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Grok Bot+18 agentsPaper Figures: Publication-Ready Figures
Turn results into journal-ready scientific figures that never overstate the data: the right plot for the claim, a colour-blind-safe journal style, and a check before submission.
$9
Paper Figures: Publication-Ready Figures
Example session with this skill installed
Here are my dose-response results for two compounds (research/results/dose_response.json) and the per-condition measurements (research/results/conditions.json). Make Figure 2 for a Nature-family journal, single column.
- Read your context and instructions
- Compiled the paper figures
Figure plan (2 panels, 89 mm wide):
a Dose-response for compounds A and B: every replicate, mean with 95% CI, four-parameter logistic fit, log dose axis.
- b Fold change vs control as a dot plot with the mean and 95% CI, n printed under each condition.
Files written
research/figures/fig2.pdfandfig2.png(89 x 55 mm, Okabe-Ito colours, panel labels a and b)research/figures/fig2.md: caption draft, data sources, and how the figure was built
Checks
- Fonts: Type 42 embedded, no Type 3 (pass)
- Every interval is named in the caption; n is shown for every condition
- Caption headline reworded: "B is more potent" became "B shifts the curve right", because only point estimates of EC50 were provided.
Connects securely to your tools. The creator never sees your data.
About this skill
What it does
Your agent becomes a careful figure collaborator for scientific papers. Give it results, a data file, or just "we need a Figure 1", and it:
- Picks the plot that fits the claim: dot plots over bar charts, every interval named (SD, SEM or 95% CI), n shown, accurate axis ranges.
- Backs every claim with the data: no cherry-picked examples, schematics marked as schematics, equations checked against the code, captions that match the evidence.
- Applies a journal style with a bundled matplotlib helper: Okabe-Ito colour-blind-safe palette, exact column widths for Nature, Cell, PNAS, Elsevier and IEEE (or any width in mm), embedded Type 42 fonts, PDF and PNG export.
- Verifies before calling it done, including a font check that catches the Type 3 fonts many journals reject.
- Writes a caption and spec file next to every figure, so the figure can be rebuilt and the caption reviewed.
Two ways to work
- Unguided: hand over results and get a proposed figure plan, then the figure.
- Guided: you specify the panels and it builds and iterates with you.
What's included
SKILL.md: the full workflow, from choosing the plot to the final checkscripts/figure_style.py: style helper (Python 3.10+, matplotlib only)examples/make_example_figure.py: a complete two-panel example (dose-response with intervals and fit, plus a dot plot with n)references/matplotlib-traps.md: layout, text and export pitfalls and their fixes
Part of the Aurora Lab research pipeline
Works entirely on its own. If you keep results in a research/ folder (results/*.json), it reads them and writes figures to research/figures/, the same convention the other Aurora Lab research skills use. 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