/product-data-cleanup — Product Data Normalizer

Takes a messy FF&E schedule and normalizes everything: casing, dimensions, units, language, materials vocabulary, currency formatting, and duplicates. Outputs a clean, consistent, spec-ready schedule.

Operates on the master Google Sheet — the same 33-column schema used by all product skills. Also works on standalone CSV files and pasted tables.

Input

The user provides a schedule in one of these ways:

1. Master Google Sheet — the shared product library. Provide spreadsheet ID or URL.
2. File path — a

   .csv

   ,

   .tsv

   ,

   .xlsx

   export, or

   .md

   file
3. Pasted table — markdown or tab-separated data in the message

If the input format is unclear, ask.

Cleanup Rules

1. Casing

eames lounge chair

Eames Lounge Chair

HERMAN MILLER

Herman Miller

HAY

HAY

cosm

Cosm

chairs

Chair

TABLES

Table

MOLDED PLYWOOD, FULL GRAIN LEATHER

Molded plywood, full grain leather

walnut/black leather

Walnut / Black Leather

Known brand abbreviations to preserve: HAY, USM, B&B, DWR, CB2, HBF, OFS, SitOnIt, 3form, ICF

2. Category Normalization

Map free-text categories to the canonical vocabulary and alias table defined in

../../schema/product-schema.md

If a category is ambiguous, keep the closest match and add a

[?]

3. Dimensions

Splitting combined dimensions:

32 x 24 x 30 in

80 × 60 × 75 cm

W32 D24 H30

32"W x 24"D x 30"H

Ancho: 80, Prof: 60, Alto: 75 cm

Dimension rules:

• Always store as separate W, D, H columns with a Unit column
• If dimensions are already split, validate they're numeric (strip any unit text from the number)
• Interpret

  "

  as inches,

  '

  as feet (convert to inches:

  2'6"

  →

  30

  )
• Accept

  ×

  ,

  x

  ,

  X

  ,

  by

  ,

  por

  as separators
• Convention: W × D × H (width × depth × height). If only 2 values, ask which is missing.
• Round to 2 decimal places max
• If unit is missing but values suggest inches (all < 100 for furniture), assume

  in

  . If values suggest cm (> 100 or explicit), use

  cm

  . If truly ambiguous, flag with

  [?]

  .

Do NOT convert units. Keep the original unit. Designers need the manufacturer's spec for ordering.

4. Language Normalization

Detect the language of each field value and normalize to English unless the user specifies otherwise.

Rule: Translate category, material, and color/finish fields. Leave Product Name and Brand as-is (proper nouns).

If the user says "keep in Spanish" or specifies a target language, respect that.

5. Materials & Finishes Vocabulary

Standardize common material terms:

6. Price & Currency

• Strip currency symbols (

  $

  ,

  €

  ,

  £

  ,

  ¥

  ) — store symbol as currency code in separate column
• Remove thousands separators (both

  .

  and

  ,

  — detect locale:

  1.234,56

  is EU format,

  1,234.56

  is US)
• Store as plain decimal number:

  5695.00

• If price says "Contact", "Quote", "Trade", "A consultar", "Consultar" → set to empty
• Currency detection:

  $

  alone defaults to

  USD

  unless context suggests otherwise (UY site →

  UYU

  , EU site →

  EUR

  )
• If a schedule mixes currencies, keep each row's original currency. Add a note at the top.

7. Duplicate Detection

• Flag rows with identical Product Name + Brand as potential duplicates
• Flag rows with identical URL as definite duplicates
• Don't auto-delete — present duplicates to the user and ask what to keep

8. Whitespace & Formatting

• Trim leading/trailing whitespace from all fields
• Collapse multiple spaces to single space
• Remove line breaks within field values
• Normalize list separators:

  wood / metal / glass

  →

  Wood, Metal, Glass

  (comma-separated)
• Remove trailing commas or semicolons

Workflow

Step 1: Load the schedule

Read the input. Report: "Loaded N rows with M columns." Map input columns to the canonical schema. If column mapping is ambiguous (e.g., a column called "Size" could be combined dimensions), ask the user.

Step 2: Analyze issues

Scan all rows and produce a summary:

## Cleanup Preview

- **Casing**: X product names need Title Case
- **Categories**: Y rows have non-standard categories (mapping: "chairs" → Seating, etc.)
- **Dimensions**: Z rows have combined dimensions to split
- **Language**: W rows have Spanish-language fields to translate
- **Materials**: V rows have non-standard material terms
- **Prices**: U rows need currency formatting cleanup
- **Duplicates**: T potential duplicate rows found
- **Empty fields**: S rows missing dimensions, R rows missing price

Step 3: Confirm scope

Ask: "Apply all fixes, or select which ones?"

If the user wants to be selective, let them pick from the list. Otherwise, apply all.

Step 4: Apply fixes

Process every row through the active cleanup rules. Track every change made.

Step 5: Present results

Show a before/after diff for a sample of changed rows (up to 5 examples). Then show the full cleaned table.

Report:

## Cleanup Complete

- Rows processed: N
- Changes made: X
- Flagged for review: Y (marked with [?])

Step 6: Save

Ask: "Save where?" Options:

• Overwrite original file (if local file input)
• Save as new file (default: append

  -clean

  to filename)
• Write to Google Sheet (same sheet or new one)
• Just show the table — leave in conversation

Edge Cases

• Mixed-language schedule: Detect dominant language per column, normalize to one language
• Merged cells or irregular formatting: Flag and ask user how to handle
• Extra columns not in schema: Preserve them as-is at the end, don't delete
• Empty rows: Remove silently
• Header detection: Auto-detect header row (first row with text that matches known field names). If uncertain, ask.