Fit Page Limit

Community

ShaishavMaisuria

fit-page-limit

Helps a paper hit its target length: recommends exactly what to compress when it is over the page limit, or what to add with substance when it is too short. Use when a researcher says "I'm over the page limit", "compress my paper", "what should I cut", "trim two pages", "my paper is too long", "help me fit NeurIPS's limit", "my paper is too short", "I have space left, what should I add", or "where is all my length going". Runs a section-by-section length analysis, then gives a prioritized, value-aware plan: which low-value sections to condense or move to an appendix, while protecting contributions, results, and citations, or which thin sections to develop. Never deletes evidence to fit and never pads to fill. Trigger words: page limit, compress, trim, cut down, too long, too short, over the limit, fit the limit, what to cut, what to add.

Overview

Publisher	ShaishavMaisuria
Repository	`research-paper-lifecycle-skills`
Skill name	`fit-page-limit`
Stars	59
Forks	22
Bundled files	2
License	Apache-2.0
Links	Skill source Repository Homepage

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

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/fit-page-limit
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/fit-page-limit

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/fit-page-limit .claude/skills/fit-page-limit

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fit Page Limit 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 Fit Page Limit 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 Fit Page Limit 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.

Fit Page Limit

Two sides of the same problem: a paper that is over the limit and needs careful cuts, or a paper that is under and needs useful substance rather than filler. This skill maps the draft section by section, then gives the author a prioritized plan for whichever direction they need.

It coordinates with polish-prose for sentence-level tightening, refactor-structure for larger reorganization, polish-tables-figures for visual layout, and preflight-check when the page limit comes from a venue rule. Run it after the draft is roughly complete.

When to use

The draft is over the limit and the author needs to know where the bulk is.
The draft is under the target length and the author wants to use space well.
The author asks for a length map: "where is all my length going?"

Inputs

The .tex source.
The real compiled page count, if available, to calibrate the estimate.
The target: a venue page limit or a length the author names.
The venue CFP or profile, if the target comes from a venue.

Process

Map the length. Run the analyzer from this skill directory; do not eyeball where the pages went.
```
bash
python3 scripts/section_budget.py paper.tex --target-pages 10 --current-pages 11
```
It reports per-section words, percentage of the paper, estimated pages, and the over/under gap. --current-pages calibrates words per page to the author's actual compile. Use --json for machine-readable output.
Confirm what counts. Re-verify the venue's limit and exclusions against the live CFP before relying on appendix moves or excluded sections. A stale page limit can desk-reject the paper.
Pick the right branch. Use references/resize-tactics.md to turn the map into a value-aware resize plan.

If over: compress value-aware, never blind

Rank sections by compressibility x low impact, and cut there first:

Redundancy: the same point repeated in the intro, method, and conclusion.
Over-long related work: condense paragraph-per-paper prose into clusters that state only the delta that matters.
Verbose method/setup: tighten with polish-prose; cut walkthroughs a figure already shows.
Move, do not delete: push proofs, extra results, or implementation detail to an appendix only when the venue excludes it.
Figures/tables: resize, merge subfigures, or drop a redundant baseline column with polish-tables-figures.

Protect contributions, headline results, and every citation. Present estimated savings per cut and let the author approve before editing. Never delete a result, claim, or reference to hit a number.

If under: expand with substance

A short, complete paper can be fine; say so. If there is real room to strengthen it, suggest substance: a missing ablation, deeper error analysis, limitations or threats to validity, clearer motivation, a worked example, or a related-work gap. Flag suggestions that would merely pad. Hand drafting to the writing skills.

Output

A length map and prioritized resize plan:

Over: ranked cut list, estimated savings, and protected material.
Under: substantive additions ranked by value.

Write durable outputs to the user's local paper-workspace/writing/ directory when a workspace exists. The skill recommends and explains; the author decides and edits.

Guardrails

Never delete results, claims, or citations to fit.
Never pad to fill.
Page numbers are estimates; tell the author to compile and check the real count.
Re-verify venue limits and exclusions against the live CFP before advising appendix moves.
Copilot, not pilot: propose the plan; the author approves and edits.

Memory

Uses the shared .paper-memory/ convention documented by paper-profile.

At start: read lessons.md for recurring overspend patterns, such as "related work always runs long".
At end: append `date - fit-page-limit - over/under - where the length went
- what was cut/added` after deduping.
Create .paper-memory/ on demand in the user's paper directory. It is local only and should usually be ignored by git.

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 Fit Page Limit AI skill do?

Why use Fit Page Limit on TypingMind?

Because you install it once and use it with any model. Fit Page Limit 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 Fit Page Limit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/fit-page-limit. 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 Fit Page Limit?

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 Fit Page Limit?

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

Is the Fit Page Limit 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Fit Page Limit

When to use

Inputs

Process

If over: compress value-aware, never blind

If under: expand with substance

Output

Guardrails

Memory

Bundled files

Frequently asked questions

Related AI Skills

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