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product-data-cleanup

Normalize FF&E product schedule data and output structured JSON reports.

Updated Jun 16, 2026
Search Tags:#duplicate detection#data normalization#product data#ff&e#aec workflows#schedule cleanup
One-click install
npx skills add https://github.com/robikscube/SJ-skills-sandbox --skill product-data-cleanup
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: product-data-cleanup
Source: https://github.com/robikscube/SJ-skills-sandbox/tree/main/Skill-List/alpaca-product/product-data-cleanup
Command: npx skills add https://github.com/robikscube/SJ-skills-sandbox --skill product-data-cleanup

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Messy, inconsistently formatted FF&E product schedules imported from PDFs or external sources require hours of manual cleanup to standardize casing, dimensions, prices, materials, and catch duplicate entries before client review.

Core Features & Use Cases

  • Full Schedule Normalization: Standardizes product names, brands, categories, materials, finishes, and price values while preserving known brand abbreviations.
  • Dimension Splitting: Automatically splits combined dimension fields into separate width, depth, height, and unit values for consistent data entry.
  • Duplicate Flagging: Identifies potential duplicate product entries without deleting them, marking them for user or agent review.
  • Use Case: Clean a CSV of imported furniture product rows for an AEC project, catching formatting errors and duplicate entries automatically before sharing with clients.

Quick Start

Use the product-data-cleanup skill to clean your FF&E product schedule file and receive a JSON report of all changes and flagged items for review.

Frequently Asked Questions about product-data-cleanup

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I normalize messy FF&E schedule data imported from PDFs?▼

To normalize FF&E schedule data, this skill standardizes product names, materials, and finishes, automatically splits combined dimensions into separate fields, and flags duplicate candidate entries for user validation.

What is the best way to flag duplicate entries in an AEC product schedule?▼

Flagging duplicate product entries in AEC schedules identifies potential duplicates and marks them for agent review without deleting them, ensuring data integrity during schedule cleanup.

Can I split combined dimension fields in a furniture schedule CSV automatically?▼

Yes, dimension splitting automatically separates combined dimension fields in furniture schedule CSVs into distinct width, depth, height, and unit values for consistent data entry.

Does this schedule cleanup process require any external dependencies or API keys?▼

No external dependencies are required for schedule cleanup, as the process relies entirely on deterministic local data processing to output structured JSON reports of changes and review flags.

How do I standardize price values and brand abbreviations for client review?▼

Standardizing price values and brand abbreviations normalizes inconsistent financial and text data while preserving known brand abbreviations, ensuring clean schedules ready for client review.

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