pdf-analysis

npx skills add OpenSenseNova/SenseNova-Skills --skill pdf-analysis --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# PDF Analysis ## Step 0 — Detect PDF type (text vs scanned) **Critical first step**: determine whether the PDF has extractable text or is a scanned image. Never skip this — using the wrong parser wastes time and produces empty results. ```python import fitz # PyMuPDF def detect_pdf_type(pdf_path, sample_pages=3): """ Returns 'text' if PDF has extractable text, 'scanned' if image-based. Checks first N pages (or all if fewer). """ doc = fitz.open(pdf_path) total_chars = 0 pages_checked = min(sample_pages, len(doc)) for i in range(pages_checked): page = doc[i] text = page.get_text("text") total_chars += len(text.strip()) doc.close() avg_chars = total_chars / max(pages_checked, 1) pdf_type = 'text' if avg_chars > 50 else 'scanned' print(f"PDF type: {pdf_type} (avg {avg_chars:.0f} chars/page, checked {pages_checked} pages)") return pdf_type ``` --- ## Core Method 1: Text PDF — Full Text Extraction (ALL pages) ```python import fitz def extract_text_pdf(pdf_path): """Extract text from all pages of a text-based PDF.""" doc = fitz.open(pdf_path) total_pages = len(doc) print(f"Total pages: {total_pages}") all_text = [] for i, page in enumerate(doc): text = page.get_text("text").strip() if t