Publication-Ready Figures & Tables
Everything between your results and the figures and tables a journal accepts. Four skills that each work on their own and cooperate through one shared research/ folder:
- Paper Figures: designs figures from your results, with the right plot for the claim, named intervals, n on the figure and a colour-blind-safe journal style.
- Figure Editor: assembles finished panels into one labelled multi-panel figure at journal width, with placeholders, resolution warnings and a caption draft.
- Figure Audit: reviews existing figures and their plotting scripts, with colour-blind and greyscale checks, and proposes a quick fix and a redesign.
- Table Design: turns results into publication-ready tables in LaTeX booktabs, Markdown or HTML, with sensible rounding and mean ± SD.
Documentation for every skill: https://the-aurora-lab.github.io/#skills
Works with every agent that reads SKILL.md — Claude Code, Cursor, Codex CLI, Gemini CLI, GitHub Copilot, Windsurf, OpenClaw, and more.
One payment, lifetime access. 4 skills unlock instantly in your library.
What's included
4 skills
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.
Turn the plots, micrographs and diagrams you already have into one labelled multi-panel figure at exact journal width, with placeholders for missing panels, a print-resolution check and a caption draft.
Review your research figures before submission, or a manuscript's as a referee: what misleads, what slows the reader, what fails colour-blind and greyscale readers. Ranked findings with evidence, a minimal fix and a redesign, both redrawn.
Turn a results CSV into the table your paper needs: booktabs LaTeX plus a Markdown copy for the README. Methods × datasets, mean ± SD rounded to match, best values in bold, missing runs marked and explained, rendered and checked.
See it in action
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.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads these skills and uses them on your task. It always asks before any payment.
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