Vehicle depreciation and value retention analysis. Triggers: "depreciation rate", "value retention", "residual value", "how fast is it losing value", "which cars hold value", "EV depreciation", "price trend over time", "brand value ranking", "depreciation curve", "residual forecast", "MSRP parity", "price over sticker", "incentive effectiveness", "geographic value variance", "which states have higher prices", residual value forecasting, segment value comparisons, brand retention rankings, MSRP-to-transaction price tracking.
From marketcheck-cowork-pluginnpx claudepluginhub marketcheckhub/marketcheck-cowork-pluginThis skill uses the workspace's default tool permissions.
references/outcomes.mdProvides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
Fetches up-to-date documentation from Context7 for libraries and frameworks like React, Next.js, Prisma. Use for setup questions, API references, and code examples.
Calculates TAM/SAM/SOM using top-down, bottom-up, and value theory methodologies for market sizing, revenue estimation, and startup validation.
→ Full procedure: read _references/profile-loading.md
Parse marketcheck-profile.md if it exists → extract: state, franchise_brands, dealer_type, country. This skill works fine without a profile.
US-only: This skill requires get_sold_summary. If country == UK: inform "Depreciation tracking requires US sold transaction data and is not available for the UK market." → Full limitations: _references/country-routing.md
Confirm: "Using profile context: [state]"
The primary user is a lender (residual value analyst, portfolio risk manager, or auto finance director) who needs to understand how quickly collateral values are declining to set accurate residual forecasts and manage portfolio exposure. Secondary users include OEM analysts evaluating incentive effectiveness and brand value positioning, and appraisers who need trend-adjusted valuations that account for recent depreciation velocity rather than stale book values.
The following fields may be auto-filled from the dealer profile:
| Required | Field | Source |
|---|---|---|
| Yes | Make and/or Model (or segment) | Always ask |
| Recommended | Model year(s) of interest | Always ask |
| Auto/Ask | Geography (state or zip) | Profile location.state or ask |
| Optional | Inventory type | New or Used (default: Used) |
| Auto/Ask | Dealer type filter | Profile dealer.dealer_type or ask |
| Optional | Comparison dimension | EV vs ICE, SUV vs Sedan, Brand A vs Brand B |
| Optional | Time horizon | 30 days, 90 days, 6 months, 1 year |
Always clarify whether the user wants depreciation of used vehicles (price decline over time on the secondary market) or new vehicle transaction-to-MSRP parity (how much above or below sticker new cars are actually selling). These are different workflows.
get_sold_summary for transaction data. UK dealers cannot use depreciation tracking. Inform them immediately if country == UK.get_sold_summary works on monthly date ranges. For quarterly analysis, call each month separately and combine.(current_avg_price / original_MSRP) × 100. This means a 3-year-old vehicle at 87% retention has lost 13% of its original value.Use this when a user asks "how fast is the RAV4 losing value" or "show me the depreciation curve for a 2022 Civic."
Get current period sold data — Call mcp__marketcheck__get_sold_summary with make, model, inventory_type=Used, date_from set to the first of the current month minus 30 days (e.g., 2026-02-01), date_to set to the last day of that month (e.g., 2026-02-28). If the user specified a state, include state. Record the average_sale_price and sold_count.
Get historical sold data at multiple intervals — Make separate calls to mcp__marketcheck__get_sold_summary for each lookback period to build the curve:
date_from=2026-01-01, date_to=2026-01-31date_from=2025-12-01, date_to=2025-12-31date_from=2025-09-01, date_to=2025-09-30date_from=2025-03-01, date_to=2025-03-31
Record average_sale_price at each point. Adjust the actual dates based on today's date.Get current active market asking price — Call mcp__marketcheck__search_active_cars with year, make, model, car_type=used, stats=price, rows=0. If state/zip was provided, include zip and radius=100 or state in the seller_name filter. This gives the current asking price stats (mean, median, min, max) for unsold inventory — the forward-looking indicator.
Get original MSRP baseline — Call mcp__marketcheck__search_active_cars with the same year, make, model, rows=1, sort_by=price, sort_order=desc to find a representative listing. Then call mcp__marketcheck__decode_vin_neovin with that listing's VIN to extract the original MSRP from the build data. If MSRP is not available from the decode, use the highest transaction price from the 1-year-ago sold data as a proxy ceiling.
