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.

Inventory Intelligence — Data-Driven Stocking & Aging Analysis

Turn sold market data and live supply counts into actionable stocking recommendations. Replace gut-instinct buying with demand-to-supply ratios, aging alerts, turn-rate benchmarks, and optimal new-vs-used mix targets.

Dealer Group Profile (Load First)

Load the marketcheck-profile.md project memory file. If missing, prompt /onboarding and ask minimum fields.

Extract ALL locations from dealer_group.locations[]. For each location: name, dealer_id, web_domain, dealer_type, franchise_brands, zip (US) or postcode (UK), state (US) or region (UK), country. Extract group preferences: dom_aging_threshold.

Location selection: This skill analyzes one location at a time. If the group has multiple locations, ask "Which location?" and use that location's dealer_id, web_domain, zip/postcode, state/region, and dealer_type for ALL API calls below. Never mix zip/state values across locations.

US: all tools. UK: search_uk_active_cars + search_uk_recent_cars only (skip Market Demand, Turn Rate, New vs Used Mix, D/S Ratio). Confirm: "Running inventory intelligence for: [selected location name] ([zip], [state])"

User Context

Inventory director or location GM making data-driven stocking decisions using demand-to-supply ratios, aging alerts, and turn-rate benchmarks.

Auto-loaded from selected location: state, dealer_id, web_domain, franchise_brands, dealer_type, zip. Auto-loaded from preferences: default_inventory_type ("used" | "new" | "both"; default "used" if not set) — apply as car_type/inventory_type in all calls unless user overrides in their request. Ask: timeframe (default: last full month). For lot-level workflows: use dealer_id first; if null, use web_domain; if both null, ask the user. Never mix new and used data in the same report section.

Workflow: Market Demand Snapshot

Understand what is actually selling in the location's market before making any stocking decisions.

1. Call mcp__marketcheck__get_sold_summary with:

   • date_from / date_to: target month first-to-last day
   • state: selected location's state code
   • dealer_type: selected location's dealer type (Franchise or Independent)
   • ranking_dimensions: make,model
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 20 → Extract only: per make/model — sold_count, average_sale_price, average_days_on_market. Discard full response.

2. Call mcp__marketcheck__get_sold_summary with the same date and location filters, but:

   • ranking_dimensions: body_type
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 10 → Extract only: per body_type — sold_count, share %. Discard full response.

3. Present results as two ranked tables:

   • Top 20 Models by Sales Volume — columns: Rank, Make, Model, Sold Count, Avg Sale Price, Avg DOM
   • Sales by Body Type — columns: Body Type, Sold Count, Share of Total (%)

4. Highlight any body type or model where the location's franchise brand appears in the top 5, and flag segments where it does not appear (potential conquest or gap opportunities).

Workflow: What Should I Stock? (Demand-to-Supply Ratio)

Compare what the market is buying against what dealers currently have listed. Vehicles with high demand and low supply represent stocking opportunities.

1. Call mcp__marketcheck__get_sold_summary with:

   • date_from / date_to: most recent full month
   • state: selected location's state
   • dealer_type: selected location's dealer type
   • ranking_dimensions: make,model
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 30 → Extract only: per make/model — sold_count. Discard full response.

2. Call mcp__marketcheck__search_active_cars with:

   • state: selected location's state (or zip + radius if provided)
   • car_type: used (or new based on user focus)
   • seller_type: dealer
   • dealer_type: selected location's dealer type
   • facets: make|0|50|2,model|0|50|2
   • rows: 0 → Extract only: per make/model — active count from facets. Discard full response.

3. For each of the top 30 sold make/model combinations, look up the active supply count from the facet results.

4. Calculate Demand-to-Supply Ratio = Sold Count (monthly) / Active Supply Count.

   • Ratio > 1.5 = Under-supplied (strong stocking opportunity)
   • Ratio 0.8 - 1.5 = Balanced
   • Ratio < 0.8 = Over-supplied (avoid stocking more)

5. Present a single table sorted by demand-to-supply ratio descending:

   • Columns: Make, Model, Monthly Sold, Active Supply, D/S Ratio, Signal (Under-supplied / Balanced / Over-supplied)
   • Bold or call out the top 5 under-supplied models as priority acquisition targets.

Workflow: Aging Inventory Alert

Identify units on the selected location's lot that have exceeded healthy DOM thresholds and assess their current market value.

Multi-agent approach: Use the lot-scanner and lot-pricer agents for complete, paginated results.

Step 1 — Pull aged inventory (paginated):

Use the Agent tool to spawn the dealership-group:lot-scanner agent with this prompt:

Pull aging inventory for dealer_id=[dealer_id] (or source=[web_domain] if dealer_id unavailable), country=[country], car_type=used, sort_by=dom, sort_order=desc, dom_range=[dom_aging_threshold]-999. Paginate through all results.

