SciPilot Skills family. Scientific data visualization advisor — thinks first, plots second. SciPilot Skills 家族成员 — 科研数据可视化顾问，先思考后绘制。

A Claude Code / Codex / Cursor Skill that does two things in order: (1) profiles your data and recommends the right chart for the argument you want to make, (2) renders it at publication grade for Nature / Science / IEEE / Elsevier / PNAS / Chinese journals. Built on matplotlib + seaborn + SciencePlots (static) and plotly (interactive), with CJK font auto-configuration so Chinese text never renders as boxes.

中文文档 | English

科研工作者最大的画图痛点往往不是"不会用 matplotlib"，而是"手上一堆数据，不知道该用什么图把结论讲清楚"。scipilot-figure-skill 是 SciPilot Skills 家族的第二个成员，专做这件事——

它的首要能力是【思考与判断】，其次才是【绘制】。

普通画图工具           scipilot-figure-skill
─────────────         ─────────────────────
你说 "画柱状图"  →   先 EDA：每列类型、样本量、分布、异常值、相关性
直接 plt.bar()        再问："你想论证什么？组间差异？关系？趋势？"
                     按数据特征 + 论证目标查决策框架推荐图型
                     n=5 想画均值柱时主动拦截 → 改用 stripplot
                     维度 > 12 时建议拆图，不硬塞
                     最后才进入绘制 → 期刊规范 → 自检 → 导出

0. 理解任务   ── 这张图要论证什么？数据在哪？
   ↓
1. 剖析数据   ── profile_data.py：列类型/样本量/分布/异常/相关
   ↓
2. 选图       ── chart_selection.md：按数据形态+论证目标决策
   ↓ (主动拦截 → viz_pitfalls.md)
3. 查规范     ── journal_specs.md：栏宽/字号/DPI/字体
   ↓
4. 配环境     ── setup_style.py：期刊预设+CJK字体
   ↓
5. 绘制       ── plot_recipes.md：9 类图配方
   ↓
6. 自检闭环   ── 程序自检 visual_qa + AI 读图 visual_review.md（缺字/裁切/重叠/对齐→回改）
   ↓
7. 导出       ── export_figure.py：多格式+按最终尺寸+灰度预览

普通画图工具画完就结束——没人回看成图，于是中文方框、文字被裁、图例压数据、子图编号乱放，全留到投稿才暴露。v2.1 补上出图后的闭环：

绘制 → 渲染 PNG → 程序自检(缺字/裁切/重叠) + AI 读图(遮盖/子图对齐/灰度)
                            ↓ 发现问题
        回到对应步骤改图 → 重渲 → 再读，直到通过

• 程序抓确定性问题：visual_qa.audit_layout() 同时盯 warning + logging 两条通道拦截缺字乱码，并查文字越界裁切、刻度标签重叠。
• AI 读图抓感知性问题：渲成 PNG 后用多模态读图能力核对图例是否压数据、子图 a/b/c 是否对齐、配色灰度可分——这些程序查不出。
• 子图标签一行对齐：layout_tools.add_panel_labels(fig) 用统一 figure 坐标让 a/b/c/d 横竖成线，不再手摆易错位。
• 字体兜底：setup_style 全模式默认修负号方框 + 自动配 CJK 字体，从源头少出乱码。

详见 references/visual_review.md。

1. 按最终尺寸出图不二次缩放 — figsize=(3.5, 2.625) 直接定 Nature 单栏
2. 矢量优先 — 数据图走 PDF / SVG / EPS；照片才用 TIFF/PNG；绝不 JPEG
3. 配色对色盲友好 — 默认 Okabe-Ito，加冗余编码，出图前查灰度
4. 字号在最终尺寸下可读 — 7-9 pt，最小 6 pt
5. 误差必有交代 — 图注必须写清 SD/SEM/95%CI + n + 检验方法

完整 15 条在 references/viz_pitfalls.md。

• P1：n<10/组的均值柱掩盖分布 → 改箱线 + stripplot
• P2：双 Y 轴的"相关"是作图者捏造的 → 拆子图或标准化
• P3：饼图与 3D 图 → 横向柱状
• P4：Y 轴不当截断 → 从 0 起或断裂明示
• P6：x 是分类用折线连均值 → 散点 / 柱状
• P14：rainbow / jet 色图 → viridis / RdBu_r
• P12：一图多论点 → 拆图，一图一结论
• P16：中文/负号变方框 → setup_style 配 CJK + 关 unicode_minus，导出前 visual_qa 拦截
• P18：子图 a/b/c 乱放 → add_panel_labels 统一对齐

请帮我安装这个 Skill：https://github.com/Haojae/scipilot-figure-skill.git

或手动：

git clone https://github.com/Haojae/scipilot-figure-skill.git \
          ~/.claude/skills/scipilot-figure-skill
pip install -r ~/.claude/skills/scipilot-figure-skill/requirements.txt

SciencePlots / pypdf / kaleido 是可选增强，缺失时不影响运行。

setup_style(lang='zh') 按优先级查找：

Noto Sans CJK SC > Source Han Sans SC > SimHei > Microsoft YaHei

找不到任何 CJK 字体时抛出清晰的安装提示。中文期刊"宋体 + Times New Roman 数字"混排：传 serif_for_zh=True。

例 1：用户只丢一个 CSV

我有这份 results.csv，帮我画成论文图。

→ Skill 不会直接画。会先：

1. 问"你这份数据主要想说服读者相信什么？"
2. 跑 profile_data.py 输出剖析报告
3. 给出推荐图型 + 理由 + 备选
4. 用户确认后才进入绘制流程

