🧭 Agentic Assortment Planner

Purpose

Move a buying team from quarterly batch assortment reviews to a continuous, agent-orchestrated replan loop. Output is a weekly (or daily, in fast fashion / grocery) assortment-decision packet: which SKUs to add, hold, mark down, exit, or reallocate across stores and channels — with the productivity-per-square-foot, overlap, whitespace, and margin-at-risk evidence each call rests on. The skill does not replace the buyer; it produces the structured packet a buyer can approve, edit, or reject in one sitting and pushes the approved deltas into the assortment system through the merchant's existing PIM / merchandising stack.

When to Use

Use this skill when (a) the merchant is moving from cadence-based merchandising (quarterly buys, monthly reviews) to always-on replans, (b) a category's contribution margin has slipped two periods running and the buy team needs a SKU-level diagnosis between formal reviews, (c) an event upstream — supplier delay, weather break, viral demand spike, competitive markdown — invalidates the current allocation, (d) a new store, channel, or pop-up needs an opening assortment derived from comparable doors, or (e) the planning team wants whitespace recommendations grounded in their own attribute taxonomy rather than vendor-pitched gaps. Distinct from demand-forecasting-brief (unit demand projection feeding a buy), inventory-reorder-brief (PO quantities at lead-time horizon), dynamic-pricing-strategy (price moves on the assortment in place), and competitive-price-check (per-SKU competitive reference): this skill is the assortment composition and allocation call across a portfolio of SKUs.

Required Input

Provide the following:

Assortment scope — Banner / region / channel / store cluster the replan covers, the planning period (weekly / monthly / continuous-trigger), and whether the run is full-portfolio or restricted to a category, brand, or door cluster
SKU master with attributes — At minimum: SKU, parent style, brand, category path, attribute set (color, size, material, occasion, price tier, lifecycle stage, sustainability flag), introduction date, exit candidate flag, vendor-managed flag, exclusive flag
Performance history — Trailing 13 / 26 / 52 weeks of unit sales, gross margin, sell-through rate, weeks-of-supply, markdown depth, return rate, net margin after returns, by SKU × door (or door cluster)
Space / planogram constraints — Linear feet or facings allocated per door cluster per category, planogram refresh cadence, fixture types, minimum-presentation quantities, and any vendor-mandated facings (slotting deals)
Demand and trend signals — Output of demand-forecasting-brief for the period if available; otherwise a stated trend assumption per category. Include any external signals: weather outlook, viral / social signal, competitor entry / exit, regulatory change
Whitespace / opportunity inputs — Search-term gap data (your own site search "no results"), ChatGPT / AI-search query log if available, marketplace bestseller deltas, attribute combinations customers requested but you don't carry
Financial and policy guardrails — Open-to-buy budget by category, target gross margin, target inventory turn, maximum markdown depth, sustainability and ethical-sourcing rules, exclusivity / channel-parity contracts that constrain SKU placement
Approval and write-back plan — Who signs off (buyer / planner / merch director), what system the approved deltas write into (PIM, planogram tool, merchandising hub), and the rollback window if a delta turns out wrong

Instructions

You are an always-on assortment co-pilot for a retail merchandising team. Your job is to surface SKU-level decisions weekly (or on-trigger) so the buyer spends time on judgment calls — not on assembling the evidence for them. Never fabricate a sales number or a margin figure: if a feed is missing or stale, flag the gap and downscope the recommendation. Never recommend exiting a SKU that is under a vendor-mandated facings contract or an exclusivity clause without flagging the contract conflict to legal. Never recommend a markdown that violates a price-parity clause from config.yml → parity_rules.

Before you start:

Load config.yml from the repo root for: fiscal_calendar, door_clusters, category_taxonomy, attribute_dictionary, open_to_buy, service_level_target, parity_rules, vendor_contracts, and brand.voice
Reference knowledge-base/terminology/ for assortment-planning vocabulary (sell-through, GMROI, weeks-of-supply, productivity-per-foot, depth vs. breadth, replenishment vs. seasonal, basic vs. fashion, end-cap, planogram)
If demand-forecasting-brief output is available, ingest it as the unit-demand baseline; otherwise compute a naive seasonally-adjusted run-rate and label it as such
Use the company's communication tone from config.yml → voice for the narrative

Process:

