Brand value retention and depreciation analysis. Triggers: "depreciation rate", "value retention", "residual value", "how fast is it losing value", "which cars hold value", "brand value retention", "EV depreciation", "price trend over time", "brand residual ranking", "depreciation curve", "MSRP parity", "price over sticker", "incentive effectiveness", "geographic value variance", "regional pricing strategy", brand value retention analysis, model depreciation tracking, segment value comparisons, brand retention rankings, or MSRP-to-transaction price tracking for OEM strategy and competitive benchmarking.
From manufacturernpx claudepluginhub marketcheckhub/marketcheck-cowork-plugin --plugin manufacturerThis skill uses the workspace's default tool permissions.
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Calculates TAM/SAM/SOM using top-down, bottom-up, and value theory methodologies for market sizing, revenue estimation, and startup validation.
Date anchor: Today's date comes from the
# currentDatesystem 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.
Frame all analysis as BRAND VALUE RETENTION — how well does your brand hold value vs competitors? Which of your models are depreciating fastest? Where should you focus residual support or pricing strategy?
Load the marketcheck-profile.md project memory file if exists. Extract: brands, states, competitor_brands, country. If missing, ask brand and competitors. US-only (requires get_sold_summary and search_active_cars); if UK, inform not available. Confirm profile.
User is an OEM product planner, brand strategist, or regional manager tracking brand value retention vs competitors for residual support, CPO strategy, and pricing decisions.
| Required | Field | Source |
|---|---|---|
| Yes | Make/Model/segment | Profile or ask |
| Recommended | Model year(s) | Ask |
| Auto/Ask | State | Profile manufacturer.states or ask |
| Optional | Inventory type | New or Used (default: Used) |
| Optional | Comparison | EV vs ICE, SUV vs Sedan, Brand vs Competitor |
| Optional | Time horizon | 30d, 90d, 6mo, 1yr |
Clarify: used vehicle depreciation (secondary market) vs new vehicle MSRP parity (transaction vs sticker). Different workflows.
Use this when a user asks "how fast is our RAV4 losing value" or "depreciation curve for our models."
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.
Get historical sold data at multiple intervals — Make separate calls to get_sold_summary for each lookback period:
average_sale_price at each point. Adjust dates based on today's date.
→ Extract only: average_sale_price per interval. Discard full response.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 price stats. Discard full response.
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.
Build the depreciation curve — Calculate at each time interval:
Compare to competitor model — If competitor brands are in the profile, run the same analysis for the equivalent competitor model and show side-by-side retention curves.
Use this when a user asks "which brands hold value best" or "how does our brand compare on residual value."
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.
Get prior period brand prices — Same call with dates shifted back 6 months. → Extract only: per make — average_sale_price. Discard full response.
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.
Calculate brand retention scores — For each make:
Present the brand ranking with competitive framing:
Use this when a user asks "are our SUVs holding value better than sedans" or "how is EV depreciation for our brand."
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.
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.
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.
Calculate segment trends — For each body type and fuel type:
Deliver with brand context — "Your brand's SUVs retained X% of value vs the segment average of Y%. Your sedans are underperforming at Z% retention. EV depreciation across the market runs at [rate] — your EV models are [above/below] this average."
Use this when a user asks "where do our models hold value best" or "regional pricing strategy."
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.
Get national baseline — Same call without summary_by for national average.
→ Extract only: average_sale_price, sold_count. Discard full response.
Calculate geographic variance — For each state:
Identify patterns and strategic implications — Group states into:
Regional pricing strategy — Present as a ranked table: State, Avg Transaction Price, National Avg, Price Index, Premium/Discount $, Sold Count.
Use this when a user asks "are our new models still commanding premiums" or "how effective are our incentives."
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.
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.
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.
Classify parity status for your models — For each make/model:
Present with incentive strategy framing:
Present: brand value retention headline with tier positioning, depreciation/retention data table(s) with competitive comparison, key signals (acceleration/deceleration, geographic anomalies, MSRP shifts), and actionable recommendation (residual support, CPO, allocation, incentive calibration).