例 2：用户指定的图型不合适

帮我画 3 组各 5 个样本的均值柱状图。

→ Skill 主动拦截：

n=5 太小，均值柱掩盖分布——审稿人很可能要求"show individual points"。 我建议改成 箱线 + stripplot 叠加每个点：5 个点直接可见。要按原方案画，还是改？

例 3：多面板组合

给我画 Figure 1：4 panel，分别是 PCA、loss、混淆矩阵、生存曲线。
Nature 双栏。

→ 各 panel 字号配色统一，子图标签 a/b/c/d 加粗左上，导出 PDF + 灰度预览。

例 4：带显著性标注

3 组数据画箱线 + 显著性。

→ 先确认 n、检验方法、是否多重校正；图注必须写清统计内容。

# 数据剖析（思考的起点）
python scripts/profile_data.py results.csv --group group --group condition

# 列可用 CJK 字体
python scripts/setup_style.py --list-fonts

# 演示导出
python scripts/export_figure.py demo --out ./test_demo

# 合规自检
python scripts/check_figure.py figs/*.pdf --min-dpi 300 --strict

MIT © 2026 Haojae

The hardest part of scientific plotting is rarely "I don't know matplotlib" — it's "I have data, I don't know which chart conveys my conclusion." scipilot-figure-skill is the second member of the SciPilot Skills family, built around this insight: think first, plot second.

Generic plotter            scipilot-figure-skill
──────────────             ──────────────────────
"plot a bar chart"  →    Profiles data first: types, n, distribution, outliers
plt.bar()                Asks: "What argument do you want this figure to make?"
                         Decides chart type from data shape + intent
                         Refuses bad choices (n=5 mean bar → stripplot instead)
                         Suggests splitting figures when dimensions exceed 12
                         Only then renders → spec → audit → export

0. Understand   — what does this figure argue? where is the data?
   ↓
1. Profile      — profile_data.py: types / n / distribution / outliers / corr
   ↓
2. Select       — chart_selection.md: decision framework by shape + intent
   ↓ (active interception → viz_pitfalls.md)
3. Spec         — journal_specs.md: column width / font / DPI
   ↓
4. Style        — setup_style.py: journal preset + CJK font config
   ↓
5. Plot         — plot_recipes.md: 9 recipe families
   ↓
6. Self-check   — visual_qa (program) + AI reads the PNG (visual_review.md): glyphs/clipping/overlap/alignment
   ↓
7. Export       — export_figure.py: multi-format + final size + grayscale

Generic plotters stop at "saved" — nobody looks at the result, so CJK tofu boxes, clipped labels, legends covering data, and misaligned panel letters all survive to submission. v2.1 closes the loop after rendering:

plot → render PNG → program audit (glyphs/clipping/overlap) + AI reads image (occlusion/panel alignment/grayscale)
                              ↓ issues found
       go back, fix, re-render, re-read — until clean

• Program catches deterministic issues: visual_qa.audit_layout() traps missing-glyph warnings (both warnings and logging channels), text clipped off-canvas, and overlapping tick labels.
• AI catches perceptual issues: after rasterizing to PNG, the skill reads the image to check whether the legend covers data, whether panel letters a/b/c line up, and whether colors stay distinct in grayscale.
• One-call panel alignment: layout_tools.add_panel_labels(fig) places a/b/c/d in unified figure coordinates so they align both ways.
• Font safety net: setup_style fixes the minus-sign box in all modes and auto-configures CJK fonts.

See references/visual_review.md.

1. Render at final size, never rescale — set figsize=(3.5, 2.625) directly
2. Vectors first — PDF / SVG / EPS for data figures; never JPEG
3. Colorblind-safe palette — Okabe-Ito default + redundant encoding
4. Readable type at final size — 7-9 pt body, 6 pt minimum
5. Errors must be explained — captions state SD/SEM/CI + n + test type

Full 15 in references/viz_pitfalls.md.

• P1: mean-only bar charts with n<10 hide distributions → use box + stripplot
• P2: dual-Y axis fabricates correlations → split panels or standardize
• P3: pie charts and 3D bars → horizontal bar
• P14: rainbow / jet colormaps → viridis / RdBu_r
• P12: one figure with five points → split, one figure per claim
• P16: CJK / minus-sign tofu boxes → setup_style configures CJK + disables unicode_minus; visual_qa blocks leftover missing glyphs
• P18: scattered panel letters → add_panel_labels aligns a/b/c in one call

Please install this Skill for me: https://github.com/Haojae/scipilot-figure-skill.git

Manual:

git clone https://github.com/Haojae/scipilot-figure-skill.git \
          ~/.claude/skills/scipilot-figure-skill
pip install -r ~/.claude/skills/scipilot-figure-skill/requirements.txt

Example 1: bare CSV

I have results.csv. Plot it for my paper.

The skill will: profile the data, ask what argument the figure should make, recommend a chart with rationale + alternatives, and only then plot.

Example 2: bad request, gentle refusal

Plot a bar chart of group means: 3 groups, 5 samples each.

The skill will reply:

With n=5 per group a mean bar chart hides the distribution and reviewers typically ask for individual data points. I recommend box plot + stripplot overlay — all five points are visible and the distribution is clear. Stick with bars, or switch?

Example 3: multi-panel composite

Figure 1, 4 panels: PCA, loss curves, confusion matrix, survival.
Nature double column.

Example 4: statistical comparison

3 groups, box plot with significance.

MIT © 2026 Haojae

matplotlib>=3.7
seaborn>=0.13
plotly>=5.18
pandas>=2.0
numpy>=1.24
scipy>=1.10
Pillow>=10.0
SciencePlots>=2.1   # optional
pypdf>=4.0          # optional
kaleido>=0.2.1      # optional
PyMuPDF>=1.23       # optional; only for visual_qa.render_preview() of saved PDFs

Python 3.9+ recommended.