Paper Style Extraction Skill

Goal: Reverse engineer academic style from PDF/TeX files and build the psmfiles/ knowledge base.

Workflow

1. Locate Source Files:

   • Look for PDF, TeX, or TXT files in the current directory or user-specified path.
   • If user requests new papers/datasets, use WebSearch to find them first.

2. Extract Text:

   • Execute the extraction script: python paper-extract-style/extract_text.py <file_path>
   • This creates ref_article/*_cleaned_body.md and ref_article/*_appendix.md.

3. Build Knowledge Base (Incremental Fusion):

   • Read: Process ref_article/*_cleaned_body.md.
   • Update psmfiles/lexicon_domain.md:
     • Extract high-frequency sentence patterns for each section (Abstract, Intro, Methods, Experiments, Conclusion).
     • Generalize: Replace specific nouns with placeholders (e.g., [Method], [Metric]).
     • De-duplicate: Do not copy the static skeleton from LEXICON.md.
   • Update psmfiles/DOMAINS_Knowledge.md:
     • Extract "Territory" statements (Industrial impact).
     • Extract "Niche" statements (Critiques of prior work, Solved problems).
     • Extract "Trends" and "Citations".
     • Fusion Logic: Append new insights; merge duplicate views to reinforce arguments. Do NOT overwrite existing data.
   • Generate psmfiles/STYLE_GUIDE.md:
     • Read paper-extract-style/TEMPLATES.md.
     • Fill in the template based on observed style (Voice, Narrative Flow, Formatting).
     • Statistics: Calculate average sentence length for each section.
     • Appendix Check: Check *_appendix.md to define Appendix formatting standards.

4. Completion:

   • Report the location of generated assets in psmfiles/.