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Skills/lingzhi227/agent-research-skills/figure-generation
figure-generation logo

figure-generation

lingzhi227/agent-research-skills
1K installs140 stars
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Installation

npx skills add https://github.com/lingzhi227/agent-research-skills --skill figure-generation

Summary

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.

SKILL.md

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

  • Upstream: data-analysis, experiment-code
  • Downstream: paper-writing-section, paper-compilation, slide-generation
  • See also: table-generation

Score

0–100
63/ 100

Grade

C

Popularity15/30

1,013 installs — growing adoption.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Figure Generation skill score badge previewScore badge

Markdown

[![Figure Generation skill](https://www.remoteopenclaw.com/skills/lingzhi227/agent-research-skills/figure-generation/badges/score.svg)](https://www.remoteopenclaw.com/skills/lingzhi227/agent-research-skills/figure-generation)

HTML

<a href="https://www.remoteopenclaw.com/skills/lingzhi227/agent-research-skills/figure-generation"><img src="https://www.remoteopenclaw.com/skills/lingzhi227/agent-research-skills/figure-generation/badges/score.svg" alt="Figure Generation skill"/></a>

Figure Generation FAQ

How do I install the Figure Generation skill?

Run “npx skills add https://github.com/lingzhi227/agent-research-skills --skill figure-generation” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Figure Generation skill do?

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. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Figure Generation skill free?

Yes. Figure Generation is a free, open-source skill published from lingzhi227/agent-research-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Figure Generation work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Figure Generation works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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