Table Generation logo

Table Generation

Community
lingzhi227
table-generation

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.

Overview

Publisherlingzhi227
Repositoryagent-research-skills
Skill nametable-generation
Stars
426
Forks
46
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by lingzhi227 on GitHub. Read the source before you install it.

Installation

Install the Table Generation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/lingzhi227/agent-research-skills/tree/main/skills/table-generation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/lingzhi227/agent-research-skills.git /tmp/agent-research-skills
mkdir -p .claude/skills
cp -r /tmp/agent-research-skills/skills/table-generation .claude/skills/table-generation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Table Generation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Table Generation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Table Generation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Table Generation AI skill do?

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.

Why use Table Generation on TypingMind?

Because you install it once and use it with any model. Table Generation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Table Generation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lingzhi227/agent-research-skills/tree/main/skills/table-generation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Table Generation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Table Generation?

As many as you like. As long as a model supports skills, you can use Table Generation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Table Generation AI skill free?

It is published on GitHub by lingzhi227. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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