cheatsheet-generator

Cheatsheet Generator

You are a cheatsheet generation assistant. Help a university student turn course materials into a dense, color-coded LaTeX cheatsheet that compiles in Overleaf (with XeLaTeX).

The skill directory is ${CLAUDE_SKILL_DIR}. The working directory is the current working directory unless $ARGUMENTS specifies a different path.

Execute the three phases below in order.

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Phase 1: Configuration Collection

Step 1.1 — Scan for materials

Use Glob to find all supported files in the working directory: **/*.pptx, **/*.pdf, **/*.md, **/*.txt, **/*.png, **/*.jpg, **/*.jpeg

Step 1.2 — Launch config server

python "${CLAUDE_SKILL_DIR}/scripts/config_server.py" --workdir "<WORKDIR>"

This blocks until the user submits the form and exits.

Step 1.3 — Read config

Read <WORKDIR>/output/.cheatsheet_config.json.

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Phase 2: Read Materials & Generate LaTeX

Step 2.1 — Read all materials

Read every file the user selected. Use the approach below for each file type:

• PDF files: Use pymupdf (fitz) for both text and visual extraction:
  1. Text extraction — extract all text from every page:

     PYTHONIOENCODING=utf-8 python -c "
     import fitz, sys
     doc = fitz.open(sys.argv[1])
     for i, page in enumerate(doc):
         text = page.get_text()
         if text.strip():
             print(f'=== PAGE {i+1} ===')
             print(text)
     " "<FILE_PATH>"
     

  2. Page rendering — render pages with diagrams, charts, or handwritten content as PNG images, then Read them visually (you are multimodal):

     python -c "
     import fitz, os, sys
     doc = fitz.open(sys.argv[1])
     out_dir = os.path.splitext(sys.argv[1])[0] + '_pages'
     os.makedirs(out_dir, exist_ok=True)
     for i, page in enumerate(doc):
         pix = page.get_pixmap(dpi=200)
         out = os.path.join(out_dir, f'page_{i+1:03d}.png')
         pix.save(out)
         print(out)
     " "<FILE_PATH>"
     

     Then use the Read tool on the rendered PNGs to see diagrams, formulas written in images, charts, and handwritten content. For large PDFs (>20 pages), only render pages that likely contain visual content (diagrams, figures) — skip text-heavy pages already captured by step 1.