Build the depreciation curve — Calculate at each time interval:
Use this when a user asks "are SUVs holding value better than sedans" or "how is EV depreciation compared to ICE."
Get current period segment data — Call mcp__marketcheck__get_sold_summary with ranking_dimensions=body_type, ranking_measure=average_sale_price, date_from (first of prior month), date_to (last of prior month), inventory_type=Used, top_n=10. This returns average transaction prices by body type for the current period.
Get prior period segment data — Call mcp__marketcheck__get_sold_summary with the same parameters but date_from and date_to shifted back 3 months (or the user's chosen comparison window). This gives the baseline for calculating segment-level price movement.
Get fuel type comparison — Call mcp__marketcheck__get_sold_summary with fuel_type_category=EV, date_from (current period), date_to (current period end), inventory_type=Used. Record the average sale price and sold count. Repeat with fuel_type_category=ICE. Repeat both calls for the prior period.
Calculate segment trends — For each body type and fuel type:
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.
Use this when a user asks "which brands hold value best" or "rank the automakers by residual value."
Get current period brand prices — Call mcp__marketcheck__get_sold_summary with ranking_dimensions=make, ranking_measure=average_sale_price, ranking_order=desc, date_from (first of prior month), date_to (last of prior month), inventory_type=Used, top_n=25.
Get prior period brand prices — Call mcp__marketcheck__get_sold_summary with the same parameters but dates shifted back 6 months (or user's preferred comparison window). This establishes the baseline for retention calculation.
Get volume context — Call mcp__marketcheck__get_sold_summary with ranking_dimensions=make, ranking_measure=sold_count, ranking_order=desc, date_from (current period), date_to (current period end), inventory_type=Used, top_n=25.
Calculate brand retention scores — For each make:
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).
Use this when a user asks "where do Tacomas hold value best" or "which states have the highest used car prices."
Get state-level transaction data — Call mcp__marketcheck__get_sold_summary with make, model (from user), summary_by=state, date_from (first of prior month), date_to (last of prior month), inventory_type=Used, limit=5000.
Get national baseline — Call mcp__marketcheck__get_sold_summary with the same make, model, same date range, but without summary_by to get the national average transaction price.
Calculate geographic variance — For each state:
Identify patterns — Group states into:
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.
Use this when a user asks "which new cars are selling over sticker" or "are markups coming down" or "incentive effectiveness."
Get current MSRP parity data — Call mcp__marketcheck__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 (last of prior month), top_n=30.
Get prior period parity data — Call mcp__marketcheck__get_sold_summary with the same parameters but dates shifted back 3 months. This shows the direction of parity movement.
Get volume context — Call mcp__marketcheck__get_sold_summary with inventory_type=New, ranking_dimensions=make,model, ranking_measure=sold_count, ranking_order=desc, date_from (current period), date_to (current period end), top_n=30.
Classify parity status — For each make/model:
Present the parity report — Show a table: Make/Model, Current % Over/Under MSRP, Prior Period %, Change Direction, Sold Volume. Highlight:
→ After assembling results, read references/outcomes.md to frame recommendations with quantified business impact, KPI benchmarks, and action-to-outcome guidance.
Always present results in this structure:
Analysis Summary — What was analyzed (make/model/segment), time period, geography, and inventory type.
Depreciation Headline — One sentence with the key finding (e.g., "The 2022 Toyota RAV4 has retained 87.3% of its original MSRP after 3 years, depreciating at 0.35% per month — outperforming the SUV segment average of 0.52% monthly").
Depreciation Curve / Trend Table
| Period | Avg Transaction Price | Retention % | Monthly Rate | Volume |
|---|---|---|---|---|
| Current Month | $XX,XXX | XX.X% | X.XX% | X,XXX |
| 60 Days Ago | $XX,XXX | XX.X% | X.XX% | X,XXX |
| 90 Days Ago | $XX,XXX | XX.X% | X.XX% | X,XXX |
| 6 Months Ago | $XX,XXX | XX.X% | X.XX% | X,XXX |
| 1 Year Ago | $XX,XXX | XX.X% | X.XX% | X,XXX |
Comparison Context — How the subject compares to its segment, competing models, or prior periods. Always include at least one comparison dimension.
Key Signals — Bullet list of notable trends:
Recommendation — One clear action tied to the user's segment (lender: adjust residual / advance rate; OEM: evaluate incentive / production; appraiser: apply trend adjustment to current valuation). Include the quantified business impact.