charts |
| description | Generate trustworthy, accessible, agent-legible charts with Semiotic. Use whenever you produce a data visualization in a React/TypeScript app, stream a chart as generative UI, or pick a chart type for a dataset. Emit a validated config and run the trust loop — never hand-write chart JSX that breaks on first paint. |
Generating charts with Semiotic
Semiotic is a React data-visualization library whose differentiator is trust:
generation is cheap, but a chart that renders, that a screen-reader user can
receive, and that carries its own provenance is scarce. This skill is the
workflow for producing one.
The cardinal rule: do not hand-write chart JSX and hope it paints. Emit a
{ component, props } proposal and run it through the trust loop, which is
validated and diagnosed; when a renderer is available, proven to paint. Otherwise it returns reasons and ranked alternatives to retry with.
The trust loop — generate → validate → diagnose → repair → prove
prepareChart (from semiotic/ai) composes the whole loop. Call it on every
proposal before you show or stream a chart:
import { prepareChart } from "semiotic/ai"
const result = prepareChart(
{ component: "BarChart", props: { data, categoryAccessor: "region", valueAccessor: "revenue" } },
{ data }
)
if (result.ok) {
} else {
}
result carries { ok, config, jsx, validation, diagnostics, repair?, reasons }.
In a server/SSR context you can inject render: renderChartWithEvidence (from
semiotic/server) so the loop also proves the scene is non-empty and reads back
render evidence (mark count, domains, ARIA label) — the first-try oracle.
As an agent tool
chartGenerationTool() returns a framework-agnostic JSON-Schema tool definition;
toAnthropicTool, toOpenAITool (Chat Completions), and
toOpenAIResponsesTool (Responses API) shape it for provider APIs. Vercel AI SDK
and LangChain accept the same JSON Schema. createChartToolHandler(optionsFor) is
the execute step. No vendor SDK is required. For backend-only use, import these
helpers from semiotic/ai/core to avoid the chart-HOC catalog.
Picking a chart for a dataset
When you don't know which chart fits, ask the data, not your priors:
import { suggestCharts } from "semiotic/ai"
const ranked = suggestCharts(data, { intent: "trend", maxResults: 3, audience })
intent is one of: trend, compare-series, compare-categories, rank,
part-to-whole, distribution, correlation, flow, hierarchy, geo,
outlier-detection, composition-over-time, change-detection.
Hard rules (the behavior contracts)
These are enforced by validation and the npx semiotic-ai --doctor gate. Honor
them in every proposal:
- Sub-path imports. Import from the family entry point, not the barrel:
semiotic/xy, semiotic/ordinal, semiotic/network, semiotic/geo,
semiotic/realtime, semiotic/ai. The full semiotic is large; a family
entry point is a fraction of it.
- Static usage requires data in props.
renderChart, SSR snapshots, and any
copy-paste example need data (or nodes/edges) present.
- Push (live) mode omits
data entirely. Create a ref, do NOT pass
data={[]} (that clears the chart on every render), then call
ref.current.push(row) / pushMany(rows). remove(id) / update(id, fn)
require a stable id accessor (pointIdAccessor for XY, dataIdAccessor for
ordinal, nodeIDAccessor/edgeIdAccessor for network).
- Required prop combinations. Beyond data, some families need a semantic
prop, in static and push mode: StackedAreaChart→
areaBy,
StackedBarChart→stackBy, GroupedBarChart→groupBy, BubbleChart→sizeBy,
SwimlaneChart→subcategoryAccessor, GaugeChart→value (value-only, no push),
ForceDirectedGraph→materialized nodes + edges (don't infer nodes from edge
endpoints).
- Categorical color via
colorBy (a field name), shared across charts with
CategoryColorProvider / LinkedCharts; fall back to colorScheme. Don't
reach for frameProps style functions to color by category.
renderChart (MCP / semiotic/server) is a single static snapshot. It
can't push later. For live behavior, return React code with a ref.
What good output looks like
import { LineChart } from "semiotic/xy"
<LineChart
data={series}
xAccessor="date"
yAccessor="value"
xScaleType="time"
title="Weekly active users"
showPoints
/>
Annotations carry provenance and lifecycle — when you mark a point, say who/why:
import { withProvenance } from "semiotic/ai"
const note = withProvenance(
{ type: "callout", x: "2026-W14", y: 9, label: "Deploy-correlated spike" },
{ provenance: { authorKind: "agent", basis: "statistical-test", confidence: 0.78 },
lifecycle: { ttlHint: "P7D", status: "proposed" } }
)
Tooling
- MCP server:
npx semiotic-mcp — tools for renderChart (SVG + render
evidence), suggestCharts, groundChart, diagnoseConfig, repairChartConfig,
proposeChartVariants, and more. Prefer these over guessing.
- Public app profile:
npx semiotic-mcp --profile public exposes the five
task-oriented tools createChart, improveChart, explainChart,
auditChart, and getChartSchema; use it when tool discovery matters more
than expert-level control.
- CLI gate:
npx semiotic-ai --doctor validates a { component, props } JSON
(--audit-a11y for an accessibility audit). Run it before shipping generated code.
- Machine-readable docs: the published
llms.txt is the chart catalog with
per-chart communicative-act labels; read it for the full surface rather than
guessing component names.
- Portable install:
npx semiotic-ai --skill prints this packaged skill so a
compatible agent host can install it at its documented skill location. The
npm package includes agent-skill/semiotic-charts/SKILL.md for offline use.
Don't
- Don't hand-write chart JSX without running
prepareChart or --doctor.
- Don't import charts from the bare
semiotic barrel in production code.
- Don't pass
data={[]} for live charts (use push mode — omit data).
- Don't promise live/interactive behavior from
renderChart — it's a static snapshot.
- Don't invent a component name; if no chart fits, say so and surface alternatives
(
suggestCharts / repairChartConfig) — a wrong chart deceives the reader who
can least afford it.