# Standardize Electronics BOM Cleanup

Inconsistent BOM exports create duplicate orders and incomplete procurement records. This guide defines cleanup rules for column mapping, matching-part merges, reference formatting, and missing-data review.

## Install

```bash
npx skillstore add nexaiguy/nex-bom-normalizer
```

## Metadata

- Status: approved
- Slug: nexaiguy-nex-bom-normalizer
- Version: 1.0
- Author version: 1.0
- Skillstore revision: r1
- Version status: invalid
- Tree hash: e042b38b4faaacd5ec5c355168f01771d2590ef56539b9704252b8391787d98a
- Author: nexaiguy
- GitHub username: nexaiguy
- License: MIT
- Repository: https://github.com/nexaiguy/nex-ai-skills/tree/463aaff26593654193deb52f24d18e80ec21e323/skills/nex-bom-normalizer
- Ref: 3ddfdca559084abffbfdd188f2ba14b597dcfd9c
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: network
- Quality score: 78
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/nexaiguy-nex-bom-normalizer
- Manifest: https://skillstore.pages.dev/api/skills/nexaiguy-nex-bom-normalizer/manifest

## Capabilities

- Describes canonical BOM columns, including RefDes, Qty, MPN, and Manufacturer.
- Defines deduplication using both manufacturer part number and manufacturer name.
- Specifies uppercase, comma-separated reference designators.
- Describes summing quantities when matching rows are merged.
- Identifies missing MPN, Qty, or Manufacturer values as candidates for a separate review CSV.

## Use Cases

- Review EDA Export Cleanup: Use the documented column and reference rules to review inconsistent Altium, KiCad, or Eagle exports before assembly handoff.
- Consolidate Procurement Lists: Apply the documented matching criteria when reviewing partial BOMs for consolidation, keeping different manufacturers separate.
- Identify Incomplete Quote Data: Review required BOM fields and separate incomplete records before requesting a manufacturing quote.

## Prompt Templates

### Map BOM Columns

```
Using the guide, map these BOM headers to RefDes, Qty, MPN, and Manufacturer: [headers]. Flag ambiguous mappings without guessing.
```

### Normalize Reference Designators

```
Normalize reference designators in these pasted BOM rows to uppercase, comma-separated values: [rows]. Preserve part identifiers and report uncertain entries.
```

### Review Duplicate Parts

```
Review these BOM rows: [rows]. Merge only matching MPN and Manufacturer pairs, sum quantities, and list unresolved conflicts separately.
```

### Plan a Validated Cleanup

```
Review [BOM data] against the guide. Propose column mappings, duplicate merges, quantity checks, and missing-field reports. Identify ambiguities and unavailable automation before changes.
```

## Limitations

- Only SKILL.md is included; the referenced Python CLI, extension source, and templates are absent.
- CSV and XLSX workflows are described, but executable import and export support cannot be verified from this package.
- The guide does not provide external pricing or stock lookups.
- Claims of guaranteed data integrity and detailed audit logging cannot be verified without the missing implementation.

## Best Practices

- Preserve the original BOM and review proposed column mappings before applying changes.
- Confirm both MPN and Manufacturer match before merging, then reconcile quantities and reference designators.
- Resolve flagged missing fields manually and validate any separately obtained implementation before processing production files.

## Anti Patterns

- Assuming the public guide includes the referenced executable or guarantees deterministic results.
- Merging rows solely by similar descriptions or matching MPNs while ignoring manufacturers.
- Inventing missing part numbers, quantities, or manufacturer names to complete a BOM.

## Security Audit

- Audited at: 2026-11-02T12:38:54.951\+00:00
- Summary: All three static findings are false positives: the URLs are attribution and catalog links, and the footer contains no system reconnaissance instructions. No evidence found of prompt injection, data exfiltration, or malicious execution in the supplied guide. Referenced automation scripts are absent, so their behavior cannot be assessed.

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