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.

Depreciation Tracker — Vehicle Value Retention & Depreciation Intelligence

Profile

Load the marketcheck-profile.md project memory file if exists. Extract: state, franchise_brands, dealer_type, country. If missing, ask. US-only skill (get_sold_summary, search_active_cars). If UK, inform: "Depreciation tracking requires US sold data and is not available for UK." Confirm: "Using profile context: [state]"

User Context

Dealer tracking how quickly inventory/target models lose value for stocking and pricing decisions.

Required	Field	Source
Yes	Make/Model or segment, model year(s)	Ask
Auto/Ask	State/ZIP, dealer_type	Profile or ask
Optional	Inventory type (New/Used), comparison dimension (EV vs ICE, etc.), time horizon	Ask

Clarify: used vehicle depreciation vs new vehicle MSRP parity — different workflows.

Workflow: Make/Model Depreciation Curve

Use this when a user asks "how fast is the RAV4 losing value" or "show me the depreciation curve for a 2022 Civic."

1. Get current period sold data — Call get_sold_summary with make, model, inventory_type=Used, date_from (first of prior month), date_to (end of prior month). Include state if specified. → Extract only: average_sale_price, sold_count. Discard full response.

2. Get historical sold data at multiple intervals — Make separate calls to get_sold_summary for each lookback period:

   • 60 days ago
   • 90 days ago
   • 6 months ago
   • 1 year ago Record average_sale_price at each point. Adjust dates based on today's date. → Extract only: average_sale_price, sold_count per interval. Discard full response.

3. Get current active market asking price — Call search_active_cars with year, make, model, car_type=used, stats=price, rows=0. Include zip/state if available. → Extract only: mean, median, min, max, count for price. Discard full response.

4. Get original MSRP baseline — Call search_active_cars with same YMMT, rows=1, sort_by=price, sort_order=desc. Decode the VIN for MSRP. Fallback: highest 1-year-ago sold price. → Extract only: msrp from decode. Discard full response.

5. Build the depreciation curve — Calculate at each time interval:

   • Retention % = (average_sale_price at interval / original MSRP) × 100
   • Monthly depreciation rate = (price change between consecutive intervals) / (months between intervals)
   • Annualized depreciation rate = monthly rate × 12 Present as a table and describe the curve shape (linear, accelerating, stabilizing).

Workflow: Segment Value Trends

Use this when a user asks "are SUVs holding value better than sedans" or "how is EV depreciation compared to ICE."

1. Get current period segment data — Call get_sold_summary with ranking_dimensions=body_type, ranking_measure=average_sale_price, date_from (first of prior month), date_to (end of prior month), inventory_type=Used, top_n=10. → Extract only: per body_type — average_sale_price, sold_count. Discard full response.

2. Get prior period segment data — Same call with dates shifted back 3 months (or user's chosen comparison window). → Extract only: per body_type — average_sale_price, sold_count. Discard full response.

3. Get fuel type comparison — Call get_sold_summary with fuel_type_category=EV, current period dates, inventory_type=Used. Repeat with fuel_type_category=ICE. Repeat both for prior period. → Extract only: average_sale_price, sold_count per fuel_type per period. Discard full response.

4. Calculate segment trends — For each body type and fuel type:

   • Period-over-period price change = (current avg price - prior avg price) / prior avg price × 100
   • Volume change = (current sold_count - prior sold_count) / prior sold_count × 100
   • Flag segments where price declined more than 3% as "accelerating depreciation"
   • Flag segments where price held within ±1% as "stable"
   • Flag segments where price increased as "appreciating" (rare but happens with supply constraints)

5. Deliver the segment comparison — Present a ranked table from strongest retention to weakest. Highlight the EV vs ICE gap specifically (this is the most commonly requested comparison). Include volume context — a segment with strong prices but falling volume may be about to soften.

Workflow: Brand Residual Ranking

Use this when a user asks "which brands hold value best" or "rank the automakers by residual value."

1. Get current period brand prices — Call get_sold_summary with ranking_dimensions=make, ranking_measure=average_sale_price, ranking_order=desc, date_from (first of prior month), date_to (end of prior month), inventory_type=Used, top_n=25. → Extract only: per make — average_sale_price. Discard full response.

