product-data-cleanup

name	product-data-cleanup
description	Clean up an FF&E schedule — normalize casing, dimensions, units, language, materials, and formatting for consistency.
allowed-tools	["Read","Write","Edit","Bash","Glob","Grep","AskUserQuestion","mcp__google-sheets__get_sheet_data","mcp__google-sheets__update_cells","mcp__google-sheets__list_sheets"]

/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

Field	Rule	Example
Product Name	Title Case	eames lounge chair → Eames Lounge Chair
Brand	Title Case, preserve known abbreviations	HERMAN MILLER → Herman Miller, HAY → HAY
Collection	Title Case	cosm → Cosm
Category	Title Case, singular	chairs → Chair, TABLES → Table
Materials	Sentence case, lowercase after first word	MOLDED PLYWOOD, FULL GRAIN LEATHER → Molded plywood, full grain leather
Colors/Finishes	Title Case per item	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. Read that file for the full mapping of variations (English, Spanish, legacy terms) to canonical category names.

If a category is ambiguous, keep the closest match and add a [?] flag for the user to review.

3. Dimensions

Splitting combined dimensions:

Input	→ W	→ D	→ H	→ Unit
32 x 24 x 30 in	32	24	30	in
80 × 60 × 75 cm	80	60	75	cm
W32 D24 H30	32	24	30	(infer)
32"W x 24"D x 30"H	32	24	30	in
Ancho: 80, Prof: 60, Alto: 75 cm	80	60	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.

Spanish (common in UY sources)	→ English
Silla	Chair (category)
Mesa	Table (category)
Escritorio	Desk (category)
Madera	Wood (material)
Cuero	Leather (material)
Acero	Steel (material)
Vidrio	Glass (material)
Tela	Fabric (material)
Mármol	Marble (material)
Roble	Oak (material)
Nogal	Walnut (material)
Blanco	White (color)
Negro	Black (color)
Natural	Natural (keep as-is)
Cromado	Chrome (finish)

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:

Variations	→ Standard
SS, Stainless, S/S	Stainless steel
Ply, Plywood, Mold ply	Molded plywood
MDF, Medium density	MDF
HPL, High pressure laminate	HPL
Lam, Laminate	Laminate
Fab, Textile	Fabric
COM, C.O.M.	COM (Customer's Own Material)
COL, C.O.L.	COL (Customer's Own Leather)
Powder coat, PC, Pwdr	Powder-coated
Chrm, Chrome plated	Chrome
Anodized alum, Anod.	Anodized aluminum
Ven, Veneer	Veneer
Sol. wood, Solid	Solid wood

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.