# Sci Figure

> Use when the user provides Excel, CSV, or structured data and wants publication-grade scientific figures, charts, plotting, exploratory analysis, statistical annotations, captions, figure export, or a traceable analysis report for a paper, thesis, lab report, or formal submission.

- Skill: `liujiayi3703/sci-figure` (Agent Skill, multi-file: 9 files)
- Install (CLI): `npx skillmds@latest add liujiayi3703/sci-figure`
- Raw SKILL.md: https://api.skillmd.com/api/skills/liujiayi3703/sci-figure/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: liujiayi3703 (https://skillmd.com/u/liujiayi3703)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/liujiayi3703/sci-figure

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# Scientific Figure Standards

Use this skill to turn spreadsheet-style data into a reproducible, publication-grade figure workflow. Prioritize rigor, consistency, and traceability over speed.

## Core promises

1. **Compliant**: use academic typography, unit labeling, export settings, and restrained color choices.
2. **Rigorous**: document statistical methods, error bars, and significance marks when group comparisons are shown.
3. **Traceable**: save the intermediate tables that support each figure so another reviewer can trace numbers back to the source data.

## When to use this workflow

Run the full workflow when both are true:

- The input is one or more structured data files such as `.xlsx`, `.xls`, or `.csv`.
- The user wants analysis, charts, figure generation, figure cleanup, or a formal report rather than a one-line answer.

For tiny one-off questions such as "what is the mean of column B", answer directly and skip the full workflow.

## Workflow

Execute these steps in order. Skip only when the user explicitly asks for a narrower deliverable.

### Step 1: Define a configuration block

Before writing analysis code, place a configuration block at the top of the script so paths, output formats, fonts, statistical thresholds, and precision rules are controlled in one place.

Read `references/config_template.md` and copy the template into the script before adding analysis logic.

### Step 2: Choose a separate output directory

Never write outputs into the raw input directory unless the user explicitly asks for that layout and the risk is acceptable. Prefer a sibling or child output directory dedicated to the run.

Default pattern:

- Derive a short kebab-case slug from the task, such as `cell-growth` or `survey-comparison`.
- Create an output folder for the run.
- Create `figures/` and `process_data/` subfolders inside it.

Recommended structure:

```text
<output_dir>/
├── 00_data_health.md
├── report.pdf
├── figures/
│   ├── pdf/
│   ├── png/
│   └── jpg/
└── process_data/
```

Tell the user where outputs will be written before proceeding.

### Step 3: Diagnose data health first

Before plotting anything, generate `00_data_health.md` for each analysis dataset. This is mandatory because plotting bad data neatly still produces bad science.

The report should cover:

- Dataset shape and memory footprint
- Column dtypes, missingness, and unique counts
- Duplicate rows
- Descriptive statistics for numeric columns
- Normality and outlier checks where relevant
- Category summaries for non-numeric columns
- Correlation and VIF checks when multiple numeric predictors are involved

Read `references/data_health.md` for the code pattern and markdown layout.

After generating the report, summarize the important findings for the user in a couple of sentences. Pause before continuing if the data shows serious issues that would undermine the analysis.

### Step 4: Clean and export process data

Clean the data based on the health report. Typical actions include:

- Drop exact duplicates
- Strip whitespace from text fields
- Parse dates
- Coerce numeric-looking text into numeric columns
- Handle missing values in a way that matches the analysis goal

Save meaningful intermediate tables into `process_data/` as numbered `.xlsx` files. A table is meaningful if it supports a figure, result, or reviewer question later.

Read `references/xlsx_export.md` for naming and formatting patterns.

### Step 5: Run statistics only when justified

Only run inferential statistics when the figure is intended to compare groups or conditions. Do not add p-values to line charts, scatter plots, or single-distribution plots unless the design truly calls for them.

Use the decision rules in `references/statistics.md` to choose the test. If paired versus independent samples is ambiguous, ask the user before committing to a test.

Save raw statistical outputs into `process_data/05_statistical_tests.xlsx` or a similarly clear file name.

### Step 6: Draw figures in publication style

Generate figures with matplotlib unless the user explicitly requests another plotting stack. Keep the visual style restrained and reviewer-friendly.

Read `references/chart_style.md` before drawing the first chart. Apply these rules consistently:

- Use serif fonts with runtime fallback for mixed Chinese and English text.
- Prefer grayscale; only use categorical color when monochrome would fail.
- Put measurement units in parentheses in axis labels.
- Apply explicit number formatting rather than matplotlib defaults.
- Use error bars only when they represent a clearly named summary interval.
- Do not place decorative titles inside the chart area.

For each figure, save:

- `figures/pdf/fig_<NN>_<slug>.pdf`
- `figures/png/fig_<NN>_<slug>.png`
- `figures/jpg/fig_<NN>_<slug>.jpg`

Also save the exact plotting dataframe for each figure in `process_data/fig_<NN>_data.xlsx`.

### Step 7: Write complete captions

Every figure needs a caption that states:

1. What is shown
2. How it was summarized or tested
3. What conclusion the reader should take away

Use the patterns in `references/captions.md`. Do not stop at description only; the caption should help interpretation.

### Step 8: Bundle the figures into a report when requested

When the user wants a deliverable report, combine the figures and captions into `report.pdf`.

Read `references/pdf_layout.md` for a reportlab-based layout template. If reportlab is unavailable, state that limitation and use a simpler fallback such as a figure-only PDF.

### Step 9: Present results clearly

When the work is done, present the main outputs in a concise order:

- `report.pdf` if one was created
- `00_data_health.md`
- The main figure files
- The key process-data files or the `process_data/` directory

Summarize what was analyzed, what the figures show, and where the outputs live.

## What to avoid

- Do not skip the data health report just because the table looks clean.
- Do not add significance marks to plots that are not genuine group comparisons.
- Do not rely on default matplotlib colors for publication figures.
- Do not put the figure title inside the chart if the caption already carries that information.
- Do not save JPG only; always keep a high-quality PDF or PNG version.
- Do not write captions that merely restate the axes.

## Reference files

Load these only when the matching step is active:

- `references/config_template.md`
- `references/data_health.md`
- `references/xlsx_export.md`
- `references/statistics.md`
- `references/chart_style.md`
- `references/captions.md`
- `references/pdf_layout.md`