2. Get prior period brand prices — Same call with dates shifted back 6 months (or user's preferred comparison window). → Extract only: per make — average_sale_price. Discard full response.

3. Get volume context — Call get_sold_summary with ranking_dimensions=make, ranking_measure=sold_count, ranking_order=desc, current period dates, inventory_type=Used, top_n=25. → Extract only: per make — sold_count. Discard full response.

4. Calculate brand retention scores — For each make:

   • Retention % = current average_sale_price / prior average_sale_price × 100
   • Volume trend = current sold_count vs prior sold_count (indicates demand strength)
   • Rank brands by retention % descending
   • Separate into tiers: Tier 1 (>98% retention), Tier 2 (95-98%), Tier 3 (90-95%), Tier 4 (<90%)

5. Present the brand ranking — Show a ranked table with: Rank, Make, Current Avg Price, Prior Avg Price, Retention %, Volume, Tier. Highlight notable movers (brands that jumped or dropped tiers since the last period).

Workflow: Geographic Depreciation Variance

Use this when a user asks "where do Tacomas hold value best" or "which states have the highest used car prices."

1. Get state-level transaction data — Call get_sold_summary with make, model, summary_by=state, date_from (first of prior month), date_to (end of prior month), inventory_type=Used, limit=5000. → Extract only: per state — average_sale_price, sold_count. Discard full response.

2. Get national baseline — Same call without summary_by for national average. → Extract only: average_sale_price, sold_count. Discard full response.

3. Calculate geographic variance — For each state:

   • Price index = state average_sale_price / national average_sale_price × 100 (100 = national average)
   • Premium/discount = state price - national price in dollars
   • Sort by price index descending to show where vehicles command the highest premiums

4. Identify patterns — Group states into:

   • Premium markets (index > 105): vehicles retain more value, typically lower supply or higher demand
   • At-national-average (index 95-105): mainstream pricing
   • Discount markets (index < 95): vehicles depreciate faster, often oversupplied or lower demand Note regional patterns (e.g., trucks command premiums in mountain/rural states, EVs hold better in CA/Northeast).

5. Deliver the geographic map — Present as a ranked table: State, Avg Transaction Price, National Avg, Price Index, Premium/Discount $, Sold Count. Highlight the top 5 and bottom 5 states for the specific vehicle.

Workflow: MSRP Parity Tracker

Use this when a user asks "which new cars are selling over sticker" or "are markups coming down" or "incentive effectiveness."

1. Get current MSRP parity data — Call get_sold_summary with inventory_type=New, ranking_dimensions=make,model, ranking_measure=price_over_msrp_percentage, ranking_order=desc, date_from (first of prior month), date_to (end of prior month), top_n=30. → Extract only: per make/model — price_over_msrp_percentage. Discard full response.

2. Get prior period parity data — Same call with dates shifted back 3 months. → Extract only: per make/model — price_over_msrp_percentage. Discard full response.

3. Get volume context — Call get_sold_summary with inventory_type=New, ranking_dimensions=make,model, ranking_measure=sold_count, ranking_order=desc, current period dates, top_n=30. → Extract only: per make/model — sold_count. Discard full response.

4. Classify parity status — For each make/model:

   • Above MSRP (price_over_msrp_percentage > 0): dealer markups or strong demand
   • At MSRP (price_over_msrp_percentage between -1% and 0%): balanced market
   • Below MSRP (price_over_msrp_percentage < -1%): incentives/discounts active
   • Trend direction: compare current vs prior period to show if markups are growing or shrinking

5. Present the parity report — Show a table: Make/Model, Current % Over/Under MSRP, Prior Period %, Change Direction, Sold Volume. Highlight:

   • Models that flipped from above-MSRP to below (incentive programs taking effect)
   • Models still commanding premiums (constrained supply or high demand)
   • Models with deepening discounts (potential oversupply or model-year transition)

Output

Present: summary headline, depreciation curve/trend table (period, avg price, retention %, monthly rate, volume), comparison context (segment/brand/geographic), key signals (bullets), and one actionable recommendation with quantified business impact.