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Table Generation

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Generate publication-quality LaTeX tables from experimental results. Convert JSON/CSV data to booktabs-styled tables with bold best results, multi-row layouts, and proper captions. Use when creating result tables, comparison tables, or ablation tables for papers.

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  • Added September 2, 2026
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Scanned September 2, 2026

npx -y skills add sergeeey/Claude-cod-top-2026 --skill table-generation --agent claude-code

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SKILL.md
---
name: table-generation
description: Generate publication-quality LaTeX tables from experimental results. Convert JSON/CSV data to booktabs-styled tables with bold best results, multi-row layouts, and proper captions. Use when creating result tables, comparison tables, or ablation tables for papers.
argument-hint: [data-source]
---

# Table Generation

Convert experimental results into publication-ready LaTeX tables.

## Input

- `$0` — Table type: `comparison`, `ablation`, `descriptive`, `custom`
- `$1` — Data source: JSON file, CSV file, or inline data

## Scripts

### Generate LaTeX table from JSON/CSV
```bash
python ~/.claude/skills/table-generation/scripts/results_to_table.py \
  --input results.json --type comparison \
  --bold-best max --caption "Performance comparison" \
  --label tab:main_results
```

Supports: `comparison`, `ablation`, `descriptive`, `multi-dataset` table types.
Additional flags: `--type multi-dataset` for methods x datasets x metrics layout, `--significance` for p-value stars, `--underline-second` for second-best results.

## References

- LaTeX table templates and examples: `~/.claude/skills/table-generation/references/table-templates.md`

## Table Types

### `comparison` — Main results table
- Rows = methods (baselines + ours), Columns = metrics/datasets
- Bold the best result in each column
- Include mean +/- std when available
- Use `\multirow` for method categories (Supervised, Self-supervised, etc.)

### `ablation` — Ablation study table
- Rows = variants (full model, minus component A, minus component B, ...)
- Columns = metrics
- Bold the full model result
- Use checkmarks for component presence

### `descriptive` — Dataset/statistics table
- Dataset characteristics, hyperparameters, or summary statistics
- Clean formatting with proper units

### `custom` — Free-form table
- User specifies layout and content

## Required Packages
```latex
\usepackage{booktabs}    % \toprule, \midrule, \bottomrule
\usepackage{multirow}    % \multirow
\usepackage{multicol}    % multi-column layouts
\usepackage{threeparttable}  % table notes
```

## Output Format

Always generate tables with:
1. `booktabs` rules (`\toprule`, `\midrule`, `\bottomrule`)
2. `\caption{}` and `\label{tab:...}`
3. Bold best results using `\textbf{}`
4. Table notes via `threeparttable` when needed
5. Proper alignment (`l` for text, `c` or `r` for numbers)

## Rules

- Only include numbers from actual experimental logs — never hallucinate results
- All numbers must match the data source exactly
- Use `$\pm$` for standard deviations
- Use `\underline{}` for second-best results when appropriate
- Keep tables compact — avoid unnecessary columns
- Use `table*` for wide tables spanning two columns
- Add glossary/notes for abbreviated column headers

## Related Skills
- Upstream: [data-analysis](../data-analysis/), [experiment-code](../experiment-code/)
- Downstream: [paper-compilation](../paper-compilation/)
- See also: [figure-generation](../figure-generation/)

Files in this skill

  • SKILL.md3 KB
  • plugin.json387 B
  • references/table-templates.md5.7 KB
  • scripts/results_to_table.py13.1 KB

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