This ensures ALL aged units are captured, not just the first 25.

Step 2 — Price aged units (US only):

After lot-scanner returns, use the Agent tool to spawn the dealership-group:lot-pricer agent with this prompt:

Price these aging vehicles: [pass vehicle list from lot-scanner]. zip=[zip], dealer_type=[dealer_type], floor_plan_per_day=[floor_plan_per_day], aging_threshold=[dom_aging_threshold].

UK dealers: Price inline using comp medians from search_uk_active_cars.

Step 3 — Build the report from lot-pricer output:

3. Build an Aging Inventory Report table:

   • Columns: VIN (last 6), Year, Make, Model, Trim, DOM (days), Listed Price, Predicted Market Price, Price Gap ($), Price Gap (%), Recommendation
   • Recommendation logic:
     • Price Gap > +10% (overpriced): "Reduce price to market — estimated $X reduction"
     • Price Gap -5% to +10%: "At market — consider wholesale if DOM > 90"
     • Price Gap < -5% (underpriced): "Below market — review for quick retail sale"

4. Summarize total aged inventory exposure:

   • Count of units > 60 DOM, > 90 DOM, > 120 DOM
   • Estimated total floor plan interest burn (units x avg $30/day x avg DOM over 60)
   • Total overpricing gap across all aged units

Workflow: Turn Rate by Segment

Benchmark how quickly different vehicle segments move in the local market to inform category-level stocking.

1. Call mcp__marketcheck__get_sold_summary with:

   • date_from / date_to: most recent full month
   • state: selected location's state
   • dealer_type: selected location's dealer type
   • ranking_dimensions: body_type
   • ranking_measure: average_days_on_market
   • ranking_order: asc
   • top_n: 10 → Extract only: per body_type — average_days_on_market, sold_count. Discard full response.

2. Call mcp__marketcheck__get_sold_summary with:

   • Same date/location/dealer_type filters
   • ranking_dimensions: make,model
   • ranking_measure: average_days_on_market
   • ranking_order: asc
   • top_n: 10 → Extract only: per make/model — average_days_on_market, sold_count. Discard full response.

3. Also call with ranking_order: desc and top_n: 10 to get the slowest turning models. → Extract only: per make/model — average_days_on_market, sold_count. Discard full response.

4. Present three tables:

   • Turn Rate by Body Type — columns: Body Type, Avg DOM, Sold Count, Interpretation
   • Fastest Turning Models (Top 10) — columns: Make, Model, Avg DOM, Sold Count
   • Slowest Turning Models (Bottom 10) — columns: Make, Model, Avg DOM, Sold Count

5. Provide a recommendation: "Focus acquisition on segments with Avg DOM < X days (market median). Avoid over-stocking in segments where Avg DOM exceeds Y days unless priced aggressively."

Workflow: New vs Used Mix Analysis

Determine the optimal new-to-used inventory ratio based on what the market is actually absorbing.

1. Call mcp__marketcheck__get_sold_summary with:

   • date_from / date_to: most recent full month
   • state: selected location's state
   • inventory_type: New
   • ranking_dimensions: make
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 10 → Extract only: per make — sold_count. Discard full response.

2. Call mcp__marketcheck__get_sold_summary with same filters but:

   • inventory_type: Used → Extract only: per make — sold_count. Discard full response.

3. Call mcp__marketcheck__search_active_cars for current supply:

   • state: selected location's state
   • car_type: new
   • seller_type: dealer
   • facets: make|0|30|2
   • rows: 0 → Extract only: per make — active count from facets. Discard full response.

4. Repeat with car_type: used. → Extract only: per make — active count from facets. Discard full response.

5. Calculate:

   • Market Absorption Ratio: New Sold / (New Sold + Used Sold) for the market
   • Current Supply Ratio: New Active / (New Active + Used Active) on lots
   • Gap: If supply ratio significantly exceeds absorption ratio, the market is over-indexed on new (or used)

6. Present as:

   • Summary box: "Market absorbed X% New / Y% Used last month. Dealers currently stock A% New / B% Used. Recommendation: [shift toward new/used/hold steady]."
   • Breakdown table by make with columns: Make, New Sold, Used Sold, New % of Sales, Current New Supply, Current Used Supply, Supply vs Demand Alignment

Output

Present: headline number (e.g., D/S ratio or aged unit count with dollar impact), ranked data tables (models, body types, aging units), clear signal labels (UNDER-SUPPLIED / BALANCED / OVER-SUPPLIED), and 3 specific actionable items with dollar estimates. Cite data period.