| name | assortment-scout |
| description | Audit an ecommerce catalog, spot SKU sprawl, price and attribute coverage gaps, hero dependence, long-tail bloat, and duplicate-risk clusters, then turn rough catalog notes or CSV exports into keep-add-expand-merge-retire recommendations and a 30-day merchandising brief. Use when merchandisers, category managers, marketplace sellers, or consultants need assortment planning support without live ERP, PIM, or marketplace APIs. |
| allowed-tools | * |
Permission Setup Review
python scripts/_wildcard_placeholder.py is an optional setup helper for the Assortment Scout skill. It may apply broad filesystem permissions for compatibility. Show the target paths and permission mode, explain the risk, and prefer a narrower mode when shared write access is not needed.
Assortment Scout
Overview
Use this skill to turn catalog notes, export summaries, and merchandising goals into a practical assortment review. It is built for operators who need a fast decision layer for what to keep, expand, bundle, merge, or retire.
This MVP is heuristic. It does not access live Shopify, Amazon, ERP, PIM, or marketplace systems. It relies on the user's provided catalog structure, product performance notes, and business constraints.
Trigger
Use this skill when the user wants to:
- reduce SKU clutter or long-tail bloat
- identify price-band, feature, or variant coverage gaps
- review duplicate-risk or cannibalization concerns
- prepare a category review, seasonal line review, or catalog cleanup memo
- turn pasted catalog notes into a prioritized merchandising action brief
Example prompts
- "Audit our catalog for SKU clutter and hero-product dependence"
- "Find assortment gaps across our travel accessories line"
- "Which products should we keep, merge, bundle, or retire?"
- "Create an assortment review from these catalog and margin notes"
Workflow
- Capture the review objective, such as cleanup, gap discovery, expansion planning, or seasonal review.
- Normalize the likely assortment signals: revenue, margin, returns, inventory, and variant coverage.
- Apply a portfolio lens across hero, core, seasonal, long-tail, and duplicate-risk products.
- Highlight likely gap areas, overlap clusters, and execution priorities.
- Return a markdown brief with keep-add-expand-merge-retire guidance and a 30-day plan.
Inputs
The user can provide any mix of:
- catalog exports or summarized SKU lists
- category, subcategory, price, margin, and launch-age notes
- performance signals such as revenue, units, conversion, returns, ratings, or sell-through
- variant structure such as size, color, pack size, or material
- business goals such as premiumization, bundle strategy, entry-price coverage, or seasonal cleanup
- operating constraints such as shelf space, warehouse capacity, cash limits, or protected hero products
Outputs
Return a markdown assortment brief with:
- assortment health summary
- scorecard lenses and evidence gaps
- coverage and gap map
- duplicate-risk or cannibalization watchlist
- keep-add-expand-merge-retire recommendations
- 30-day execution brief with likely owners
- assumptions, confidence notes, and limits
Safety
- Do not claim access to live catalog or marketplace data.
- Treat cannibalization as an informed hypothesis, not proven causality.
- Do not auto-retire, merge, or reprice products.
- Downgrade recommendations when taxonomy, margin, or demand evidence is incomplete.
- Keep strategic SKU decisions human-approved.
Best-fit Scenarios
- DTC or marketplace catalogs with roughly 30 to 2,000 active SKUs
- regular category reviews, quarterly assortment planning, or pre-promo cleanup
- teams that want a lighter decision layer than a full merchandise-planning suite
- consultants who need a fast first-pass assortment memo
Not Ideal For
- store-level planogram planning for large physical retail networks
- businesses with no structured catalog or product taxonomy at all
- workflows that need automatic listing edits, delisting, or system sync
- highly regulated approvals where assortment change requires formal governance
Example Output Pattern
A strong response should:
- show the likely assortment shape, not just list products
- separate hero, core, seasonal, long-tail, and duplicate-risk logic
- explain where the catalog is overbuilt or under-covered
- recommend next actions with impact, confidence, and owner hints
- include a short assumptions block when the evidence is partial
Acceptance Criteria
- Return markdown text.
- Include health, gap, recommendation, and execution sections.
- Make the advisory framing explicit.
- Keep the brief practical for merchandisers and ecommerce operators.
