alterlab-figure-qa

name	alterlab-figure-qa
description	Verify publication figures with a render-then-check QA pass — data-fidelity against every underlying row, axis/label floor-and-ceiling legibility, bounding-box collision detection for overlapping text/markers, and 300-dpi print-readiness. Use when proofing or auditing a finished figure for correctness and print quality, catching mislabeled or overlapping elements, or confirming a plot faithfully represents its data before submission. For CREATING the plot prefer alterlab-matplotlib (or alterlab-seaborn / alterlab-plotly); for multi-panel publication layout prefer alterlab-scientific-viz; for schematic diagrams prefer alterlab-scientific-schematics. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Write Edit Bash(python:*) Bash(uv:*)
compatibility	Runs under `uv run python` with the plotting stack already used to make the figure (Matplotlib and, for image/text-box checks, Pillow). No GPU, no account. Operates on a rendered figure plus the source data — it re-renders and inspects, it does not need network access.
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

Figure QA (render-then-verify)

Overview

A finished figure can look fine and still be wrong: a series plotted from the wrong column, a legend covering a data point, tick labels clipped at the axis edge, or a 150-dpi export that pixelates in print. This skill is a render-then-verify QA pass — it re-renders the figure and checks it against a concrete, measurable checklist before submission. It complements the plotting skills (which make figures); it does not create plots.

When to Use This Skill

Use this skill when the user wants to:

• Proof a finished figure for correctness and print-readiness before submission.
• Confirm the plot faithfully represents its data (every row/series accounted for).
• Catch overlapping or clipped labels, legends, and markers.
• Verify resolution / export settings (e.g. 300 dpi, vector where required).

Does NOT Trigger

Scenario	Use instead
Create/plot the figure in the first place	alterlab-matplotlib / alterlab-seaborn / alterlab-plotly
Lay out a multi-panel publication figure	alterlab-scientific-viz
Draw a schematic / diagram	alterlab-scientific-schematics
Build an infographic	alterlab-infographics

Core Capabilities

1. Data-fidelity check

Verify the rendered marks against every row of the source data: series count, point count per series, min/max/range on each axis, and that categorical labels match the data's categories. Flag any series plotted from the wrong column or a silently dropped subset.

2. Label floor-and-ceiling legibility

Check that tick labels, axis titles, and annotations are within legibility bounds — a font floor (nothing below the journal's minimum pt at final size) and a ceiling (titles not so large they crowd the panel) — and that nothing is clipped at the axis boundary.

3. Bounding-box collision detection

Compute the bounding boxes of text, legend, and markers and detect overlaps/collisions (legend over data, colliding labels, out-of-axes text). Report each collision with its location so it can be nudged.

4. Resolution and export QA

Confirm the export meets the target: ≥300 dpi for raster, vector format where the venue requires it, correct figure dimensions/column width, and embedded fonts. Fail the check if the raster resolution is below the floor.

5. Report

Emit a pass/fail checklist per criterion with the specific offending element(s), so the fix is actionable. See references/figure_qa_checklist.md for the full rubric and Matplotlib bbox recipes.

Resources

• references/figure_qa_checklist.md — the full QA rubric, Matplotlib renderer/bbox recipes for collision detection, dpi/vector export checks, and journal legibility floors. Loaded on demand.

Part of the AlterLab Academic Skills suite.