Generate publication-ready figures and LaTeX tables from experiment results.
npx skills add https://github.com/Shallow-W/llm-wiki --skill paper-figure-shallow-w
Please help me install this Agent Skill. Skill: paper-figure Source: https://github.com/Shallow-W/llm-wiki/tree/main/.claude/skills/paper-figure Command: npx skills add https://github.com/Shallow-W/llm-wiki --skill paper-figure-shallow-w
It removes the time-consuming manual work of turning experiment results into clean, publication-ready figures and comparison tables for a paper.
Ask the paper-figure skill to generate the needed paper figures from your figure plan and experiment data by running: generate the paper figures using PAPER_PLAN.md and the available JSON/CSV data.
High-intent search queries and answers about installing and using this skill.
To generate publication-ready figures, provide your CSV data and PAPER_PLAN.md file. The skill creates per-figure scripts and outputs vector PDFs with LaTeX include snippets for consistent styling.
Creating LaTeX comparison tables from experiment results involves reading JSON or screen logs to structure ablations and method comparisons. The process produces LaTeX-ready tables that integrate directly into your paper manuscript.
For an ML paper, you can make line charts, bar charts, scatter plots, heatmaps, and box or violin plots. These scientific plots support multi-panel layouts and apply consistent publication styling defaults.
Yes, you can use JSON log files to plot training curves automatically. The workflow reads JSON inputs alongside PAPER_PLAN.md to produce reproducible per-figure scripts and vector PDF outputs without manual plotting.
The generated figure styling follows academic publication standards by enforcing consistent fonts, sizes, and print-friendly rules. It outputs vector PDFs and omits titles inside figures to meet professional formatting expectations.
To ensure reproducibility when generating multi-panel figures, the workflow creates dedicated per-figure generation scripts. This allows you to regenerate the exact same vector PDF outputs from your experiment data consistently.
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