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Evident Charts

Community
rhiever
evident-charts

Use when creating, revising, or reviewing explanatory data charts for a blog, report, paper, slide, newsletter, or social post. Includes matplotlib, seaborn, pandas .plot, ggplot2, Plotly, Altair/Vega-Lite, D3, and PNG/SVG charts. Also use for "critique this chart", "why does this plot look bad", or "make this figure better" with a chart image. Not for unrelated coding, software architecture diagrams, or non-data illustrations.

Overview

Publisherrhiever
Repositoryevident-charts
Skill nameevident-charts
Stars
61
Forks
5
Bundled files
23
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.

  • 23 bundled files

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

  • Open source

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

Installation

Install the Evident Charts 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/rhiever/evident-charts/tree/main/skills/evident-charts
  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/rhiever/evident-charts.git /tmp/evident-charts
mkdir -p .claude/skills
cp -r /tmp/evident-charts/skills/evident-charts .claude/skills/evident-charts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evident Charts 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 Evident Charts 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 Evident Charts 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.

evident-charts

Charts that make one point a reader gets at a glance. Resolve resources from this installed skill's directory, not the working directory (Claude Code: ${CLAUDE_SKILL_DIR} when available). Run scripts by absolute path with the project's Python.

Critique, review, or a chart image with a question: follow references/critique.md; otherwise the workflow below.

Host and user instructions

  • User instructions and host requirements take precedence over this skill, including its evidence rules. Preserve an explicit override, explain the consequence briefly, and report the rule deviation and any failed check; do not silently replace the requested chart.
  • Use only available, permitted tools. Python 3.10+ runs the checks; matplotlib checks also need matplotlib and numpy. Palette and SVG scripts use the standard library; SVG layout checks need Chrome/Chromium. Optional helpers or exports may need their own libraries.
  • Without Chrome, run SVG spec checks when a supported spec is available; otherwise skip SVG checks. Without Python, skip script checks. Review a rendered image when the host can view it. Report checks run, failed, skipped, and not checkable, with reasons. A partial check is not a full pass.
  • Use a fresh reviewer only when delegation and vision are available and allowed by the user and host. Otherwise self-review; if no image can be viewed, mark visual review skipped. Do not install tools or delegate outside the host's permissions.

Rules

Rule lines read ID [tag] rule. Break: escape. check: name. [E] experimental evidence: break only with a stated reason. [P] practitioner consensus: break for a clear reason. [T] taste: bend freely. A project's style guide overrides the house look (assets/) and [T] rules; retain integrity guidance unless explicitly overridden. check: names a check in scripts/check_chart.py (scripts/check_svg.py runs a subset of them on other stacks; --list-checks names them); palette means scripts/check_palette.py. Citations: references/sources.md, only when asked why.

Workflow

  1. Analyze. Load the data, print summary stats, confirm units and scope, run the checks below.
  2. Brief in one line: Takeaway: <claim the data supports> | Audience: <lay/expert> | Destination: <preset>. The takeaway answers every part of the request (HI-1). Ask at most two questions, only if the takeaway is ambiguous. Presets (assets/presets.json): blog (default), mobile (phone, phone-read newsletters), social (X), social_portrait (LinkedIn, Instagram), slide (deck), report (print, memos, docs).
  3. Choose the form from the takeaway and data shape; open references/choosing.md only when the form is not obvious. It may be a table or one hero number over a sparkline or part bar (stats_size="hero", LY-6). Render alternatives (references/candidates.md) only when unsure between two honest forms.
  4. Build in the project's stack; otherwise Python + matplotlib with scripts/evident.py (other stacks: references/libraries.md). Fit title and subtitle with ev.fits_title and ev.fits_subtitle.
  5. Check with available tools. matplotlib: python <skill-dir>/scripts/check_chart.py <chart.py> --dest <preset>; other stacks: export an SVG with live text and run python <skill-dir>/scripts/check_svg.py <chart.svg> --dest <preset> --spec <figure or Vega JSON> (its docstring gives each stack's export). Color-blindness and contrast checks need the render measurement. Fix failures together, except explicit user overrides; retain those findings in the report.
  6. Review the rendered PNG, never the code (references/critique.md, Review loop), using an allowed fresh reviewer or self-review. Fix P0/P1 issues within the user's instructions, re-render, recheck, re-review; stop when none remain to fix, at most 3 rounds. Report unresolved findings and skipped work.
  7. Deliver the format asked for (report may add a PDF): absolute paths to available outputs; the brief; analysis choices a reader should know (any transform, exclusions, thresholds, minimum n); source confidence and anything unconfirmed (SRC-1); alt text when the image is viewable (A1); deviations; checks run, failed, skipped, or not checkable; the one-line review log. Never claim an output was rendered or inspected when it was not.

Analysis checks

  • Use the field's standard comparison: same period last year, calendar months or quarters, named members the audience acts on, standard thresholds; pool, smooth, roll, or rebase only when it would mislead, named in the subtitle (H22, H24).
  • Flag a preliminary last point, never pool it away; compare a partial last period like-for-like (year to date, trailing 12 months, or within the only measure covering it) (H17, H15, H12).
  • Parts sum to the published total; remainder = aggregate minus members; check duplicates, sentinel codes (0, 888, 999, blank), future-dated zero rows (unreleased), split events, and frozen values; compute every number from the data (H11, H24).
  • Show n or intervals for small samples, never rank tiny ones; test survey differences at the publisher's MOE level (H23, U1).

Chart defaults

  • Title answers the question as asked ("what drives X": contribution, not rate; H13); subtitle qualifies it and gives what, units, and when; source line names the real publisher and dataset, a note any data you added; nothing addressed to the user on the chart (TI-1, TI-6, SRC-1, SRC-2).
  • Marks encode the title's measure by position on a common scale; lines only over ordered x (ENC-1, TIME-1).
  • Bars from zero; lines use a range that fits the data (zero only for ratio claims); no dual y-axes, 3D, inverted value axes, or rainbow colormaps (H1, H2, H3, H8, H5, C11).
  • One accent on gray when one element is the story; otherwise color meaningful groups; at most 4 hues (C2, C3).
  • Direct labels over legends (LB-1). Up to about 10-12 marks: value labels replace the value axis, never both; show the numbers the comparison needs; context columns only if needed (VL-1, VL-2).
  • Units on every tick or in the axis title; light value gridlines only; remove ink that carries no information; leave white space; text at least 12 px at display size (TI-7, GR-1, DC-1, TY-2).

Files

Read a reference only when its decision comes up.

FileRead when
references/integrity.mdAn analysis, axis, scale, uncertainty, or accessibility question
references/text.mdTitles, labels, annotation, layout
references/color.mdA color decision beyond the defaults
scripts/evident.pyOptional matplotlib helper; its docstring is the usage
assets/presets.json, assets/palettes.jsonDestination sizes and type scale; house colors

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 Evident Charts AI skill do?

Use when creating, revising, or reviewing explanatory data charts for a blog, report, paper, slide, newsletter, or social post. Includes matplotlib, seaborn, pandas .plot, ggplot2, Plotly, Altair/Vega-Lite, D3, and PNG/SVG charts. Also use for "critique this chart", "why does this plot look bad", or "make this figure better" with a chart image. Not for unrelated coding, software architecture diagrams, or non-data illustrations.

Why use Evident Charts on TypingMind?

Because you install it once and use it with any model. Evident Charts 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 Evident Charts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rhiever/evident-charts/tree/main/skills/evident-charts. 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 Evident Charts?

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 Evident Charts?

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

Is the Evident Charts AI skill free?

Yes. It is published on GitHub by rhiever 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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