cross-location-balancer

name	cross-location-balancer
description	Inter-store inventory transfer recommendations. Triggers: "transfer opportunities", "balance inventory", "which store needs what", "inter-store transfer", "move vehicles between locations", "inventory balancing", "rebalance my lot", "cross-location optimization", identifying vehicles to move between locations for faster turns.
version	0.1.0

Date anchor: Today's date comes from the # currentDate system context. Compute ALL relative dates from it. Example: if today = 2026-03-14, then "prior month" = 2026-02-01 to 2026-02-28, "current month" (most recent complete) = February 2026, "three months ago" = December 2025. Never use training-data dates.

get_sold_summary parameter safety:

• Always set inventory_type explicitly (New or Used) — omitting it defaults to New, returning zero results for used-vehicle queries
• Always set limit: 5000 — the default (1000) silently truncates when (months × states × ranking combos) exceeds 1000 rows
• For volume totals, use ranking_dimensions: dealership_group_name (or the single relevant dimension) — never use the default make,model,body_type which creates ~150K rows for national 3-month queries
• Use separate calls for totals vs breakdowns — don't combine in one call

Cross-Location Balancer — Inter-Store Inventory Transfer Recommendations

User Profile (Required)

Load the marketcheck-profile.md project memory file. If missing, prompt /onboarding. Requires 2+ locations. Extract: locations[] (dealer_id, name, zip, state, dealer_type), preferences, country. US-only for demand data. Confirm: "Analyzing transfer opportunities across [N] locations"

User Context

Dealer group operations manager allocating vehicles across rooftops — moving surplus aged units to under-stocked locations to save floor plan costs.

Workflow: Transfer Opportunity Analysis

Step 1 — Get each location's inventory mix

For each location, use the Agent tool to spawn the dealership-group:lot-scanner agent:

Fetch inventory facets for dealer_id=[dealer_id], country=[country]. Mode: facets_only Facets: make|0|20|1, model|0|30|1, body_type|0|10|1 Also get: total_count, and aging facets if available

→ Extract only: per location — total_count, body_type counts, make/model counts. Discard full response.

Step 2 — Get local demand for each location's market

For each location (US only), call mcp__marketcheck__get_sold_summary with:

• state: the location's state
• zip: the location's zip (with radius)
• inventory_type: Used
• ranking_dimensions: body_type
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 10
• limit: 5000
• date_from / date_to: prior month

→ Extract only: per body_type — sold_count, ranking position. Discard full response.

Step 3 — Calculate location-level gaps

For each location:

• Dealer mix % = units in category / total units x 100
• Market demand % = sold in category / total sold x 100
• Gap = Market demand % - Dealer mix %
• Positive gap = location is UNDER-STOCKED in this category (needs more)
• Negative gap = location is OVER-STOCKED (has too many)

Step 4 — Match senders with receivers

Cross-reference all locations to find transfer pairs:

• Sender: Location A has gap < -5% in a category (over-stocked) AND aged units in that category
• Receiver: Location B has gap > +5% in the same category (under-stocked)

For each potential transfer:

• Transport cost estimate = distance between locations x $1.50/mile (or flat rate if adjacent)
• Expected DOM improvement = difference in avg DOM for that category between locations
• Floor plan savings = expected DOM improvement x $[floor_plan_cost/day]
• Net benefit = floor plan savings - transport cost

Only recommend transfers where net benefit > $500.

Step 5 — Identify specific units to transfer

For the top transfer opportunities, get the specific aged units from the sender location:

• Units in the over-stocked category with DOM > aging_threshold
• Sort by DOM descending (move the oldest first)

Output

CROSS-LOCATION TRANSFER RECOMMENDATIONS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
[Group Name] | [N] locations analyzed

INVENTORY MIX BY LOCATION
Category    | [Location 1]      | [Location 2]      | [Location 3]      | Market Demand
            | Count (Share)     | Count (Share)      | Count (Share)     | (Share)
------------|-------------------|-------------------|-------------------|-------------
SUV         | XX (XX%)          | XX (XX%)          | XX (XX%)          | XX%
Pickup      | XX (XX%)          | XX (XX%)          | XX (XX%)          | XX%
Sedan       | XX (XX%)          | XX (XX%)          | XX (XX%)          | XX%
...

RECOMMENDED TRANSFERS (sorted by net benefit)

Transfer 1: [Category] from [Location A] → [Location B]
  Units to move: [N] (oldest first)
  Reason: [A] has XX% surplus, [B] has XX% deficit in [category]
  Transport cost: ~$XXX
  Expected DOM improvement: XX days faster at [B]
  Floor plan savings: ~$X,XXX
  Net benefit: $X,XXX
  Specific units:
    VIN (last 8) | Year Make Model | DOM at [A] | Listed Price
    -------------|-----------------|-----------|-------------
    [unit 1]     | 22 RAV4 XLE     | 72 days   | $28,500
    [unit 2]     | 21 CR-V EX      | 65 days   | $26,200

Transfer 2: ...

TRANSFER SUMMARY
  Total recommended transfers: [N] units
  Total transport cost: ~$X,XXX
  Total expected floor plan savings: ~$XX,XXX
  Net benefit: ~$XX,XXX over [X] months

Important Notes

• This skill requires dealer_id for all locations — locations without a dealer_id are skipped.
• US-only for demand data: Demand analysis uses get_sold_summary. UK locations can still be analyzed for inventory mix but without market demand comparison.
• Transport cost is estimated. Actual costs depend on the group's logistics setup.
• Only recommend transfers for aged units (DOM > threshold) — don't move fresh inventory.
• If two locations are in different states, note potential registration and title considerations.
• For groups with 5+ locations, focus on the 3 highest-impact transfer pairs to keep it actionable.