/product-spec-pdf-parser — PDF Product Spec Parser

Extract structured FF&E data from product PDF files — price books, fact sheets, configurator sheets, and spec sheets. Uses PyMuPDF for text extraction and Claude's reasoning to parse wildly varying PDF layouts into a standardized schedule.

Input

The user provides PDFs in one of these ways:

1. File paths — one or more PDF file paths
2. Folder path — a directory containing PDFs (will process all .pdf files)
3. Just invoked — ask the user for file paths or a folder

Also ask (or use defaults):

• Output destination — Google Sheet, local CSV, or markdown (default: ask)
• Variant depth — expand (one row per variant/SKU, default) or summarize (comma-separated variants in one row)

Output Schema

Products are written to the master Google Sheet — the same 33-column schema used by all product skills, plus PDF-specific extra columns. When writing to CSV, use the same column order.

Read ../../schema/product-schema.md (relative to this SKILL.md) for the full column reference, field formats, and category vocabulary. Read ../../schema/sheet-conventions.md for CRUD patterns with MCP tools.

Skill-specific column values:

• AG (Source): pdf-parser
• AF (Status): saved
• J (Link): Blank (no URL for PDFs)
• D (Thumbnail): Blank (no image URL typically)
• C (Vendor): Blank (source is PDF, not a retailer)
• V (Sale Price): Blank (PDFs don't have sale prices)
• AC (Image URL): Blank (no image from PDF)

PDF-specific data in Notes (col AE)

PDFs contain fields that don't have dedicated master columns. Append these to Notes using | as delimiter:

• Variant: Variant: Diamond, Black
• Price Adder: Price adder: +$130 (PostureFit SL)
• Country of Origin: Origin: Sweden
• Source File: Source: alphabeta-fact-sheet.pdf

Example Notes cell: Variant: Diamond, Black | Origin: Sweden | Source: alphabeta-fact-sheet.pdf

Variant Handling

Different PDF types require different approaches:

Fact sheets with SKUs (e.g., Alphabeta lamp)

• One row per SKU. Each shade shape × color = one row.
• Product Name stays the same across rows. Variant describes the distinguishing attributes.
• Example: "Alphabeta Floor Lamp" / Variant: "Diamond, Black" / SKU: "..."

Fact sheets with upholstery/finish combos (e.g., Puffy lounge chair)

• One row per upholstery option. Frame finish goes in Colors/Finishes.
• Distinct products (chair + ottoman) each get their own set of rows.
• Example: "Puffy Lounge Chair" / Variant: "Traffic Red" / Colors/Finishes: "Chrome frame"

Price books / configurators (e.g., Aeron price book)

• One row per distinct product type (e.g., Work Chair, Stool, Side Chair).
• Base configuration in main fields. Summarize configuration options — do NOT explode every permutation.
• Use Price Adder for incremental costs of add-ons or upgrades.
• Example: "Aeron Chair" / Variant: "Size B, Graphite" / List Price: 1395.00 / Price Adder: 130.00 (PostureFit SL)

expand vs summarize mode

• expand (default): One row per variant, SKU, or distinct option. Best for procurement and ordering.
• summarize: One row per product. Colors/Finishes and Variant are comma-separated lists. Best for quick reference.

Workflow

Step 1: Get input

Parse the user's input to identify PDF file(s) and output preferences.

• If given a folder, list all .pdf files and report count
• If no PDFs found or path is invalid, ask the user
• Confirm variant depth — default to expand unless the user says otherwise
• Report: "Found N PDF(s) to process."

Step 2: Extract text from PDF

Use PyMuPDF (fitz) to extract text from each PDF. Run this Python script via Bash:

import fitz
import sys
import json

pdf_path = sys.argv[1]
doc = fitz.open(pdf_path)
pages = []
for i, page in enumerate(doc):
    text = page.get_text()
    pages.append({"page": i + 1, "text": text})
doc.close()

print(json.dumps({"filename": pdf_path.split("/")[-1], "total_pages": len(pages), "pages": pages}))

For each PDF, extract all pages and save the JSON output.

Step 3: Parse products with Claude

Read the extracted text and identify all products, variants, and specifications. This is the core intelligence step — Claude reasons over the text to structure it.

For small PDFs (≤20 pages): Process all pages at once.

For large PDFs (>20 pages): Process in chunks of 10 pages at a time. After each chunk:

• Accumulate parsed products
• Carry forward context (product name, brand, any ongoing configuration table)
• At the end, deduplicate and merge

Parsing instructions:

1. Identify the document type — fact sheet, price book, configurator, spec sheet, catalog
2. Extract global fields first — brand, designer, collection, warranty, certifications, country of origin (these usually appear once)
3. Find product boundaries — headings, page breaks, or new product names signal a new product
4. For each product, extract all variants based on the variant handling rules above
5. Map dimensions carefully — PDFs often format dimensions as "W × D × H" or in a spec table. Parse into separate W, D, H fields.
6. Prices — distinguish between base price and adders. If a configurator shows "Base: $1,395 / Add: $130 for PostureFit", set List Price = 1395, Price Adder = 130
7. Leave fields blank rather than guessing — if a field isn't in the PDF, leave it empty

Step 4: Present results

Show a summary markdown table with the parsed products. Include:

• Row count per PDF
• Any issues or assumptions made
• Sample of the first 10 rows if large

Ask: "Does this look correct? Should I adjust anything before saving?"

Step 5: Write output

Ask the user (if not already specified): "Where should I save this?"

Options:

• Master Google Sheet — append rows to the shared product library. Ask for spreadsheet ID if not already known.
• Local CSV — save to a specified path (default: ./ffe-pdf-parse-YYYY-MM-DD.csv)
• Just the table — leave as markdown in the conversation

CSV Format

When saving to CSV, use the CSV header from ../../schema/product-schema.md.

Google Sheets Format

Append rows to the master Google Sheet using the same 33-column schema. Set Clipped At to current timestamp and Source to pdf-parser. PDF-specific data (variant, price adder, country of origin, source filename) goes in the Notes column.

Edge Cases

• Scanned PDFs (image-only): PyMuPDF will return empty or garbage text. Detect this (very short text relative to page count) and tell the user: "This PDF appears to be scanned/image-based. Text extraction won't work — consider using an OCR tool first."
• Multi-language PDFs: Extract data as-is. Note the language. The cleanup skill handles translation.
• PDFs with tables as images: Common in price books. If a section seems to have missing data despite being a spec-heavy document, note it and flag for manual review.
• Password-protected PDFs: PyMuPDF will fail to open. Catch the error and tell the user.
• Very large PDFs (100+ pages): Process in 10-page chunks. Give progress updates every 20 pages.
• Mixed product types in one PDF: Handle each product type independently. A catalog with chairs AND tables gets rows for both.

Error Reporting

After processing, always report:

Parsed: X products from Y PDF(s)
- filename.pdf: N products extracted
- filename2.pdf: M products extracted
Issues: [list any problems]