figure-generation
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 this agent skill?
npx skills add https://github.com/lingzhi227/agent-research-skills --skill figure-generationIs this agent skill safe to install?
- Gen Agent Trust Hubfail
This skill implements a 'Generate-then-Execute' pipeline that creates and runs arbitrary Python code on the local system based on user-provided descriptions. This pattern is highly susceptible to prompt injection attacks where a malicious request could cause the agent to generate and execute code that deletes files, exfiltrates sensitive data, or installs malware.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
- Runlayerpass
3/3 files flagged
- ZeroLeakspass
Score: 93/100 · 2 sections analyzed
What does this agent skill do?
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)
- Generate a self-contained Python script using the template from
scripts/figure_template.pyas a starting point - Write script to a temp file and execute:
python figure_script.py - If error: capture traceback, feed back, regenerate (see ERROR_PROMPT in references)
- If no
.pngproduced: add explicit save instruction, retry - 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
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/lingzhi227/agent-research-skills/figure-generation">View figure-generation on skillZs</a>