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Paper Compile

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wanshuiyin
paper-compile

Compile LaTeX paper to PDF, fix errors, and verify output. Use when user says "编译论文", "compile paper", "build PDF", "生成PDF", or wants to compile LaTeX into a submission-ready PDF.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill namepaper-compile
Stars
16.3K
Forks
1.4K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Paper Compile 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.
  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/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep
mkdir -p .claude/skills
cp -r /tmp/Auto-claude-code-research-in-sleep/skills/paper-compile .claude/skills/paper-compile
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paper Compile 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 Compile 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 Compile 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 Compile: LaTeX to Submission-Ready PDF

Compile the LaTeX paper and fix any issues: $ARGUMENTS

Constants

  • COMPILER = latexmk — LaTeX build tool. Handles multi-pass compilation automatically.
  • ENGINE = pdflatex — LaTeX engine. Options: pdflatex (default), xelatex (for CJK/custom fonts), lualatex.
  • MAX_COMPILE_ATTEMPTS = 3 — Maximum attempts to fix errors and recompile.
  • PAPER_DIR = paper/ — Directory containing LaTeX source files.
  • MAX_PAGES — Page limit. ML conferences: main body to Conclusion end (excluding references & appendix). ICLR=9, NeurIPS=9, ICML=8. IEEE venues: references ARE included in page count. IEEE journal ≈ 12-14 pages, IEEE conference ≈ 5-8 pages (all inclusive).

Workflow

Step 1: Verify Prerequisites

Check that the compilation environment is ready:

bash
# Check LaTeX installation
which pdflatex && which latexmk && which bibtex

# If not installed, provide instructions:
# macOS: brew install --cask mactex-no-gui
# Ubuntu: sudo apt-get install texlive-full
# Server: conda install -c conda-forge texlive-core

Verify all required files exist:

bash
# Must exist
ls $PAPER_DIR/main.tex

# Should exist
ls $PAPER_DIR/references.bib
ls $PAPER_DIR/sections/*.tex
ls $PAPER_DIR/figures/*.pdf 2>/dev/null || ls $PAPER_DIR/figures/*.png 2>/dev/null

Step 2: First Compilation Attempt

bash
cd $PAPER_DIR

# Clean previous build artifacts
latexmk -C

# Full compilation (pdflatex + bibtex + pdflatex × 2)
latexmk -pdf -interaction=nonstopmode -halt-on-error main.tex 2>&1 | tee compile.log

Step 3: Error Diagnosis and Auto-Fix

If compilation fails, read compile.log and fix common errors:

Missing packages:

! LaTeX Error: File `somepackage.sty' not found.

→ Install via tlmgr install somepackage or remove the \usepackage if unused.

Undefined references:

LaTeX Warning: Reference `fig:xyz' on page 3 undefined

→ Check \label{fig:xyz} exists in the correct figure environment.

Missing figures:

! LaTeX Error: File `figures/fig1.pdf' not found.

→ Check if the file exists with a different extension (.png vs .pdf). Update the \includegraphics path.

Citation undefined:

LaTeX Warning: Citation `smith2024' undefined

→ Add the missing entry to references.bib or fix the citation key.

[VERIFY] markers in text: → Search for [VERIFY] markers left by /paper-write. These indicate unverified citations or facts. Search for the correct information or flag to the user.

Overfull hbox:

Overfull \hbox (12.5pt too wide) in paragraph at lines 42--45

→ Minor: usually ignorable. If severe (>20pt), rephrase the text or adjust figure width.

BibTeX errors:

I was expecting a `,' or a `}'---line 15 of references.bib

→ Fix BibTeX syntax (missing comma, unmatched braces, special characters in title).

\crefname undefined for custom theorem types: → Ensure \crefname{assumption}{Assumption}{Assumptions} and similar are in the preamble after \newtheorem{assumption}.

Step 4: Iterative Fix Loop

for attempt in 1..MAX_COMPILE_ATTEMPTS:
    compile()
    if success:
        break
    parse_errors()
    auto_fix()

For each error:

  1. Read the error message from compile.log
  2. Locate the source file and line number
  3. Apply the fix
  4. Recompile

Stuck after 2 attempts? If Codex plugin is installed, invoke /codex:rescue — Codex can independently read the LaTeX source and compile.log to spot issues Claude missed (e.g., conflicting packages, encoding problems, subtle macro errors). If not installed, continue with Claude's own diagnosis.

Step 5: Post-Compilation Checks

After successful compilation, verify the output:

bash
# Check PDF exists and has content
ls -la main.pdf
# Check page count
pdfinfo main.pdf | grep Pages

# macOS: open for visual inspection
# open main.pdf

Visual review (automated): If the compiled PDF exists, read it directly to check visual presentation:

  • Figure quality: readable labels, legible text, distinguishable colors
  • Layout: no orphaned section headers, no awkward page breaks
  • Figures appear near their first text reference (not pages away)
  • Tables: aligned columns, consistent decimal precision
  • No overfull content visibly extending past margins

This is a quick visual scan, not a full review — the improvement loop does deeper visual review.

