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Data Forms

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huytieu
data-forms

Pick the right way to represent a dataset so a reader gets the finding in three seconds — a catalog of 20+ chart and diagram forms with when-to-use and failure modes, plus the encoding decisions that make any of them readable (takeaway headline, direct labels, kill the axis, highlight-and-mute, show the caveat). Style-agnostic — use with whatever palette or design system the destination already has. Use when charting survey results, benchmark data, usage metrics, or research findings for a post, brief, deck, or report, and whenever the default bar chart feels like it's burying the point.

Overview

Publisherhuytieu
RepositoryCOG-second-brain
Skill namedata-forms
Stars
1.2K
Forks
138
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Data Forms 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.
  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/huytieu/COG-second-brain.git /tmp/COG-second-brain
mkdir -p .claude/skills
cp -r /tmp/COG-second-brain/skills/data-forms .claude/skills/data-forms
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Forms 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 Data Forms 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 Data Forms 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.

data-forms

A repertoire, not a style. Distilled from Lenny's Newsletter data illustrations (2025-2026 survey issues) — what makes those charts work is not the palette, it's the form selection and the encoding discipline. Both are portable to any visual language.

For the visual layer, use whatever the destination already has: your product's brand tokens for in-product surfaces, the built-in dataviz skill for palette construction and accessibility, your house style for personal sites. This skill decides what shape the data takes — the layer above color.

Start here: the picker

What the data doesReach forForm #
One question, 3-6 ordered answersOrdered column · Waffle · Nested bands1 · 6 · 7
One question, many multi-select answersRanked bar · Valence bar3 · 4
Same metric, two points in timePaired columns · Slope2 · 9
Same metric, 3+ points in timeTime-series area · Bump13 · 18
One metric across 7-10 ordered bucketsStaircase column14
One metric across many unordered segmentsSmall multiples · Dot plot8 · 17
Signed score (-100…+100, net, delta)Diverging bar5
Two groups compared across many rowsDumbbell16
Agreement / Likert across several statementsCentered stacked row19
Segments × several metricsPill matrix10
Open-text answersWord cloud · Coded theme bar11 · 3
A relationship the numbers can't carryMetaphor diagram · Two-column flow12 · 20
A named segmentation from clusteringPersona cards15
Two dimensions, few labeled pointsNamed-quadrant scatter21

Full catalog with mockups and failure modes: references/forms.md.

Six decisions that matter more than the form

1. Write the takeaway before you pick a form. Finish the sentence "The point of this chart is ___." If you can't, you have a table, not a chart, and no form will save it. That sentence becomes the headline. Two legal headline modes:

  • Claim, for comparisons and analysis — "Burnout is surging, and optimism is fading"
  • Question verbatim, when the chart is the answer distribution — "How worried are you about layoffs?"

Never a field name. "Burnout by company size" is a spreadsheet tab.

2. Print every value. The reader should never estimate against an axis. Number sits adjacent to its mark. This is what earns you the right to do #3.

3. Delete the axis. Baseline only — no y-axis, no gridlines, no ticks. Exceptions: time series and slope charts, where the shape is the message and needs a reference grid. Everywhere else the axis is scaffolding you forgot to remove.

4. No legend when a direct label fits. Category name goes on or beside its mark. A legend is justified only when one key serves several panels (small multiples) or encodes a dimension orthogonal to position (valence, group membership).

5. Highlight two, mute the rest. In a nine-category comparison, only the one or two subjects named in the headline get emphasis; everyone else recedes. The chart should argue the headline, not present the table. If the highlighted subject isn't actually the outlier, the chart just disproved your headline — change the headline, not the data.

6. Show the caveat in-frame. Small n, margin of error, non-response, "percentages sum past 100 because multi-select" — put it in the chart, not a footnote. A dashed outline on a non-significant bar with a bracket reading "difference within margin of error" is more credible than a clean chart plus a disclaimer nobody reads.

And one restraint: at most one annotation. Point at the thing a reader would otherwise miss. Zero is fine. Two is clutter.

Encoding rules of thumb

  • Color means something or it isn't there. Don't color bars that a label already distinguishes. Reserve saturation for the subject of the headline.
  • Sequential ramp = magnitude or rank. Diverging ramp = signed. Categorical = identity. Mixing these is the most common way a chart lies.
  • Per-column scales in a matrix. In form 10, each metric gets its own ramp and its own direction, otherwise "dark = high" fights "dark = bad."
  • Neutral is an unfilled outline, not grey. Grey reads as missing data.
  • Redundant encoding is fine where there's no axis — a word cloud can size and color by frequency; nothing is being wasted.
  • Order carries meaning. Sort by value unless the categories are inherently ordered (time, buckets, Likert). Keep the same order across every panel of a small multiple.
  • Bars start at zero, always. If zero isn't meaningful for the metric, you wanted a dot plot (form 17), not a bar.
  • Anthropomorphize only when the subject is human. Waffle grids of people, emoji anchors, persona illustrations — these earn their keep for sentiment and headcount, and read as cheap for latency and revenue.

Workflow

  1. State the takeaway in one sentence.
  2. Classify what the data does (distribution / ranking / comparison / trend / correlation / composition / qualitative) and pick from the picker table.
  3. Read that form's entry in references/forms.md — especially its failure mode. If your data triggers the failure mode, take the alternative listed there.
  4. Apply the six decisions and the encoding rules.
  5. Build it in whatever the destination uses.
  6. Render it and look at the image. Not the DOM, not the spec — the pixels. Run the checklist. Fix what you see and re-render.

Pre-ship checklist

Read the rendered image and answer each. A "no" is a fix, not a note.

  • Headline states a finding or asks the literal question — not a field name
  • Every mark carries its value, legible at 50% zoom
  • No legend that direct labels could have replaced
  • Emphasis lands on the subject of the headline; everything else recedes
  • Longest label doesn't collide with, wrap under, or overflow its mark
  • Bars start at zero; no truncated scale
  • ≤1 annotation
  • Squint test: at 25% zoom the shape of the answer still reads
  • Caveats (n, margin of error, multi-select) visible in-frame
  • The form's own failure mode from references/forms.md does not apply

Reference

references/forms.md — 21 forms, each with an ASCII mockup, when to use it, and the condition under which it stops working.

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 Data Forms AI skill do?

Pick the right way to represent a dataset so a reader gets the finding in three seconds — a catalog of 20+ chart and diagram forms with when-to-use and failure modes, plus the encoding decisions that make any of them readable (takeaway headline, direct labels, kill the axis, highlight-and-mute, show the caveat). Style-agnostic — use with whatever palette or design system the destination already has. Use when charting survey results, benchmark data, usage metrics, or research findings for a post, brief, deck, or report, and whenever the default bar chart feels like it's burying the point.

Why use Data Forms on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huytieu/COG-second-brain/tree/main/skills/data-forms. 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 Data Forms?

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 Data Forms?

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

Is the Data Forms AI skill free?

Yes. It is published on GitHub by huytieu 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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