figure-generationlisted

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.
sergeeey/Claude-cod-top-2026 · ★ 5 · AI & Automation · score 73

Install: claude install-skill sergeeey/Claude-cod-top-2026

# 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 ```bash 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-promp