Automated checks:

  • PDF file exists and is > 100KB (not empty/corrupt)
  • Total page count is reasonable (MAX_PAGES + appendix + references)
  • No "??" in the PDF (undefined references — grep the log)
  • No "[?]" in the PDF (undefined citations — grep the log)
  • Figures are rendered (not missing image placeholders)
bash
# Check for undefined references
grep -c "LaTeX Warning.*undefined" compile.log

# Check for missing citations
grep -c "Citation.*undefined" compile.log

Step 6: Page Count Verification

CRITICAL: Verify paper fits within MAX_PAGES.

For ML conferences (ICLR/NeurIPS/ICML/CVPR/ACL/AAAI): Main body = first page through end of Conclusion section (not necessarily §5 — could be §6, §7, or §8 depending on structure). References and appendix are NOT counted.

For IEEE venues: The TOTAL page count (including references) must fit within the limit. There is no separate "main body" counting — everything up to and including the references counts.

Precise check using pdftotext:

bash
# Extract text and find where Conclusion ends vs References begin
pdftotext main.pdf - | python3 -c "
import sys
text = sys.stdin.read()
pages = text.split('\f')
for i, page in enumerate(pages):
    if 'Ethics Statement' in page or 'Reproducibility' in page:
        print(f'Conclusion ends on page {i+1}')
    if any(w in page for w in ['References', 'Bibliography']):
        lines = [l for l in page.split('\n') if l.strip()]
        for l in lines[:3]:
            if 'References' in l or 'Bibliography' in l:
                print(f'References start on page {i+1}')
                break
"

If Conclusion ends mid-page and References start on the same page, the main body is that page number (e.g., if both are on page 9, main body = ~8.5 pages, which is fine for a 9-page limit since it leaves room for the References header).

If over limit:

  • Identify which sections are longest
  • Suggest specific cuts (move proofs to appendix, compress tables, tighten writing)
  • Report: "Main body is X pages (limit: MAX_PAGES). Suggestion: move [specific content] to appendix."

Step 6.5: Stale File Detection

Check for orphaned section files not referenced by main.tex:

bash
# Find all .tex files in sections/ and check which are \input'ed by main.tex
for f in paper/sections/*.tex; do
    base=$(basename "$f")
    if ! grep -q "$base" paper/main.tex; then
        echo "WARNING: $f is not referenced by main.tex — consider removing"
    fi
done

This prevents confusion from leftover files when section structure changes (e.g., old 5_conclusion.tex left behind after restructuring to 7 sections).

Step 7: Submission Readiness

For conference submission, additional checks:

  • Anonymous: no author names, affiliations, or self-citations that reveal identity
  • Page limit: main body within MAX_PAGES (to end of Conclusion)
  • Font embedding: all fonts embedded in PDF
    bash
    pdffonts main.pdf | grep -v "yes"  # should return nothing (or only header)
  • No supplementary mixed in: appendix clearly after \newpage\appendix
  • File size: reasonable (< 50MB for most venues, < 10MB preferred)
  • No [VERIFY] markers: search the PDF text for leftover markers

Step 8: Output Summary

markdown
## Compilation Report

- **Status**: SUCCESS / FAILED
- **PDF**: paper/main.pdf
- **Pages**: X (main body to Conclusion) + Y (references) + Z (appendix)
- **Within page limit**: YES/NO (MAX_PAGES = N)
- **Errors fixed**: [list of auto-fixed issues]
- **Warnings remaining**: [list of non-critical warnings]
- **Undefined references**: 0
- **Undefined citations**: 0

### Next Steps
- [ ] Visual inspection of PDF
- [ ] Run `/paper-write` to fix any content issues
- [ ] Submit to [venue] via OpenReview / CMT / HotCRP

Key Rules

  • Never delete the user's source files — only modify to fix errors
  • Keep compile.log — useful for debugging
  • Don't suppress warnings — report them, let the user decide
  • If LaTeX is not installed, provide clear installation instructions rather than failing silently
  • Font embedding is critical — some venues reject PDFs with non-embedded fonts
  • Page count rules differ by venue — ML conferences: main body to Conclusion (refs excluded). IEEE venues: total pages including references.

Common Venue Requirements

VenueStyle FileCitationPage LimitRefs in limit?Submission
ICLR 2026iclr2026_conference.stynatbib (\citep/\citet)9 pages (to Conclusion end)NoOpenReview
NeurIPS 2025neurips_2025.stynatbib (\citep/\citet)9 pages (to Conclusion end)NoOpenReview
ICML 2025icml2025.stynatbib (\citep/\citet)8 pages (to Conclusion end)NoOpenReview
IEEE JournalIEEEtran.cls [journal]cite (\cite{}, numeric)~12-14 pages (Transactions) / ~4-5 (Letters)YesIEEE Author Portal / ScholarOne
IEEE ConferenceIEEEtran.cls [conference]cite (\cite{}, numeric)5-8 pages (varies by conf)YesEDAS / IEEE Author Portal

Frequently asked questions

What does the Paper Compile AI skill do?

Compile LaTeX paper to PDF, fix errors, and verify output. Use when user says "编译论文", "compile paper", "build PDF", "生成PDF", or wants to compile LaTeX into a submission-ready PDF.

Why use Paper Compile on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/paper-compile. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Paper Compile?

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 Compile?

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

Is the Paper Compile AI skill free?

Yes. It is published on GitHub by wanshuiyin 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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