loom-data-visualization

A guide for choosing and designing charts, dashboards, and analytical reports. It explains how to match a chart to the relationship in the data and present quantities accurately and accessibly.

In plain words
What is it for?
Use it to select charts for comparisons, proportions, distributions, trends, rankings, geographic data, correlations, and performance against targets.
Why use it?
It helps prevent misleading charts, distorted comparisons, hidden variation, and inaccessible displays. The guidance applies across analytics, infrastructure monitoring, and machine-learning work.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/cosmix/loom/loom-data-visualization
Any agent
npx skills add cosmix/loom --skill loom-data-visualization
Clone the repo
git clone --depth 1 https://github.com/cosmix/loom

Made for: Claude Code, Codex.

Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,505 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00040 $0.02505
Opus 5 $0.00020 $0.01252
Sonnet 5 $0.00008 $0.00501
Haiku 4.5 $0.00004 $0.00250

Measured 2d ago against content hash 211d82690947, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

loom-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 2d 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.

skills/loom-data-visualization/SKILL.md · 179 lines

How it starts

The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Visualization

Overview

Match the chart to the data relationship, encode quantities in perceptually accurate channels, avoid distortion, and make it accessible. This skill is the design layer above the plotting library.

Chart selection by data relationship

Goal Prefer Avoid
Compare across categories horizontal bar (sorted), dot plot pie with >~5 slices
Part-to-whole stacked bar, treemap; pie only ≤5 slices many pies / donuts for precise comparison
Distribution histogram, box, violin, ECDF bar of means (hides spread)
Two-variable relationship scatter (+ trend), 2D density/hexbin when dense scatter with 100k overplotted points
Correlation matrix heatmap (diverging scale) 3D surface
Trend over time line; area for cumulative connecting unordered categories with lines
Ranking ordered bar / lollipop pie
Performance vs target bullet chart gauge cluster
Geographic choropleth (normalized), point/flow map raw-count choropleth (just shows population)

Perceptual accuracy (why bars beat pies)

Cleveland–McGill ranking of how accurately humans decode a quantity:

position on common scale > position on non-aligned scale > length > angle/slope > area > volume > color hue/saturation.

  • Encode the most important quantity in position/length (bar, dot, line), not area or color.
  • Pie/donut = angle+area (weak); bubble = area (people underestimate large circles — area scales as r², so double the value ≠ double the radius). Reserve area/color for secondary dimensions.
  • Sort categorical bars by value (not alphabetically) unless order is semantic — sorting is the insight.

Avoiding distortion (the "lie factor")

  • Truncated y-axis exaggerates change. Bar charts must start at 0 (bar length encodes the value). Line/time-series may zoom the axis to show variation — but label it clearly; don't imply a 2% change is a cliff.
  • Dual y-axes manufacture spurious correlation and let you slide two series' scales arbitrarily. Replace with indexed series (all = 100 at t0), two small multiples, or a ratio.
  • Aim for lie factor ≈ 1 (graphic effect size / data effect size). Don't use 3D, shadows, or area to represent 1D quantities.
  • Chartjunk: maximize data-ink ratio (Tufte) — drop gridline clutter, heavy borders, redundant legends, background gradients. Direct-label lines instead of a legend when few series.

Read the full file on GitHub · 179 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. 2d ago First seen · 179 lines · 40 tokens per session scan A 211d82690947

Subscribe to this mod's changes

loom-data-visualization is a skill published in the GitHub repository cosmix/loom (54 stars, last pushed 3d ago), licensed MIT. It adds 40 tokens to every session and 2,505 once invoked, about $0.0002 per session on Opus 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 2026-08-30.

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