myzhao0114-del/scientific-figure-skill

★ 16Audience · researcherComplexity · 2/5ActiveSetup · easy

Mindmap

mindmap
  root((repo))
    What it does
      Data health checks
      Auto-formats figures
      Exports multiple formats
      Generates captions
    Key features
      Font management
      Color palettes
      P-value formatting
      Error bar rules
    Use cases
      Journal paper figures
      Thesis charts
      Defense slides
      Research plots
    Tech stack
      OpenAI Codex
      Python plotting
      Excel data
    Audience
      Graduate students
      Researchers
      Scientists

mindmap root((repo)) What it does Data health checks Auto-formats figures Exports multiple formats Generates captions Key features Font management Color palettes P-value formatting Error bar rules Use cases Journal paper figures Thesis charts Defense slides Research plots Tech stack OpenAI Codex Python plotting Excel data Audience Graduate students Researchers Scientists

Things people build with this

USE CASE 1

Generate publication-ready figures from raw data by describing what you want in plain language to the Codex agent.

USE CASE 2

Automatically check data quality and produce a health report covering missing values, outliers, and distribution issues before plotting.

USE CASE 3

Create figures with consistent fonts, colors, and statistical notation across an entire thesis or paper without manual formatting.

USE CASE 4

Export figures in multiple formats (PDF, PNG, JPG) with structured captions and traceable intermediate data tables.

Tech stack

PythonOpenAI CodexMatplotlibPandasExcel

Getting it running

Difficulty · easy Time to first run · 5min

Requires OpenAI Codex access and a git clone into the Codex skills directory; no additional dependencies or configuration needed.

License information not provided in the explanation.

In plain English

scientific-figure-skill is a packaged set of rules and templates for making scientific charts that look ready for a journal paper, a thesis, or a defense slide deck. The author is a researcher who got tired of AI-generated figures that look fine at first glance but fall apart on closer inspection: mixed Chinese and English fonts, missing axis units, loud colors, sloppy p-value formatting, vague error bars, and captions that describe a plot but never state a conclusion. The project packages years of figure-fixing experience as a reusable skill. The skill is built for Codex, OpenAI's coding agent. Installation is one git clone command into the Codex skills directory. After that, the user gives the agent a short instruction in plain language, for example, use this Excel file to make a regression figure, or fit a GAM on AQI and AI and make the plot. The skill handles the rest, instead of asking the user to spell out every formatting rule. The default behavior bakes in a long list of conventions. Fonts are split into a Chinese family and an English family with cross-platform fallbacks. Colors default to restrained, low-noise palettes, with the colorblind-friendly Okabe-Ito set when more colors are needed. P-values follow standard rules such as p < 0.001 instead of p = 0.0000, and significance is marked with the usual asterisks plus ns. Error bars and captions are pushed toward one-pass correctness, and figure borders are drawn on all four sides rather than only on the left and bottom. Before plotting, the skill runs a data health check. It writes a report file called 00_data_health.md that covers missing values, outliers, collinearity by VIF, distributions, and normality. It then cleans the data, saves intermediate tables as xlsx so every number on the final chart can be traced back to a source row, draws the figure under a unified style, and exports PDF, PNG, and JPG copies along with a structured caption. The project ships a SKILL.md, references for chart style, captions, statistics, PDF layout, and xlsx export, plus an agent config for OpenAI. It is aimed at graduate students and researchers who want plotting workflows to be more standardized.

Copy-paste prompts

Prompt 1

Use this Excel file to make a regression figure with proper axis labels, error bars, and p-value notation following scientific standards.

Prompt 2

Fit a GAM on AQI and AI data and create a plot with the Okabe-Ito colorblind-friendly palette and a caption that states the conclusion.

Prompt 3

Generate a data health report for my dataset, then create a figure showing the relationship between these two variables with all formatting rules applied.

Prompt 4

Make a multi-panel figure for my thesis defense with consistent fonts, restrained colors, and captions that explain what each panel shows and why it matters.

Prompt 5

Create a publication-ready chart from this data with Chinese and English font support, proper significance markers, and export it as PDF and PNG.

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Generated 2026-05-22 · Model: sonnet-4-6 · Verify against the repo before relying on details.