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Grok Bot+18 agentsFigure Audit: Catch Figure Problems Before Reviewers Do
This skill turns your agent into a careful figure reviewer.
$9
Figure Audit: Catch Figure Problems Before Reviewers Do
Example session with this skill installed
Review Figure 3 before I submit. It's in research/paper/draft.pdf, and research/figures/fig3.py is the script that made it (the data files are next to it). It will print at double-column width, 183 mm. Tell me what a reviewer would object to, check it for colour-blind readers, and go ahead and redraw it without waiting for me.
- Read your context and instructions
- Compiled the figure audit
- Generated the document
Figure 3 isn't ready to submit. A reviewer would say both claims in the text that cites it depend on how it was drawn more than on the data.
What a reviewer would object to, worst first
1.
Panel a, cut-off bars. The y-axis starts at 66 %, so catalyst C's bars are about 1.6× taller than B's when the yield is only about 5 % higher.
2.
Panel b, two y-axes. The temperature axis is set to 59.5–66.5 °C, and that range is what makes the two curves sit on top of each other.
3.
"Outperformed all others" isn't supported with n = 3. C is not clearly separated from D in water (Welch t = 1.6) or from B in ethanol (t = 1.9).
4. The heat-transfer conclusion has no test of which way the cause runs. The outlet is up to 6 °C above the 60 °C jacket, so the reaction could be heating the reactor.
5.
Error bars aren't defined, n isn't stated, and there are no a/b panel letters although the text cites Fig. 3a and 3b.
6. The figure in the draft is a 100 ppi image; include the vector fig3.pdf instead.
Colour-blind readers: panel a fails. Red and green are the only cue separating water from ethanol, and they merge under simulated deuteranopia. Panel b gets by because one line is dashed.
Redraws (both at 183 mm, no audit errors, pass greyscale and all three simulations):
good.png: your layout, with every replicate plus mean ± SD, blue/orange plus marker shapes, and the twin axis split into two stacked panels.better.png: catalyst C in one accent colour, every replicate with its 95 % interval, solvents labelled on the plot.
Full report, scripts and check sheets are in research/reviews/figure-audit-fig3/. Your script, data and draft are untouched.
fig3.pdf
PDF · document
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
About this skill
What it does
A figure is read before its caption, so a truncated axis or a convenient second y-axis can oversell a result that the text then has to defend. This skill turns your agent into a careful figure reviewer. Point it at a figure (PNG, a PDF page, or the matplotlib script that drew it) and it:
- Pins down the claim the figure supports, from the caption and the sentence that cites it, and judges everything against that claim.
- Audits the plotting script automatically without overwriting your files: twin y-axes, bars that don't start at zero, legends far from the data, grids, rotated or crowded ticks, missing units and panel letters, error bars that need defining, and text below the minimum at the width the figure will actually print.
- Checks colour-blind and greyscale readers: one sheet with greyscale plus simulated protanopia, deuteranopia and tritanopia views, and a pairwise check of the script's data colours with the numbers.
- Checks the numbers behind the claim when the data are there: replicates, intervals, how far a truncated axis exaggerates a difference.
- Ranks every finding by harm (misleads, slows reading, clutter, polish) with the evidence and a concrete fix, and says which findings came from the automatic audit and which are judgement.
- Redraws the figure twice when it has the script and data: Good, a minimal fix in your own style, and Better, a message-first redesign with one accent colour, direct labels and a caption that says what to see. Both are re-audited and inspected before you see them.
Reviews figures; doesn't make them from scratch
Figure Audit reviews figures that already exist and the scripts behind them. It pairs well with Aurora Lab's Paper Figures (which designs figures from data) and Figure Editor (which assembles panels into one figure), but needs neither.
What's included
SKILL.md: the review workflow, a checklist in five groups (integrity, chart form, construction, colour, message), severity bands, report format and how to phrase a critique for a co-author or a referee reportscripts/figtools.py: renders PDF pages, crops panels, makes greyscale and colour-blind views and a comparison sheet, and audits matplotlib scripts headlessscripts/plotkit.py: matplotlib techniques for the redesign (direct labels, accent colour, replicates with mean and 95 % CI, 1-2-5 ticks)examples/: a deliberately flawed synthetic figure script with its data, and the report, minimal fix and redesign a real run produced
Part of the Aurora Lab research pipeline
Works entirely on its own. With a research/ folder it reads figures from research/figures/ and the manuscript from research/paper/, and writes each review to research/reviews/figure-audit-<figure>/. No skill calls another.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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Requires
Python
macOS