Agent skill

Figure Table Quality

by Mathews-Tom in Mathews-Tom/armory

Readability and rendering audit for figures and tables in academic manuscripts.

MITAuto-check passedFrontend & Design

Install Figure Table Quality

skills CLI
$ npx skills add Mathews-Tom/armory --skill figure-table-quality -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Mathews-Tom/armory figure-table-quality --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Mathews-Tom/armory.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/figure-table-quality .claude/skills/figure-table-quality && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
figure-table-quality
GitHub stars
328
Token cost
~1.7k tokens
SKILL.md length
682 words
Files
2
Skills in repo
80
Repo updated
First seen
Licence
MIT

At a glance

Readability and rendering audit for figures and tables in academic manuscripts.

  • Works in 4 steps: Build the display-scale map → Per-figure audit (all 9 checks) → Table audit → …
  • : check figure quality
  • SKILL.md covers Purpose, Execution, Output format and Auto-Fix Rules, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Figure Table Quality is an agent skill from Mathews-Tom/armory. 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).

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `evals/cases.yaml`).

It sits in Frontend & Design, covering Plain language and style rules, LaTeX and Accessibility. The repository describes itself as: Curated, production-grade skills for AI coding agents. Battle-tested workflows for developers who use AI seriously. The licence is MIT.

When your agent uses it

  • : check figure quality
  • Readability check
  • Figure rendering
  • Are my figures readable

Example prompts

  • “check figure quality”
  • “audit plots”
  • “readability check”
  • “/figure-table-quality”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Build the display-scale map
  2. Per-figure audit (all 9 checks)
  3. Table audit
  4. Cross-figure consistency

What it can do on your machine

Read from SKILL.md and the folder at commit 4594fb7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Figure Table Quality loads about 1.7k tokens when it runs. Until then it costs about 134 tokens; SKILL.md has 682 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~134
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Mathews-Tom/armory at commit 4594fb7, republished under its MIT licence (© Mathews-Tom). 682 words, ~1,713 tokens.

Download SKILL.mdSave it as .claude/skills/figure-table-quality/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
figure-table-quality
description
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).
metadata.version
1.0.0
metadata.complements
figure-rhetoric, manuscript-review, manuscript-typography
metadata.category
review
metadata.tags
figures, tables, readability, rendering, latex
metadata.difficulty
advanced
metadata.phase
review

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)?
Show full SKILL.md (258 more words)Show less
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)

© Mathews-Tom, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/figure-table-quality of Mathews-Tom/armory.

  • SKILL.md
  • evals/cases.yaml

Open the folder on GitHubat commit 4594fb7

Compare with similar skills

Figure Table Quality next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Figure Table Quality compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Figure Table Quality this skillMathews-Tom/armory328—~1.7kAutomated safety check: PassMIT
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Baseline UIibelick/ui-skills9.5k8 repos~855Automated safety check: PassMIT
UI/UX Design System AdvisorGalaxy-Dawn/claude-scholar5.7k1 repos~1.1kAutomated safety check: PassMIT
Color Auditrome-os/rome725—~2.7kAutomated safety check: PassMIT
Typesetsudomakes/backroad16210 repos~1.4kAutomated safety check: PassMIT

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Questions about Figure Table Quality

What does Figure Table Quality do?

Readability and rendering audit for figures and tables in academic manuscripts. Figure Table Quality is an agent skill from Mathews-Tom/armory. Readability and rendering audit for figures and tables in academic manuscripts.

When should I use Figure Table Quality?

Figure Table Quality fits situations like: : check figure quality; readability check; figure rendering; are my figures readable.

How do I install Figure Table Quality in Claude Code?

Run `npx skills add Mathews-Tom/armory --skill figure-table-quality -a claude-code`. Or copy the skill folder (skills/figure-table-quality in Mathews-Tom/armory) into .claude/skills/figure-table-quality in your project. Claude Code loads it when a task matches its description.

How do I install Figure Table Quality in Codex?

Run `npx skills add Mathews-Tom/armory --skill figure-table-quality -a codex`. Or copy the skill folder (skills/figure-table-quality in Mathews-Tom/armory) into .agents/skills/figure-table-quality in your project. Codex loads it when a task matches its description.

Can I use Figure Table Quality in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Mathews-Tom/armory --skill figure-table-quality -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/figure-table-quality, .gemini/skills/figure-table-quality, .github/skills/figure-table-quality and .opencode/skills/figure-table-quality in your project.

What does Figure Table Quality need to run?

SKILL.md names no scripts, command-line tools or credentials: Figure Table Quality is instructions for the agent only.

Does Figure Table Quality access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Figure Table Quality safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Figure Table Quality use?

Figure Table Quality is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Figure Table Quality use?

About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Figure Table Quality?

Skills that share tags, products or a category with Figure Table Quality: Md Review (borghei/Claude-Skills, 881 stars), Baseline UI (ibelick/ui-skills, 9.5k stars), UI/UX Design System Advisor (Galaxy-Dawn/claude-scholar, 5.7k stars) and Color Audit (rome-os/rome, 725 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Figure Table Quality?

Mathews-Tom (a GitHub user) maintains it in Mathews-Tom/armory, which has 328 GitHub stars. The repository holds 80 skills in this directory. The repository was last updated on October 6, 2026.

Source: Mathews-Tom/armory on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.