Visualizer

Community

MAGA2010

visualizer

Decide and render one figure per argument: chart type chosen against alternatives, title states the finding, caption defines the encoding, body gives fact -> interpretation -> implication.

Overview

Publisher	MAGA2010
Repository	`data-master`
Skill name	`visualizer`
Stars	55
Forks	1
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by MAGA2010 on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/MAGA2010/data-master/tree/main/skills/visualizer
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.

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/MAGA2010/data-master.git /tmp/data-master
mkdir -p .claude/skills
cp -r /tmp/data-master/skills/visualizer .claude/skills/visualizer

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Visualizer 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 Visualizer 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 Visualizer 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.

visualizer

Stage 3a of the data-master pipeline. The router in ../../SKILL.md owns the end-to-end run; this skill owns one stage and one gate. Read the parent contract before changing anything here: the invariants in ../../SKILL.md (additive-only, types decided once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

When to invoke

After statistics exist. The decision layer runs even when no figure is produced -- "no chart says anything a table does not" is itself a recorded decision.

Inputs

cleaned data
analysis/profile.json, descriptive.json, inferential.json

Outputs

analysis/chart_decisions.json: palette, markers, decisions[{id, chart_type, spec, uncertainty, title, caption, body{fact, interpretation, implication}, rationale, rejected, output_path, order}], rules, dtype_provenance
outputs/charts/*.png at 200 dpi
outputs/charts_rendered.json: per-figure bytes, ink_ratio, non_blank, pixels

Commands

powershell
python scripts/chart_decision.py --clean outputs/cleaned/cleaned.csv --profile outputs/analysis/profile.json --descriptive outputs/analysis/descriptive.json --inferential outputs/analysis/inferential.json --out outputs/analysis/chart_decisions.json
python scripts/render_chart.py --clean outputs/cleaned/cleaned.csv --decisions outputs/analysis/chart_decisions.json --outdir outputs/charts

Gate (default FAIL)

every decision names the chart types it rejected and why;
every title states a finding, not a topic;
every figure passes its blank-page check (non_blank: true, ink ratio above zero);
a comparison figure states its interval (error bars, band or CI).

Failure modes seen in real runs

A title and a subtitle drawn at the same anchor. suptitle(wrap=True) keeps y=0.98 and grows downward into whatever sits below it; wrap by measured width and place each line explicitly.
A blank figure that saves without error. Ink ratio is read back from the PNG after save, because a successful savefig proves only that a file exists.
The same argument drawn as three separate figures. Panels, not repeats.

Do not

do not use 3D unless it changes the ranking;
do not use a rainbow palette for an ordered variable;
do not render a figure whose decision record has no rejected alternative.

Upstream / downstream

previous: ../statistical-analyzer/SKILL.md
next: ../report-generator/SKILL.md
catalog: ../../references/chart-decision-catalog.md

Frequently asked questions

What does the Visualizer AI skill do?

Decide and render one figure per argument: chart type chosen against alternatives, title states the finding, caption defines the encoding, body gives fact -> interpretation -> implication.

Why use Visualizer on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MAGA2010/data-master/tree/main/skills/visualizer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Visualizer?

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 Visualizer?

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

Is the Visualizer AI skill free?

Yes. It is published on GitHub by MAGA2010 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

visualizer

When to invoke

Inputs

Outputs

Commands

Gate (default FAIL)

Failure modes seen in real runs

Do not

Upstream / downstream

Frequently asked questions

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