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Polish Tables Figures

Community
ShaishavMaisuria
polish-tables-figures

Polishes LaTeX tables and figures to publication quality. Use when a researcher mentions table formatting, booktabs, figure layout, subfigures, captions, colorblind-safe colors or palettes, cref/cleveref consistency, overfull hbox, column widths, or asks to make tables and figures look professional or camera-ready for a venue (ACM, IEEE, NeurIPS, LNCS, CHI). Converts hline-and-vertical-rule tables to booktabs, sizes columns within the page budget, lays out subfigures with subcaption, applies caption conventions per venue family (table captions above, figure captions below, ACM Description alt-text), fixes ref/cref inconsistencies, and recommends colorblind-safe palettes (Okabe-Ito, Paul Tol) using bundled stdlib-Python linters and palette generators. Edits presentation only — never alters data values, units, or results.

Overview

PublisherShaishavMaisuria
Repositoryresearch-paper-lifecycle-skills
Skill namepolish-tables-figures
Stars
59
Forks
22
Bundled files
7
LicenseApache-2.0
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.

  • 7 bundled files

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

  • Open source

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

Installation

Install the Polish Tables Figures 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/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/polish-tables-figures
  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/ShaishavMaisuria/research-paper-lifecycle-skills.git /tmp/research-paper-lifecycle-skills
mkdir -p .claude/skills
cp -r /tmp/research-paper-lifecycle-skills/skills/polish-tables-figures .claude/skills/polish-tables-figures
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Polish Tables Figures 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 Polish Tables Figures 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 Polish Tables Figures 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.

Polish Tables & Figures

Turn draft-quality LaTeX floats into the tables and figures reviewers expect to see at ACM / IEEE / NeurIPS-style / LNCS venues: booktabs rules, columns that fit the page budget, clean subfigure layouts, venue-correct captions, consistent \cref references, and colorblind-safe colors. Every change is presentation-only; the numbers are sacred.

When to use

  • "Make my tables/figures look professional" / "polish for camera-ready"
  • "Convert this table to booktabs" / "my table is too wide" / "overfull hbox"
  • "Lay these plots out as subfigures" / "fix my captions"
  • "Are my figure colors colorblind-safe?" / "pick a palette for this plot"
  • "Fix my \ref/\cref mess"
  • After tailor-to-venue, before preflight-check.

Inputs

  1. The main .tex file (the one with \documentclass); \input/\include files are followed automatically.
  2. Optional: a venue profile venues/conferences/<venue>-<year>.yml (schema in venues/schema.yml) — sets column count and caption conventions. Without one, conventions are inferred from the documentclass.
  3. Strongly recommended: a compiled .log next to the .tex so the column-sizing (overfull box) check can run.

Process

  1. Resolve venue conventions. If a venue is named, load its profile (sanity-check with python3 scripts/venueyaml.py venues/conferences/<venue>.yml). Re-verify format-critical facts against the live cfp_url before relying on them: template/documentclass, column count, any stated figure/table rules (minimum font sizes, color/grayscale requirements, accessibility requirements). Profiles are a starting point, never ground truth.

  2. Lint everything first:

    python3 scripts/check_floats.py paper.tex \
        --venue venues/conferences/<venue>-<year>.yml

    Flags: --json, --strict (warnings also fail), --log <file>, --overfull-threshold <pt>, --no-inputs. Exit codes: 0 clean, 1 findings, 2 bad arguments. The linter covers: booktabs style (\hline, \cline, vertical rules, double rules, missing package), \resizebox/tiny-font scaling, caption position per family, missing \caption/\label/\centering/ACM \Description, label-before-caption, deprecated subfigure package, width=\textwidth in a one-column float at a two-column venue, raster graphics, [h]/[H] placement, undefined and unreferenced labels, mixed Figure~\ref vs \cref styles, "Figure \cref" double prefixes, cleveref load order, lowercase \cref at sentence start, and Overfull \hbox entries from the .log.

  3. Fix tables — work through findings using references/tables.md: the mechanical booktabs conversion recipe (it preserves cell content byte-for-byte), then column sizing within the page budget (cut → abbreviate → align numbers → \tabcolsep → tabularx → table* — never \resizebox).

  4. Fix figures — references/figures.md: width discipline (\columnwidth/\linewidth, figure* for spans), subfigure layout with subcaption, vector formats, font sizes inside plots, float placement.

  5. Fix captions and cross-references — references/captions-crossrefs.md: per-family caption position/style table, self-contained caption writing, and the cleveref setup (load last, capitalise, \cref vs \Cref, one style everywhere).

  6. Fix color — references/color-accessibility.md. Check the colors actually used in the paper (hex values from TikZ/pgfplots definitions, or sampled from included plots):

    python3 scripts/palettes.py check 4477AA EE6677 ...   # arbitrary colors
    python3 scripts/palettes.py show okabe-ito --format latex|pgfplots|matplotlib

    check simulates protanopia/deuteranopia/tritanopia and grayscale print, flags confusable pairs (exit 1), and the references file explains redundant encoding (markers + line styles, not color alone). If plots are generated by user scripts (matplotlib/pgfplots), edit the plotting source and ask the user to regenerate — never claim recolored results.

  7. Recompile and re-run the linter until the verdict is PASS (or every remaining WARN is a deliberate, user-approved choice). Then hand off to preflight-check for the full desk-reject lint.

Output

Edited .tex (and plotting-script) changes applied in place with a short change log per float: what changed and why, citing the venue convention. Plus the final linter report (PASS / PASS-WITH-WARNINGS / FAIL). No new files unless the user asks for extracted style snippets (e.g. a colors.tex palette preamble).

Adapt to your discipline

Conventions here target CS venues (ACM/IEEE/ML/LNCS). Forking for another field: adjust the caption-position conventions in references/captions-crossrefs.md and the family mapping in scripts/check_floats.py (convention_label); the booktabs, sizing, and color guidance is field-agnostic.

Guardrails

  • Never change data: cell values, units, significant digits, axis scales, error bars, and legends' meanings are untouchable. Reformatting only. If a value looks wrong, tell the user — do not "fix" it.
  • Never fabricate venue rules. The profile and the live CFP are the only sources; when neither states a rule, label advice as general convention.
  • Do not regenerate or recolor result plots yourself; edit the user's plotting source and let them re-run it.
  • Bold-best-result formatting must come from the user's stated criterion, not your reading of which number "looks best".
  • Citation issues found along the way go to verify-citations; submission readiness goes to preflight-check. Never submit anything on the user's behalf.

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 Polish Tables Figures AI skill do?

Polishes LaTeX tables and figures to publication quality. Use when a researcher mentions table formatting, booktabs, figure layout, subfigures, captions, colorblind-safe colors or palettes, cref/cleveref consistency, overfull hbox, column widths, or asks to make tables and figures look professional or camera-ready for a venue (ACM, IEEE, NeurIPS, LNCS, CHI). Converts hline-and-vertical-rule tables to booktabs, sizes columns within the page budget, lays out subfigures with subcaption, applies caption conventions per venue family (table captions above, figure captions below, ACM Description a...

Why use Polish Tables Figures on TypingMind?

Because you install it once and use it with any model. Polish Tables Figures 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 Polish Tables Figures in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/polish-tables-figures. 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 Polish Tables Figures?

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 Polish Tables Figures?

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

Is the Polish Tables Figures AI skill free?

Yes. It is published on GitHub by ShaishavMaisuria under the Apache-2.0 license. 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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