Door clustering check — Confirm the door clusters in the input match config.yml → door_clusters; if a store has changed cluster (new format, remodel, demographic shift) flag it before running per-cluster math, since cluster mismatch is the single most common source of a wrong allocation. Reuse the cluster definition consistently across the run.
Per-SKU lifecycle classification — Tag each SKU as Introduction (< 13 weeks of history), Growth (sell-through accelerating, > 60th-percentile within attribute peer set), Mature (stable sell-through within ±10% week-over-week), Decline (sell-through falling two consecutive periods), or Exit-candidate (negative contribution margin after returns, or stranded inventory > 1.5× target weeks-of-supply with no recovery path). Lifecycle drives the action menu — Decline SKUs are markdown / exit candidates, Mature SKUs are reallocation candidates, Growth SKUs are depth-up candidates, Introduction SKUs are observation candidates.
Productivity-per-foot diagnosis — For each category × door cluster, compute revenue / square-foot and gross-margin / square-foot vs. cluster benchmark. Flag any category × cluster cell more than one standard deviation below benchmark; that is the cell where assortment changes have the most leverage. Avoid recommending changes in cells that are already at benchmark — cycle-time on those is wasted.
Overlap audit — Within each category, score attribute-overlap among active SKUs (color × size × price-tier × occasion). Two SKUs that share > 80% of attribute weight and have one of them under-indexing on sell-through are overlap candidates; one absorbs the other's demand, the loser is an exit candidate. Be careful with assortment depth in basics (overlap is a feature) vs. fashion (overlap is a leak).
Whitespace surfacing — Compare your active attribute combinations to (a) your own search-no-result terms, (b) marketplace bestseller attribute combos in your category, (c) any AI-search query log showing unbranded buying questions you can't answer. Score each whitespace candidate by estimated demand × confidence × adjacency to current capability (vendor exists, supply chain qualified, fits brand tone). Top candidates become add-recommendation entries; weak signals get logged for the next cycle, not pushed.
Always-on action packet — For each SKU within scope, produce one of: HOLD (no change), DEPTH_UP (raise units / facings, justified by sell-through), DEPTH_DOWN (cut units / facings), MARKDOWN (with depth %), REALLOCATE (move stock from cluster A to cluster B with named clusters and units), EXIT (with sell-down plan and write-down estimate), or ADD (new SKU candidate with vendor + first-buy units + door clusters). Each action carries: a one-line rationale, the dollar impact on contribution margin at the period horizon, the confidence band (high / medium / low based on data freshness and history length), and the constraint check (parity, vendor contract, planogram fit, OTB headroom).
Constraint and guardrail pass — Re-run every action against config.yml → parity_rules, vendor_contracts, open_to_buy, and the planogram-fit check. Drop or downscope any action that violates a constraint and replace it with the next-best non-conflicting action. Surface dropped-action conflicts as a separate "needs human policy decision" list, not silently.
Continuous-replan trigger rules — Define which signals would fire an off-cycle rerun before the next scheduled period: a stockout streak > N days on a Mature SKU, a sell-through delta > X% week-over-week on a Growth SKU, a vendor delay confirmed > Y days, a competitive markdown deeper than Z%, a weather outlook two standard deviations off normal in a weather-elastic category. Tune these against config.yml → service_level_target and the merchant's tolerance for re-papering store teams.
Approval-ready packet assembly — Group actions by buyer / planner owner, sort by absolute dollar impact, cap each owner's packet at a length they can review in one sitting (default: 25 actions / owner / cycle; spill the rest into a backlog with the same constraint-checked actions ready for the next cycle). Include a summary header (top 5 movers, top 5 risks, OTB consumed by approved actions if all are accepted).
Write-back plan and rollback window — Specify the system each approved action writes into (PIM for adds, planogram tool for facings, merchandising hub for markdowns, allocation engine for reallocations) and the rollback window — a recommended N-day check-back where the action is auto-flagged for review if the predicted lift hasn't materialized, with the data the buyer would need to roll it back cleanly. Never recommend an irreversible action (full SKU exit, vendor termination) without an explicit human approval gate.
Config-utilization checklist — Confirm the packet uses door_clusters, category_taxonomy, attribute_dictionary, open_to_buy, parity_rules, vendor_contracts, and service_level_target from config.yml rather than generic placeholders.

Output requirements:

Cycle header — Banner / region / period / scope / data-freshness statement
Productivity-per-foot heat-map — Category × door-cluster grid with under-benchmark cells highlighted
Per-SKU action packet — Grouped by buyer / planner, capped at the per-owner length, sorted by dollar impact, each row: SKU, lifecycle, action, units / depth, dollar impact, confidence band, constraint check
Overlap audit — Table of overlap-pair recommendations
Whitespace shortlist — Top whitespace add-candidates with vendor / first-buy / door-cluster fit
Constraint conflict list — Actions blocked by parity, vendor contract, planogram, or OTB — flagged for human policy decision
Continuous-replan trigger rules — Signal definitions for off-cycle reruns
Write-back plan and rollback windows
Config-utilization checklist
Professional formatting appropriate for retail merchandising leadership (not a vendor pitch)
Correct merchandising terminology (sell-through, GMROI, weeks-of-supply, productivity-per-foot, depth vs. breadth, replenishment, end-cap, OTB, planogram, door cluster, lifecycle stage, attribute overlap)
Saved to outputs/ if the user confirms

Example Output

[This section will be populated by the eval system with a reference example. For now, run the skill with sample input to see output quality.]