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Eval Graphics

CommunityPopular
elvisun
eval-graphics

Turn an eval study's numbers into on-brand, publish-ready figures using the Newsjack chart room (the eval design system), then validate them with Playwright. For producing the charts in a published eval/data study.

Overview

Publisherelvisun
Repositorynewsjack
Skill nameeval-graphics
Stars
1.5K
Forks
151
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by elvisun on GitHub. Read the source before you install it.

Installation

Install the Eval Graphics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/elvisun/newsjack/tree/main/eval/design-system
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/elvisun/newsjack.git /tmp/newsjack
mkdir -p .claude/skills
cp -r /tmp/newsjack/eval/design-system .claude/skills/eval-graphics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Eval Graphics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Eval Graphics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Eval Graphics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Eval Graphics — the chart room

Internal tooling, not a product skill. This lives in eval/design-system/ and is used by maintainers to publish eval studies. It is not a Newsjack user skill, must never be installed into skills/, and is never loaded at product runtime. Do not confuse it with the main skills folder.

You produce figures for an eval study: standalone HTML that renders the Newsjack house chart style, validated by Playwright and screenshotted to PNGs you drop into a writeup. One grammar — newsprint paper, ink, a single vermilion mark — across every figure, so a study reads like one publication.

Files you work with

  • assets/colors_and_type.css — design tokens (palette, type, spacing). Never edit; always link.
  • charts.css — the chart primitives (.bar-primary, .line-base, .fig, .stat, heatmap classes, masthead/section/colophon scaffold). Never edit; always link.
  • chart-room.html — the specimen gallery of all 9 figure types with placeholder data. This is your copy-paste source: find the figure that fits your data, lift its block, swap the geometry and labels.
  • scripts/validate.mjs — Playwright validator + screenshotter.

The grammar (non-negotiable brand rules)

These come straight from the design system. Breaking one means the figure is off-brand:

  • One chroma. Vermilion #E05A47 is the only colour. The highlighted / winning series is accent; every other series is quiet grey (--c-base, #E4E0D8) or an ink wash. If you reach for a second colour, you've gone wrong. No gradients. No emoji. Ever.
  • Paper, not white. Background is --nj-page #F9F8F6. Borders are the hairline rgba(26,26,26,0.10). Cards are 0-radius with a faint editorial shadow.
  • Type roles. Figure titles: Newsreader italic. Axis ticks, value labels, legends, eyebrows: IBM Plex Mono, ALL CAPS, ≥0.12em tracking. Descriptive captions: DM Sans.
  • Value labels float above the bar (mono, centered over the column).
  • Headline figures are one consistent colour — the whole numeral and its symbol in the accent (e.g. +38%, 4.2× fully vermilion), via <span class="accent">.
  • Section pattern: top hairline → mono number + lowercase-italic title (left)
    • mono subtitle (right) → content.

Accent strategy — set on <body>

data-accentUse
winner (default)best/highlighted series = accent, others grey. The standard comparison look.
singlecomparisons go all-grey; accent is reserved for one hero mark (a donut, one bar).
monoeverything ink, no chroma — for a sober, neutral data study.

Also data-grid="on|off" (gridlines) and data-barstyle="solid|outline".

Pick the figure to fit the data

Data shapeFigure (block in chart-room.html)
Two series across a few benchmarksFIG.01 grouped bars — the flagship
One series, ranked by categoryFIG.02 single-series bars
Before → after on sparse metricsFIG.03 dumbbell / lollipop
A value over time / scale / versionsFIG.04 line / scaling curve
Two-axis tradeoff, one point highlightedFIG.05 scatter / quadrant
Composition / share across rowsFIG.06 100% stacked bars
One number that deserves the frameFIG.07 donut
Model × task (or any) matrixFIG.08 heatmap (data-driven JS)
2–4 punchy single numbers, no axesFIG.09 big-stat callouts

How to compute geometry (filling an SVG template)

The SVGs use plain coordinates inside a viewBox. The mapping math you need:

Vertical bars (FIG.01/02). Choose a baseline yBase (value 0) and a top yTop (max value). scale = (yBase - yTop) / maxVal. For a value v: barHeight = v * scale, barY = yBase - barHeight, value label at y = barY - 12. (Specimen FIG.01: yBase=500, 100→y=120, so scale=3.8.)

