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Figure Table Quality

Community
Mathews-Tom
figure-table-quality

Readability and rendering audit for figures and tables in academic manuscripts. Computes effective font/marker sizes at display scale from generation scripts, checks label collisions, color/hatch accessibility, axis-range efficiency, table formatting, and cross-figure consistency. Triggers on: "check figure quality", "audit plots", "readability check", "figure rendering", "are my figures readable", "table formatting check". Companion to figure-rhetoric (visual argument) and manuscript-typography (typesetting).

Overview

PublisherMathews-Tom
Repositoryarmory
Skill namefigure-table-quality
Stars
318
Forks
47
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 Mathews-Tom on GitHub. Read the source before you install it.

Installation

Install the Figure Table Quality 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/Mathews-Tom/armory.git /tmp/armory
mkdir -p .claude/skills
cp -r /tmp/armory/skills/figure-table-quality .claude/skills/figure-table-quality
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Figure Table Quality 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 Figure Table Quality 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 Figure Table Quality 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.

Figure & Table Quality Audit

Pipeline position: Phase 2.5 (between Grounding/Polish and Submission). Runs after figure-rhetoric (content) and before arxiv-preflight (compliance). See /manuscript-pipeline for full execution order.

Purpose

Verify that every figure and table in a manuscript renders at readable size in the compiled PDF. figure-rhetoric checks whether figures communicate the right message. This skill checks whether the reader can physically read them.

Execution

Step 1 — Build the display-scale map

For every \includegraphics in the .tex source:

  1. Extract the display width (e.g., \textwidth, 0.7\textwidth, 0.55\textwidth)
  2. Compute the effective display width in inches using the document geometry
  3. Read the figure generation script to find the figsize for each figure
  4. Compute: scale = display_width / figsize_width

Also extract from the generation script or config:

  • Base font sizes: title, label, tick, legend, annotation
  • Marker sizes, line widths
  • Bar widths (for grouped bar charts)

Step 2 — Per-figure audit (all 9 checks)

For each figure, compute effective values at display scale and check:

2a. Font size at render
effective_font = script_font × scale
ElementMinimumWarning
Axis title7pt8pt
Tick labels6pt7pt
Legend text6pt7pt
Annotations6pt7pt
Panel titles7pt8pt

FAIL if any element falls below minimum. WARN if any element falls below warning threshold.

2b. Label collision

Check for overlapping text in:

  • X-axis tick labels (especially with rotation < 45° and > 3 labels)
  • Y-axis tick labels (long text strings)
  • Data annotations near each other
  • Legend entries overlapping data

For rotated labels: compute horizontal footprint as len(label) × char_width × cos(rotation). If footprint > tick spacing, FAIL.

2c. Marker and line visibility
effective_marker = script_marker × scale
effective_linewidth = script_linewidth × scale
  • Markers below 4pt effective: WARN
  • Line widths below 0.5pt effective: WARN
2d. Bar chart readability

For grouped bar charts:

  • Compute effective bar width in inches
  • Check if bars are distinguishable (minimum 3pt effective width)
  • Check if hatch patterns render at effective size
  • Check for bar-label alignment
2e. Color and hatch accessibility
  • Are all series distinguishable in grayscale?
  • Do hatch patterns provide redundant encoding for color?
  • Are there more than 5 colors without hatching? WARN
  • Are similar colors used for unrelated series?
2f. Axis range efficiency
  • Compute data range vs axis range
  • If less than 40% of axis range contains data: WARN (wasted space)
  • If data touches axis boundary: WARN (clipped data)
2g. Annotation readability
  • Do any data annotations overlap each other?
  • Are annotations positioned to avoid occluding data?
  • Do annotations use consistent formatting (fontsize, weight)?
2h. Legend placement
  • Does the legend overlap any data points or bars?
  • Is the legend in a consistent position across similar figures?
  • For multi-panel figures: is the legend in the first panel only (not repeated)?
2i. Whitespace and margins
  • Does tight_layout() or equivalent handle margins?
  • Are panel titles cut off?
  • Is there excessive whitespace (> 30% of figure area empty)?

Step 3 — Table audit

For each \begin{tabular} or \begin{table}:

  1. Column alignment — Are numeric columns right-aligned? Text left-aligned?
  2. Rule style — Uses booktabs (\toprule, \midrule, \bottomrule)? No vertical rules?
  3. Caption position — Table captions above, figure captions below?
  4. Width — Does the table overflow margins? Check for \resizebox or \small hacks.
  5. Number formatting — Consistent decimal places? Aligned decimal points?
  6. Header clarity — Are column headers unambiguous?

Step 4 — Cross-figure consistency

  1. Shared elements — Do figures that share axes use the same scale?
  2. Color scheme — Is the same color used for the same condition across all figures?
  3. Label vocabulary — Are condition/model labels identical across all figures?
  4. Font family — Same font across all figures?

Output format

markdown
## Figure & Table Quality Report

### Display Scale Map
| Figure | figsize | display | scale | verdict |
|--------|---------|---------|-------|---------|
| fig1   | 7×4     | 6.27"   | 90%   | OK      |

### Per-Figure Findings
#### Figure 1 (fig1_resolve_rates.pdf)
- [PASS] Font sizes: tick 8.1pt, legend 8.1pt
- [PASS] No label collisions
- [WARN] Bar width 3.2pt — borderline at print size
...

### Table Findings
#### Table 1 (table1_resolve.tex)
- [PASS] Booktabs rules
- [PASS] Caption above tabular
...

### Cross-Figure Consistency
- [PASS] Color scheme consistent
- [FAIL] Label mismatch: fig2 uses "cmd", other figures use "Yuj"

### Summary
- [count] FAIL (must fix)
- [count] WARN (should fix)
- [count] PASS

Auto-Fix Rules

AUTO-FIX (apply directly):

  • Rotation increase for overlapping x-labels (20°/15° → 45°)
  • figsize reduction to match display context (eliminate >25% downscaling)
  • Missing tight_layout() calls
  • Inconsistent label text across figures (align to config/source of truth)

HUMAN-REQUIRED (present and wait):

  • Figure redesign (different layout, panel arrangement)
  • Axis range changes
  • Color scheme changes
  • Font size increases that affect layout
  • Table restructuring

Integration

This skill reads:

  • .tex source (\includegraphics directives, \geometry settings)
  • Figure generation scripts (figsize, font sizes, rotations, annotations)
  • Figure config files (shared settings)
  • Generated figure files (visual spot-check if PDF readable)
  • Table .tex files

It does NOT:

  • Evaluate whether figures communicate the right message (that's figure-rhetoric)
  • Check arXiv format compliance (that's arxiv-preflight)
  • Audit prose or claims (that's manuscript-review)

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 Figure Table Quality AI skill do?

Readability and rendering audit for figures and tables in academic manuscripts. Computes effective font/marker sizes at display scale from generation scripts, checks label collisions, color/hatch accessibility, axis-range efficiency, table formatting, and cross-figure consistency. Triggers on: "check figure quality", "audit plots", "readability check", "figure rendering", "are my figures readable", "table formatting check". Companion to figure-rhetoric (visual argument) and manuscript-typography (typesetting).

Why use Figure Table Quality on TypingMind?

Because you install it once and use it with any model. Figure Table Quality 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 Figure Table Quality in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mathews-Tom/armory/tree/main/skills/figure-table-quality. 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 Figure Table Quality?

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 Figure Table Quality?

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

Is the Figure Table Quality AI skill free?

Yes. It is published on GitHub by Mathews-Tom 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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