Nex Bom Normalizer
by Nex AI
Standardize, deduplicate, and clean messy Bill of Materials (BOM) files into procurement-ready CSVs.
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Works with the AI tools you already use
See it in action
You say
Clean up this messy parts list in source_bom.xlsx and give me a standardized CSV for the assembly house.
Your agent does
I've normalized source_bom.xlsx.
- Merged 4 duplicate rows.
- Standardized columns to RefDes, Qty, MPN, Manufacturer.
- 2 rows flagged for missing MPNs in source_bom_flagged.csv. Cleaned file: source_bom_normalized.csv.
What you get
About this skill
The problem
Engineering and procurement teams waste hours manually cleaning inconsistent BOMs exported from different EDA tools. Messy reference designators, duplicate part entries, and non-standard column headers cause ordering errors and assembly delays.
What it does
- Standardizes inconsistent column headers into a canonical set (RefDes, Qty, MPN, Manufacturer, etc.).
- Deduplicates parts by merging rows based on Manufacturer Part Number and Manufacturer name.
- Normalizes reference designators into comma-separated, uppercase strings.
- Calculates and updates total quantities automatically during the merge process.
- Flags rows with missing required fields (MPN, Qty, Manufacturer) into a separate CSV for manual review.
Frameworks & tools
Python-based CLI tool (scripts/normalize.py). Works with CSV and XLSX input files.
Why this beats prompting it yourself
A generic LLM prompt often misses edge cases in part deduplication or fails to maintain strict column ordering. This skill uses a dedicated Python script to ensure 100% data integrity, providing a repeatable process with a detailed audit log of every fix applied.
Use cases
- Cleaning messy exports from Altium, KiCad, or Eagle for procurement.
- Merging multiple partial BOMs into a single master component list.
- Preparing parts lists for pick-and-place machine programming.
- Identifying missing part data before sending a quote request to a CM.
Known limitations
Does not perform external API lookups for pricing or stock. Data normalization is strictly local based on the provided input file.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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Creator
249 skills on Agensi
Founder of Nex AI. I build production-grade Claude Skills from systems that actually run: multi-tenant SaaS, Telegram agents, Raspberry Pi infrastructure, 3D multiplayer rooms. Every skill ships battle-tested patterns, not theory. 33+ open source skills published, commercial catalog growing.
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