Cross-Channel SKU Mapping Audit

What it does

Audit explicit channel-variant to canonical SKU mappings, exact text identities, unit factors and unmapped conflicts.

Model and scope

canonical contains unique exact sku and unit. channel_products contain channel, variant_id, location, exact sku, nonnegative quantity and unit; channel/variant/location is unique. mappings explicitly contain channel, variant_id, canonical_sku, from_unit, to_unit and positive factor. One channel variant must have exactly one mapping. Multiple locations for the same variant identity are permitted; contradictory identity or distinct variants reusing one exact SKU within a channel are conflicts. Unknown canonical, ambiguous mappings and unit conflicts are reported and excluded from quantities. Converted quantity = input quantity*factor; factors are never inferred. Valid totals stay separate by channel/location/canonical/unit, to avoid summing identical physical stocks exposed on several channels. No fuzzy matching, automatic canonical merge or bundle expansion.

Try saying

Check my confirmed SKU mapping for leading-zero differences, one-to-many conflicts and unit mismatches.

Roll up only validated mappings by channel and location; do not merge stock snapshots across channels.

Runtime and outputs

Reads supplied normalized JSON and creates result.json, report.md and rows.csv with input and output hashes. Preserve raw CSV; ask the assistant to normalize exports against the bundled input reference. Python 3.10+ standard library only; no paid data key or MochiLab backend needed. Optional explanation uses your current agent model and its ordinary service costs separately. Existing output directories are refused. Includes fictional examples and independent offline domain tests. No API/store changes, uploads, automatic tasks, notifications, legal or tax decisions.

References

• Primary reference 1 ↗
• Primary reference 2 ↗