# Paperchart > Generate clean, calm chart PNGs in the spirit of Anthropic and OpenAI blog posts. Trigger when the user asks to produce a chart, model comparison table, diagram or infographic for a technical blog post, release notes, a research write-up or a slide deck and wants a single restrained palette, minimal chrome, heavy inline labelling, and PNG output rendered from React at native resolution. The primary surface is a shell CLI named `paperchart` that takes a JSON file and writes a PNG. Thirty-two primitives covering charts (line, area, scatter, heatmap, calendar heatmap, histogram, cdf, box-plot, ranking, dumbbell, slope, waterfall, small-multiples, timeline, funnel, sankey, treemap, radar, table, stacked-bar, grouped-bar, critical-path, pack-layout, latency, bytes, recall) and Mermaid-style structural diagrams (flowchart, architecture, sequence, state-diagram, er-diagram, delivery). Six themes, layout + style overrides per chart. - Skill: `shuakami/paperchart` (Agent Skill, multi-file: 15 files) - Install (CLI): `npx skillmds@latest add shuakami/paperchart` - Raw SKILL.md: https://api.skillmd.com/api/skills/shuakami/paperchart/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Web & Frontend - License: MIT - Author: shuakami (https://skillmd.com/u/shuakami) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/shuakami/paperchart --- # paperchart A six-primitive chart toolkit for rendering quiet, print-weight charts to PNG. The surface is a shell CLI: feed it a JSON file, get a 2×-DPR figure. Trigger on any of: "make a chart for this blog post", "render a before / after benchmark as an image", "I need a figure for my paper / release notes / announcement", "design a minimal chart in the style of Anthropic or OpenAI research posts", "draw a binary layout / critical path / latency comparison / recall parity chart". Do **not** use this skill when the user asks for an interactive dashboard, a real-time graph, or a chart with brand colours other than the paperchart palette. This skill is opinionated on purpose. ## The CLI ```bash paperchart -i data.json -o out.png [--width 1600] [--dpr 2] paperchart --defaults -o out.png # render the built-in sample ``` Types: `table`, `latency`, `bytes`, `stacked-bar`, `grouped-bar`, `funnel`, `ranking`, `dumbbell`, `slope`, `line`, `area`, `small-multiples`, `timeline`, `scatter`, `heatmap`, `calendar-heatmap`, `histogram`, `box-plot`, `cdf`, `radar`, `treemap`, `sankey`, `waterfall`, `critical-path`, `recall`, `pack-layout`, `delivery`, `flowchart`, `architecture`, `sequence`, `state-diagram`, `er-diagram`. Themes: `paper` (warm off-white, rust), `ink` (near-white, black), `slate` (cool grey, deep blue), `forest` (off-white, forest green), `mono` (pure monochrome), `dusk` (dark charcoal, warm orange). Pass `--theme ` on the CLI or put `"theme": ""` in the JSON envelope. Invoke it with `npx github:shuakami/paperchart -i data.json -o out.png` from any directory. On first run npm clones the repo, runs `prepare` to build the Vite bundle, and `playwright-chromium`'s postinstall downloads headless Chromium once (~200 MB). Subsequent runs are near-instant. If the skill has already been installed via `npx skills add shuakami/paperchart`, the full repo is available under `.claude/skills/paperchart/` (or `.agents/skills/paperchart/` on non-Claude agents). You can also just `cd` there and `node bin/paperchart.mjs -i data.json -o out.png`. ## Authoring recipe When the user asks for a chart: 1. Pick the primitive whose shape fits the data. One primitive per figure. If none fit, decline and explain why — do not invent a new chart kind inline. 2. Write the JSON file with the shape documented below for that primitive. Keep captions short — 6 to 12 words. Use the accent colour on **exactly one** row / segment / panel. Leave the rest neutral. 3. Run the CLI. Show the user the output path. If they want to iterate, edit the JSON and re-run — the file is the single source of truth. 4. Never embed chart titles inside the chart. Titles belong in the blog prose next to the image. ## The input envelope Every chart accepts the same shape: ```json { "theme": "ink", "layout": { "width": 1800, "fontScale": 1.05, "xAxisCaption": "..." }, "style": { "accent": "#1f6feb" }, "data": } ``` `theme`, `layout`, and `style` are optional. If you pass just `data` (or wrap your data as a bare array), the chart uses good defaults. ## Primitive schemas Short form. Full schemas are in the project README and in the per-type `skills//SKILL.md` files. - `table` — `{ columns: [{ key, label, align?, unit?, group? }], rows: [{ label, caption?, highlight?, values: { [key]: number | string } }] }`. Model / config / product comparison. Exactly one `highlight: true` row. - `latency` — `Row[]`. Each row: `{ group, caption, color, bars: [{ level, ms }] }`. - `bytes` — `Row[]`. Each row: `{ group, caption, accent, segments: [{ kb, fill, tag }], firstLoadKB, deferredKB }`. - `stacked-bar` — `Row[]` where each row is `{ label, caption?, segments: [{ key, value, color? }] }`. Composition per row. - `grouped-bar` — `{ series: [{ key, label, color? }], groups: [{ label, caption?, values: { [key]: number } }] }`. - `ranking` — `Row[]`. Each row: `{ label, caption?, value, accent? }`. Sorted leaderboard, one accented row. - `dumbbell` — `Row[]`. Each row: `{ label, before, after }`. - `slope` — `{ startLabel, endLabel, unit?, series: [{ label, start, end, accent? }] }`. - `line` — `{ xLabels, series: [{ label, values, accent? }] }`. - `area` — `{ xLabels, series: [{ label, values }] }` (stacked). - `small-multiples` — `{ xLabels?, unit?, panels: [{ label, caption?, values, accent? }] }`. - `scatter` — `Point[]` with optional regression line. - `heatmap` — `{ rowLabels, colLabels, values: number[][] }`. - `histogram` — `{ bins: [{ x0, x1, count }] }`. - `cdf` — `{ points: [{ value, cumulative }] }`. - `waterfall` — `{ steps: [{ label, delta, subtotal? }] }`. - `timeline` — `Row[]` where each row is `{ label, caption?, spans: [{ start, end, label, accent? }], milestones?: [{ at, label }] }`. - `funnel` — `Stage[]`. Each stage: `{ label, caption?, count, accent? }`. Drop-off auto-computed between stages. - `sankey` — `{ sources: Node[], targets: Node[], flows: [{ from, to, value }] }`. Two-column flow diagram. - `treemap` — `Item[]` where each item is `{ label, value, caption?, accent?, children?: [{ label, value }] }`. Two-level squarified. - `radar` — `{ axes: [{ key, label, caption?, max? }], series: [{ label, caption?, accent?, values: { [axisKey]: number } }] }`. - `box-plot` — `Row[]`. Each row: `{ label, caption?, min, q1, median, q3, max, outliers?, accent? }`. - `calendar-heatmap` — `{ start?: "YYYY-MM-DD", days: [{ date, value }] }` or just `[{ date, value }]`. - `critical-path` — `Row[]`. Each row: `{ label, detail, startMs, endMs, kb, critical: boolean }`. - `recall` — `Query[]`. Each query: `{ query, hits, sets: "equal" }`. - `pack-layout` — `Segment[]`. Each segment: `{ label, detail, bytes, accent: boolean }`. - `delivery` — `{ header, subheader, panels: PanelSpec[] }` with up to 3 panels. - `flowchart` — `{ direction?: "TD" | "LR", nodes: [{ id, label, caption?, shape?: "rect" | "round" | "stadium" | "diamond" | "cylinder", accent? }], edges: [{ from, to, label?, dashed?, accent? }] }`. Layered DAG, back-edges detected and drawn as loop-backs. - `architecture` — `{ groups: [{ id, label, caption?, services: [{ id, label, caption?, accent? }] }], connections: [{ from, to, label?, dashed?, accent?, bidirectional? }] }`. Groups stack left-to-right; services stack top-to-bottom inside each group. - `sequence` — `{ actors: [{ id, label, caption? }], steps: [{ kind: "msg", from, to, label?, reply?, async?, accent? } | { kind: "note", over: id | [id, id], text }] }`. Time flows downward. - `state-diagram` — `{ direction?: "TD" | "LR", states: [{ id, label, caption?, start?, end?, accent? }], transitions: [{ from, to, label?, dashed?, accent? }] }`. Start / end pseudostates render as • / ⦾. - `er-diagram` — `{ entities: [{ id, label, caption?, accent?, fields: [{ name, type?, pk?, fk?, note? }] }], relationships: [{ from, to, fromCard, toCard, label?, accent? }] }` where cardinality codes are `"1"`, `"0..1"`, `"M"`, `"1..M"`, `"0..M"`. ## The palette — five values only | role | hex | | --- | --- | | page / card background | `#F6F1EA` | | accent (one element per chart) | `#C75F3C` | | secondary neutral | `#D6B99B` | | ink | `#2B2A27` | | rule / hairline | `#E6DCCE` | No gradients, no shadows, no rounded corners, no emojis. ## Typography Inter, weights 400 / 500 / 600, sizes 13 to 26 px. Tabular numerals for any numeric value. Group title: 22 px / 600. Caption: 14 px / 400 at 62 % opacity. Axis caption: 15 px / 400 at 62 % opacity. Inline value: 16–17 px / 500. ## Composition rules 1. **No chart titles.** The blog prose explains what the chart shows. 2. **Inline labels over legends.** Put axis names on the axis, group names next to groups, values next to bars. 3. **Single accent.** Only one colour stands out. 4. **Generous whitespace.** Give every row at least 200 px vertical room if it has a caption beneath the group name. 5. **Native-size PNG.** Never ask the user to scale the chart down in the final layout — fonts get compressed horizontally and everything looks cheap. ## Deeper customisation If the user needs something the CLI does not cover (different sizes, extra annotations, a different accent placement), point them at the repository. Every chart lives in its own file under `src/charts/` with the default data array at the top; edit, save, run `npm run snap`. ## Repository `https://github.com/shuakami/paperchart`