Enriches FF&E product lists with AI-inferred categories, subcategories, primary colors, materials, and style tags. Processes CSV files, Google Sheets, or pasted data to standardize metadata.
npx claudepluginhub alpacalabsllc/skills-for-architects --plugin 06-materials-researchThis skill is limited to using the following tools:
Takes a product list with basic info (name + brand) and fills in missing metadata — category, subcategory, primary color, material, and style tags — using AI. Works on CSV files, Google Sheets, or pasted data.
Automates FF&E product research: takes designer brief, web searches for matching products, curates structured candidates, saves selections to Google Sheet.
Generates Wix product listings from images: uses Claude Vision to analyze for name, description, category, pricing, tags; creates via Stores REST API and uploads images.
Displays static menu of 36 skills and 7 agents organized by architecture tasks including site due diligence, NYC zoning, programming, specifications, and sustainability.
Share bugs, ideas, or general feedback.
Takes a product list with basic info (name + brand) and fills in missing metadata — category, subcategory, primary color, material, and style tags — using AI. Works on CSV files, Google Sheets, or pasted data.
Accept product data in any format:
Google Sheet ID:
/product-enrich 1FMScYW9guezOWc_m4ClTQxxFIpS6TNRr373R-MJGzgE
CSV file:
/product-enrich ~/Documents/project/products.csv
Pasted data:
/product-enrich
Eames Lounge Chair, Herman Miller
Saarinen Tulip Table, Knoll
PH 5 Pendant, Louis Poulsen
Togo Sofa, Ligne Roset
For each product, infer the following fields:
Map to the canonical vocabulary (22 terms) defined in ../../schema/product-schema.md.
More specific classification within the category:
The dominant color of the product as typically sold:
Primary materials, comma-separated:
2-4 descriptive tags from:
If the product data includes an image URL (column AC in the master schema), use Claude vision to verify and refine the enrichment. The image may reveal:
Show a preview of the enrichment before applying:
## Product Enrichment Preview
| Product | Brand | Category | Subcategory | Color | Material | Style Tags |
|---------|-------|----------|-------------|-------|----------|------------|
| Eames Lounge Chair | Herman Miller | Chair | Lounge Chair | Walnut/Black | Molded plywood, Leather | Mid-Century Modern, Iconic, Residential |
| Saarinen Tulip Table | Knoll | Table | Dining Table | White | Marble, Aluminum | Mid-Century Modern, Sculptural, Organic |
| PH 5 Pendant | Louis Poulsen | Light | Pendant | White | Aluminum | Scandinavian, Classic, Iconic |
| Togo Sofa | Ligne Roset | Sofa | Sofa | Brown | Fabric | Contemporary, Organic, Sculptural |
4 products enriched. Apply? (y/n)
Flag any products where enrichment is uncertain:
⚠ "Custom Reception Desk" — unknown product, category set to Desk but verify
Write enriched fields to the master 33-column schema:
Do NOT overwrite existing values unless the field is empty or the user explicitly asks to overwrite.
Add new columns if they don't exist: Category, Subcategory, Primary Color, Material, Style Tags. Save as {original-name}-enriched.csv.
Output the enriched table in markdown.
✓ Enriched 4 products
Categories: 4 assigned (0 already had values)
Colors: 4 assigned
Materials: 4 assigned
Style tags: 14 total tags across 4 products
Uncertain: 1 (flagged for review)
/product-spec-bulk-fetch — fetch specs first, then enrich the results/product-data-cleanup — cleanup normalizes formatting, enrich adds metadata/product-data-import — enriched products make better formatted schedules/product-match — enriched tags help find better matches