Latex Paper Conversion

LaTeX Paper Conversion

Overview

This skill automates the tedious and recurring process of converting an academic paper written in LaTeX from one publisher's template to another. Different journals (e.g., Springer, MDPI, IEEE) have vastly different structural requirements, document classes, margin settings, and bibliography styles. This skill streamlines these conversions by executing a structured multi-stage workflow, extracting content, mapping it to a new template, and resolving common compilation errors.

When to Use This Skill

• Use when the user requests to port an existing LaTeX paper to a new journal's format.
• Use when the user provides an existing .tex file and a new template directory.
• Use when the user mentions converting from format A (e.g., IPOL/Neural Processing) to format B (e.g., MDPI).

How It Works

Step 1: Pre-requisites & Assessment

Identify the Source LaTeX file and asking the user for the Target Template Directory. Understand the core layout mapping (single-column vs. double-column, bibliography style).

Step 2: Extraction & Injection Script Generation

Create a Python script (e.g., convert_format.py) to parse the source LaTeX file. Use Regular Expressions to extract core text blocks. Merge the new template's preamble, the extracted body, and the backmatter. Write this to a new file in an output directory.

Step 3: Systematic Fixing

Perform generic fixes on the extracted body text before writing the final file, or in subsequent calls:

• Convert math environment cases (e.g., \begin{theorem} to \begin{Theorem}).
• Adjust aggressive float placements (e.g., [!t] or [h!]) to template-supported options. Avoid forcing [H] unless the float package is explicitly loaded.
• Ensure \includegraphics paths are relative to the new .tex file location.
• Convert \begin{tabular} to \begin{tabularx}{\textwidth} or use \resizebox if moving to a double-column layout.

Step 4: Compilation & Debugging

Run a build cycle (pdflatex -> bibtex -> pdflatex). Check the .log file using grep or rg to systematically fix any packages conflicts, undefined commands, or compilation halts.

Examples

Example 1: Converting IPOL to MDPI

``` USER: "I need to convert my paper 'SAHQR_Paper.tex' to the MDPI format located in the 'MDPI_template_ACS' folder." AGENT: Triggers latex-paper-conversion skill

1. Analyzes source .tex and target template.tex.
2. Creates Python script to extract Introduction through Conclusion.
3. Injects content into MDPI template.
4. Updates image paths and table float parameters [h!] to [H].
5. Compiles via pdflatex and bibtex to confirm zero errors. ```

Best Practices

• ✅ Always write a Python extraction script; DO NOT manually copy-paste thousands of lines of LaTeX.
• ✅ Always run pdflatex and verify the .log to ensure the final output compiles.
• ✅ Explicitly ask the user for the structural mapping if the source and target differ drastically (e.g., merging abstract and keywords).
• ❌ Don't assume all math packages automatically exist in the new template (e.g., add \usepackage{amsmath} if missing).

Common Pitfalls

• Problem: Overfull hboxes in tables when moving from single to double column. Solution: Detect \begin{tabular} and automatically wrap in \resizebox{\columnwidth}{!}{...} or suggest a format change.
• Problem: Undefined control sequence errors during compilation. Solution: Search the Paper.log and include the missing \usepackage{} in the converted template.

Additional Resources

• Overleaf LaTeX Documentation

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags latex-paper-conversion <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns