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data-visualization

data-visualization is a skill for Claude Code, Codex from msdakot/ai-foundary. It costs 34 tokens per session (999 once invoked), scanned A, original, MIT.

A data-visualization guide for turning datasets into accurate, accessible charts and interactive dashboards.

In plain words
What is it for?
Use it to visualize comparisons, distributions, proportions, relationships, trends, and geographic data in browsers, notebooks, PDFs, or presentations.
Why use it?
It helps avoid misleading charts by choosing the chart type from the analytical goal, audience, data, and output format before choosing a library.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 5repo 6mo ago A scan 34 tokens original MIT

Good fit Use it to visualize comparisons, distributions, proportions, relationships, trends, and geographic data in browsers, notebooks, PDFs, or presentations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/msdakot/ai-foundary/data-visualization
View source ↗ msdakot/ai-foundary
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add msdakot/ai-foundary --skill data-visualization
Clone the repo
git clone --depth 1 https://github.com/msdakot/ai-foundary

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for data-visualization

README.md
[![agentmods](https://agentmods.dev/badge/skills/msdakot/ai-foundary/data-visualization/github.svg)](https://agentmods.dev/skills/msdakot/ai-foundary/data-visualization)
Your own site
<a href="https://agentmods.dev/skills/msdakot/ai-foundary/data-visualization"><img src="https://agentmods.dev/badge/skills/msdakot/ai-foundary/data-visualization/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for data-visualization

Your own site · 80×15
<a href="https://agentmods.dev/skills/msdakot/ai-foundary/data-visualization"><img src="https://agentmods.dev/badge/skills/msdakot/ai-foundary/data-visualization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 999 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00034 $0.00999
Opus 5.5 $0.00014 $0.00400
Sonnet 5.5 $0.00007 $0.00200
Haiku 4.5 $0.00003 $0.00100

Measured 2mo ago against content hash 6ebecf47c658, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

data-visualization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/ai-data-agents/data-visualization/SKILL.md · 105 lines

How it starts

The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Visualization Agent

You build visualizations that communicate clearly and accurately. You choose the right chart type before picking a library.

Step 1 — Understand the Data and Goal

Answer these before touching code:

  • What is the analytical goal? (comparison, distribution, composition, relationship, trend, geospatial)
  • What is the audience? (technical, executive, public)
  • Is interactivity needed, or is this a static export?
  • What is the rendering target? (web browser, notebook, PDF, presentation)

Step 2 — Choose Chart Type

Use perceptual accuracy hierarchy (Cleveland & McGill) — position > length > angle > area > color:

Goal Best chart type
Compare values across categories Bar chart (horizontal if many categories)
Show distribution Histogram, KDE, violin, box plot
Show composition Stacked bar (avoid pie charts unless ≤ 4 slices)
Show relationship Scatter, bubble, heatmap
Show trend over time Line chart, area chart
Show part-of-whole at one point Treemap, waffle (not pie)
Geospatial Choropleth, dot map

Step 3 — Choose Library

Use case Library
Custom interactive web D3.js
Standard interactive web charts Plotly, Chart.js
Dashboards Dash, Streamlit, Observable
Scientific / publication static Matplotlib, Seaborn
Quick EDA in notebooks Plotly Express, Altair
Large datasets (> 50K points) Datashader + Holoviews, or canvas-based

Switch from SVG to Canvas for datasets > 5,000 rendered elements.

Step 4 — Design and Implement

Axes and Labels

  • Label all axes with name and unit
  • Round axis tick values to human-readable numbers
  • Avoid overlapping labels — rotate, abbreviate, or reduce density

Color

  • Use colorblind-safe palettes: viridis, cividis, ColorBrewer sequential/diverging
  • Sequential palette for continuous data, categorical palette for nominal groups
  • Diverging palette when data has a meaningful midpoint (e.g., positive/negative)
  • Test with a color vision simulator before finalizing

Read the full file on GitHub · 105 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 105 lines · 34 tokens per session scan A 6ebecf47c658

Subscribe to this mod's changes

data-visualization is a skill published in the GitHub repository msdakot/ai-foundary (5 stars, last pushed 6mo ago), licensed MIT. It adds 34 tokens to every session and 999 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metrics
Related

data-visualization compared with skills from other repositories

data-visualization next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
data-visualizationmsdakot/ai-foundarySkill56mo agoMITA34Claude Code, Codex
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playwright-climicrosoft/playwrightSkill99kyesterdayApache-2.0A19Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginnersSkill79k4d agoMITA200Claude Code, Codex
systematic-debuggingobra/superpowersSkill313k3d agoMITA21Claude Code
next-cache-components-adoptionvercel/next.jsSkill143kyesterdayMITA95Claude Code
next-partial-prefetching-adoptionvercel/next.jsSkill143kyesterdayMITA103Claude Code