360 Secure SkillHub
360 安全技能中心

figure-generation

该 Skill 将图表描述扩展为绘图规范，生成并执行 matplotlib/seaborn Python 脚本，失败时重试，再通过视觉反馈检查并优化。支持柱状图、训练曲线、热力图、消融图、折线图、散点图、雷达图、小提琴图、t-SNE 和注意力图，输出 300 DPI PNG、矢量 PDF 及 LaTeX 插图代码。

lingzhi227
热度 0.00
最近更新 2026-10-19 01:34:53
分类：创作依赖：代码执行 / 读写文件 / 内部调用大模型应用场景：图像生成 / 数据处理 / 研究 / 代码生成

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add lingzhi227/agent-research-skills/figure-generation
Metadata
name
figure-generation
description
Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, code generation with execution, VLM visual feedback). Handles bar charts, line plots, heatmaps, training curves, ablation plots, and more. Use when the user needs figures, plots, or visualizations for a paper.
argument-hint
["figure-description"]

Scientific Figure Generation

Generate publication-quality figures for research papers.

Input

  • $0 — Description of the desired figure
  • $1 — (Optional) Path to data file (CSV, JSON, NPY, PKL) or results directory

Scripts

Generate figure template

python ~/.claude/skills/figure-generation/scripts/figure_template.py --type bar --output figure_script.py --name comparison
python ~/.claude/skills/figure-generation/scripts/figure_template.py --list-types

Available types: bar, training-curve, heatmap, ablation, line, scatter, radar, violin, tsne, attention

Three-Phase Pipeline (from MatPlotAgent)

Phase 1: Query Expansion

Expand the user's figure description into step-by-step coding specifications using the prompts in references/figure-prompts.md. Determine: figure type, data mapping (x/y/color/hue), style requirements, paper conventions.

Phase 2: Code Generation with Execution Loop (up to 4 retries)

  1. Generate a self-contained Python script using the template from scripts/figure_template.py as a starting point
  2. Write script to a temp file and execute: python figure_script.py
  3. If error: capture traceback, feed back, regenerate (see ERROR_PROMPT in references)
  4. If no .png produced: add explicit save instruction, retry
  5. On success: report the generated figure path

Phase 3: Visual Refinement

Read the generated PNG file and visually inspect using the VLM feedback prompts from references/figure-prompts.md:

  • Does the figure type match the request?
  • Are labels, titles, and legends correct?
  • Is the color scheme appropriate and consistent?
  • Are axis scales sensible? Is text readable at publication size?

If improvements needed: generate corrective instructions and re-execute.

References

  • All MatPlotAgent prompts: ~/.claude/skills/figure-generation/references/figure-prompts.md
  • Figure templates: ~/.claude/skills/figure-generation/scripts/figure_template.py

Output

Both PNG (preview, 300 DPI) and PDF (vector, for paper) formats. Plus the LaTeX include code:

\begin{figure}[t]
    \centering
    \includegraphics[width=\linewidth]{figures/figure_name.pdf}
    \caption{Description. Best viewed in color.}
    \label{fig:figure_name}
\end{figure}

Quality Requirements

  • DPI ≥ 300, or vector PDF
  • Colorblind-friendly palette (no red-green only)
  • All text ≥ 8pt at print size
  • Consistent styling across all paper figures
  • No matplotlib default title — use LaTeX caption

Related Skills