Dumbbell (FIG.03). Horizontal axis from xMin (value 0) to xMax (value 100). x(v) = xMin + (v/ (maxVal)) * (xMax - xMin). Draw a .stem line from x(before) to x(after), a .dot-base at before, a .dot-primary at after.

Line (FIG.04). y(v) = yBase - v*scale; evenly space x across the points; .line-primary for focal, .line-base (dashed) for baseline; optional .area-primary polygon closes down to yBase.

Donut (FIG.07). C = 2 * π * r (specimen r=104 → C≈653.45). For percent p: dash = (p/100) * C; set stroke-dasharray="{dash} {C}" on the accent ring, transform="rotate(-90 cx cy)" so it starts at 12 o'clock.

Heatmap (FIG.08). Don't hand-place cells — edit the data, tasks, models arrays in the inline <script>. Cell alpha = 0.12 + clamp((v-min)/(max-min)) * 0.88; text flips to white above alpha 0.55. Already handles any grid size.

Big-stat (FIG.09). Pure markup: <span class="fig-num"><span class="accent"> +38</span>%</span> — wrap the whole figure (or the part that should be coloured) in .accent. Keep the numeral and its symbol the same colour.

Build process

  1. Read the study's numbers (e.g. an aggregate.py printout or results.json). Decide which 1–4 figures tell the story; don't over-chart.
  2. Scaffold an HTML file in the study's run folder, e.g. eval/<study>/runs/<run>/figures/<name>.html. Link the design system by relative path: <link rel="stylesheet" href="../../../../design-system/assets/colors_and_type.css"> and .../design-system/charts.css. (Count the ../ from the figure file to eval/design-system/.) Set <body data-accent="…">.
  3. Lift the matching figure block(s) from chart-room.html. Recompute every coordinate from the real data using the math above. Replace category labels, value labels, series names, captions, the masthead/section text. Delete the figures you don't use.
  4. Keep it honest. Label sample sizes (n=), say what the score is, don't round a tie into a win, and don't invent a series the data doesn't have. The angle-generator/meanest-editor anti-slop ethos applies to charts too.
  5. Validate with Playwright (required):
    bash
    cd eval/design-system/scripts
    node validate.mjs ../../<study>/runs/<run>/figures/<name>.html --out ../../<study>/runs/<run>/figures/png
    All checks must pass (no JS errors, accent painted, serif applied, every figure has geometry, no empty SVGs). It writes a full-page PNG plus one crop per figure — those are your embeddable assets.
  6. Eyeball the screenshot before publishing. Read the full-page PNG; confirm bars/labels line up and nothing overflows.

Worked example

For the Fable-5-vs-Opus-4.8 study (eval/fable-vs-opus/), the headline numbers (overall dim mean 4.60 vs 4.36; 67/33 head-to-head; 24 vs 7 robust wins; per- dimension deltas) map cleanly to: a FIG.01 grouped-bars of the 7 dimensions (Fable accent, Opus grey), a FIG.07 donut for the win rate, and a row of FIG.09 big-stat callouts (overall mean, robust wins, publishable rate). See eval/fable-vs-opus/runs/2026-06-09-full/figures/ for the built, validated set.

Guardrails

  • Never edit assets/colors_and_type.css or charts.css to force a one-off look — if a figure needs something the system lacks, that's a design-system change, raise it, don't hack the study.
  • Never add a second colour, a gradient, a drop shadow on a chart, or emoji.
  • Never ship a figure that hasn't passed validate.mjs.
  • Placeholder data stays clearly labelled until real numbers replace it.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Eval Graphics AI skill do?

Turn an eval study's numbers into on-brand, publish-ready figures using the Newsjack chart room (the eval design system), then validate them with Playwright. For producing the charts in a published eval/data study.

Why use Eval Graphics on TypingMind?

Because you install it once and use it with any model. Eval Graphics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Eval Graphics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elvisun/newsjack/tree/main/eval/design-system. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Eval Graphics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Eval Graphics?

As many as you like. As long as a model supports skills, you can use Eval Graphics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Eval Graphics AI skill free?

Yes. It is published on GitHub by elvisun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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