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Figure Generation skill

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.

$npx skills add lingzhi227/agent-research-skills --skill figure-generation
View this skill on GitHublingzhi227/agent-research-skills/skills/figure-generation/SKILL.md
Pack stars
462
Pack installs
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Alyn Score
49/99
Updated
9 months ago
– views – installs via Alyn

lingzhi227

Figure Generation

Research Science

Install Figure Generation

Pick your agent. Every command is ready to paste.

$npx skills add lingzhi227/agent-research-skills --skill figure-generation

The open skills CLI detects your agents (Claude Code, Cursor, Codex and 70+ more) and installs to the right folder. Add -g to install globally.

01

Install it

Run the command above, or upload the folder in Claude.

02

Ask naturally

Describe the task. Your agent loads figure-generation when the request matches its description.

03

Name it if needed

If it does not trigger, say “use the figure-generation skill”. In Codex, type $figure-generation.

The skill itself

SKILL.md

FolderGitHub

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

Tips for Figure Generation

  • • Start a fresh session after installing so your agent picks up the new skill.
  • • This skill ships scripts. Read them before the first run, and run it in a project without secrets first.
  • • It bundles reference files. The agent reads them only when needed, so they cost no context until then.
  • • It is part of a pack with 30 skills. Install only the ones you need with --skill to keep triggers precise.
  • • If two skills overlap, mention the one you want by name.

Questions about Figure Generation

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.

How do I install Figure Generation?

Run "npx skills add lingzhi227/agent-research-skills --skill figure-generation" in your project. The skills CLI installs it for Claude Code, Cursor, Codex and 70+ other agents. In Claude on the web, upload the skill folder as a ZIP under Customize > Skills.

Does Figure Generation work in Cursor and Codex?

Yes. Figure Generation uses the open SKILL.md format, so it works in Claude Code, Claude, Cursor, OpenAI Codex, Gemini CLI, GitHub Copilot and other agents that support agent skills.

Is Figure Generation free?

Figure Generation is open source on GitHub. It is free to install; check the license before commercial use.

Who made Figure Generation?

Figure Generation is published by lingzhi227 in the lingzhi227/agent-research-skills repository, together with 29 other skills.

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