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Paper Write

Community
fcakyon
paper-write

Write, structure, or revise academic paper sections, keep notation consistent, refine figures and tables, and set up or fix the LaTeX build (TeX distribution, biber vs bibtex, venue templates, compilation). Use when the user says "write the abstract", "structure the methods", "improve this section", "notation consistency", "figure refinement", "paper structure", "setup latex", "biber vs bibtex", "latex compilation error", "install latex packages", "venue template", or "texlive setup".

Overview

Publisherfcakyon
Repositorylabrat
Skill namepaper-write
Stars
438
Forks
38
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Paper Write 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/fcakyon/labrat/tree/main/plugin/skills/paper-write
  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/fcakyon/labrat.git /tmp/labrat
mkdir -p .claude/skills
cp -r /tmp/labrat/plugin/skills/paper-write .claude/skills/paper-write
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Write 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 Paper Write 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 Paper Write 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.

Paper Write: academic paper writing and LaTeX setup

For LaTeX environment setup, biber vs bibtex, venue templates and compilation, follow references/latex.md.

Core Principles

  1. Precision over elegance. Every claim traces to code, data or a cited source.
  2. Conciseness. Remove words that don't add information.

Writing Process

  1. Read first. Always read the existing section before writing.
  2. Identify the claim. What is this paragraph trying to say?
  3. Find the evidence. Where in code/results does this come from?
  4. Write the text. State claim, present evidence, interpret.
  5. Verify. Re-read against source to catch any drift, then compile with the project pipeline from references/latex.md Step 5 and fix any error before the next edit.

Section-Specific Guidance

Abstract

  • Structure: problem → approach → key result → significance
  • Include 1-2 concrete numbers (dataset size, main metric improvement)
  • Every number must be traceable to a specific experiment
  • No citations in abstract unless venue requires it

Introduction

  • Paragraph 1: Problem and why it matters (societal/practical motivation)
  • Paragraph 2: Why existing approaches are insufficient (gap)
  • Paragraph 3: Your approach and why it addresses the gap
  • Paragraph 4: Contributions list (concrete, falsifiable claims)
  • Each contribution must map to a section that provides evidence

Related Work

  • Organize by theme/approach, not chronologically
  • For each group: what they do, what's missing, how your work differs
  • Be fair: acknowledge strengths of prior work, don't strawman
  • End each paragraph with how your work addresses the limitation

Methods

  • Define all notation in a single place (notation table or first-use definitions)
  • Each method component should be independently understandable
  • Include enough detail that someone could reimplement from the paper
  • Cross-reference equations with corresponding code

Experiments

  • Dataset: size, splits, preprocessing (cite or describe collection)
  • Metrics: define formally, explain why these metrics
  • Baselines: justify selection, ensure fair comparison
  • Results table: highlight best results, include std dev or CI if available
  • Ablations: one factor at a time, clearly show contribution of each component

Conclusion

  • Summarize contributions (not the entire paper)
  • State limitations honestly
  • Future work: specific and feasible, not vague

Notation Consistency Protocol

Define once, use identically everywhere. When writing or editing any section:

  1. Read existing notation definitions in the paper
  2. Use EXACTLY the same symbols. Do not introduce synonyms.
  3. If a new symbol is needed, check it doesn't clash with existing ones
  4. Maintain a notation table if the paper has one

Common pitfalls:

  • Using both $x$ and $\mathbf{x}$ for the same concept
  • Defining $N$ as dataset size in methods but using $n$ in experiments
  • Inconsistent subscript conventions (e.g., $f_i$ vs $f(i)$)

Figure Refinement Methodology

1. Specification Capture

Before generating or modifying any figure:

  • What data does it show? (exact source file/variable)
  • What message should the reader take away?
  • What are the hard constraints? (font size ≥ 8pt, column width, color scheme)
  • What aspects of the current version are correct and must be preserved?

2. Constraint Preservation

Across multiple rounds of revision, track constraints explicitly:

Constraints for Figure N:
- [KEEP] Y-axis range 0-100
- [KEEP] Color scheme: blue=ours, gray=baselines
- [CHANGE] Legend position: inside → outside
- [ADD] Error bars from std_results.json

3. Variant Generation

When exploring design alternatives:

  • Generate 2-3 variants side by side when feasible
  • Each variant changes ONE visual aspect
  • Let the user compare and choose, don't pick for them

4. Visual Verification

After generating any figure, ALWAYS:

  • Open the rendered image and look at it
  • Check data values, colors and axis ranges against the spec and constraint list above
  • Verify labels, legends, and annotations are correct
  • Confirm the takeaway message is clear from a glance

Output Format

When writing paper text:

  • Provide LaTeX-ready output that matches the paper's existing style
  • Include comments for any claim that needs verification: % TODO: verify this number
  • Flag any notation inconsistencies found during writing
  • Suggest specific improvements with before/after comparisons

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 Paper Write AI skill do?

Write, structure, or revise academic paper sections, keep notation consistent, refine figures and tables, and set up or fix the LaTeX build (TeX distribution, biber vs bibtex, venue templates, compilation). Use when the user says "write the abstract", "structure the methods", "improve this section", "notation consistency", "figure refinement", "paper structure", "setup latex", "biber vs bibtex", "latex compilation error", "install latex packages", "venue template", or "texlive setup".

Why use Paper Write on TypingMind?

Because you install it once and use it with any model. Paper Write 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 Paper Write in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fcakyon/labrat/tree/main/plugin/skills/paper-write. 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 Paper Write?

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 Paper Write?

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

Is the Paper Write AI skill free?

Yes. It is published on GitHub by fcakyon under the MIT 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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