Comparable-backed vehicle valuation. Triggers: "appraise this vehicle", "what's it worth", "trade-in value", "comparable analysis", "fair market value", "wholesale vs retail", "appraisal report", "how much should I offer", "vehicle valuation", defensible valuations for trade-ins, acquisitions, or retail pricing decisions.
From dealership-groupnpx claudepluginhub marketcheckhub/marketcheck-cowork-plugin --plugin dealership-groupThis skill uses the workspace's default tool permissions.
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Analyzes competition with Porter's Five Forces, Blue Ocean Strategy, and positioning maps to identify differentiation opportunities and market positioning for startups and pitches.
Load the marketcheck-profile.md project memory file. If exists, extract ALL locations from dealer_group.locations[] — for each: name, zip (US) or postcode (UK), dealer_type, cpo_program, cpo_certification_cost, country. Extract group preferences: default_radius_miles (→ radius).
Location selection: If multiple locations exist, confirm which location this appraisal is for — the location's zip/postcode determines the local comparable market. Using the wrong location's zip returns irrelevant comps. Use that location's zip, dealer_type, and CPO settings for all calls below. If profile missing, ask for ZIP+radius directly.
US: all tools (decode_vin_neovin, predict_price_with_comparables, search_active_cars, search_past_90_days, get_car_history). UK: search_uk_active_cars + search_uk_recent_cars only (comp median, no VIN decode/history). Confirm: "Appraising for: [selected location name] ([zip])"
When appraising a vehicle, determine if it is Certified Pre-Owned (CPO):
is_certified=true.get_car_history shows the vehicle currently listed as certified.When the vehicle IS CPO, the Full Comparable Appraisal workflow adds these steps:
predict_price_with_comparables with is_certified=true to get the certified market value.predict_price_with_comparables WITHOUT is_certified to get the standard market value.search_active_cars with the YMMT filters PLUS is_certified=true to find certified-only comparables.In the Valuation Summary output, add:
| Measure | Value |
|---|---|
| CPO Predicted Retail Value | $XX,XXX |
| Non-CPO Predicted Retail Value | $XX,XXX |
| CPO Premium | +$X,XXX (+X.X%) |
| Active CPO Comps | N within radius |
| Active Non-CPO Comps | N within radius |
For the Trade-In Quick Appraisal: if CPO, note the premium but keep the quick format. Show: "CPO Value: $XX,XXX | Standard Value: $XX,XXX | Premium: +$X,XXX"
Dealer group manager or trade-in desk manager needing a defensible, comparable-backed valuation for trade-ins, acquisitions, or retail pricing.
| Required | Field | Source |
|---|---|---|
| Yes | VIN or YMMT, odometer | Ask |
| Auto/Ask | ZIP, radius | Profile or ask |
| Recommended | Condition, purpose, CPO status | Ask |
VIN provided → decode first (US only) to lock in exact specs.
Use this for formal appraisals, insurance claims, or any situation where the valuation must be supported by cited comparables.
Decode VIN — Call mcp__marketcheck__decode_vin_neovin with vin.
→ Extract only: year, make, model, trim, body_type, drivetrain, engine, transmission. Discard full response.
Predict price — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip, dealer_type=franchise, is_certified if applicable.
→ Extract only: predicted_price, comparable VINs with prices and miles. Discard full response.
Pull active comps — Call mcp__marketcheck__search_active_cars with YMMT from step 1, zip, radius=75, miles_range=<odo-15k>-<odo+15k>, car_type=used, sort_by=price, sort_order=asc, rows=20.
→ Extract only: per listing — VIN, price, miles, dealer_name, distance, dom. Discard full response.
Pull sold transactions — Call mcp__marketcheck__search_past_90_days with same YMMT + location filters, sold=true.
→ Extract only: per listing — VIN, sold_price, miles, dealer_name, sale_date. Discard full response.
Synthesize the valuation — Combine all three data sources:
Present the appraisal report — Deliver a structured report with the valuation, every cited comparable (VIN, price, miles, dealer, distance), methodology notes, and confidence assessment.
Use this when speed matters — the customer is at the desk and the dealer needs a number in under 60 seconds.
Predict price — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip, dealer_type=franchise.
→ Extract only: predicted_price, top comparable VINs with prices and miles. Discard full response.
Pull tight comps — Call mcp__marketcheck__search_active_cars with YMMT, zip, radius (from profile default_radius_miles, minimum 75), car_type=used, sort_by=price, sort_order=asc, rows=5.
→ Extract only: per listing — price, miles, dealer_name, dom. Discard full response.
Deliver the quick value — Present:
Use this when the user needs to understand how values differ across geographies, common for multi-state dealer groups or relocation decisions.
Primary market stats — Call mcp__marketcheck__search_active_cars with year, make, model, zip, radius=100, stats=price,miles, rows=0, car_type=used.
→ Extract only: mean, median, min, max, count for price and miles. Discard full response.
Comparison market stats — Repeat step 1 for each additional ZIP. → Extract only: mean, median, count per market. Discard full response.
Sold summary by state — Call mcp__marketcheck__get_sold_summary with make, model, inventory_type=Used, summary_by=state, ranking_measure=average_sale_price, ranking_order=desc, top_n=10.
→ Extract only: per state — average_sale_price, sold_count. Discard full response.
Calculate regional variance — Build a comparison table: market, median price, mean price, sample size, and delta from the lowest market. Identify arbitrage opportunities where the same vehicle sells for significantly more in one region.
Present the regional map — Show the price variance table and highlight any market where the price delta exceeds 5% — these represent real arbitrage or relocation value.
Use this when the user needs to understand the gap between wholesale and retail values, critical for trade-in offers and auction buying decisions.
Predict franchise (retail) price — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip, dealer_type=franchise.
→ Extract only: predicted_price. Discard full response.
Predict independent (wholesale-proxy) price — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip, dealer_type=independent.
→ Extract only: predicted_price. Discard full response.
Pull franchise listings — Call mcp__marketcheck__search_active_cars with YMMT, zip, radius=75, dealer_type=franchise, car_type=used, sort_by=price, sort_order=asc, rows=10.
→ Extract only: per listing — price, miles, dealer_name; plus median. Discard full response.
Pull independent listings — Call mcp__marketcheck__search_active_cars with same filters, dealer_type=independent, rows=10.
→ Extract only: per listing — price, miles, dealer_name; plus median. Discard full response.
Calculate the spread — Present:
Note: When the location has a dealer_type, highlight which price is their primary market. For franchise locations, the franchise price is the primary retail benchmark. For independent locations, the independent price is the primary benchmark. Always show both.
Use this when the user asks "what has this VIN been listed at over time" or needs to understand depreciation patterns for a specific unit.
Pull listing history — Call mcp__marketcheck__get_car_history with vin, sort_order=asc.
→ Extract only: per event — date, dealer_name, price, dom. Discard full response.
Decode VIN — Call mcp__marketcheck__decode_vin_neovin with vin.
→ Extract only: year, make, model, trim. Discard full response.
Build the trajectory — From the history, extract each listing event: date, dealer, asking price, and DOM at that dealer. Calculate:
Current market context — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip.
→ Extract only: predicted_price. Discard full response.
Present the timeline — Show a chronological table of all listings with price, dealer, and DOM. Highlight any unusual patterns (rapid dealer hops, price increases between dealers suggesting reconditioning, or steep drops suggesting undisclosed issues).
Present: vehicle identification (VIN, YMMT, specs, odometer), valuation summary table (predicted retail, active comp range, sold transaction range, recommended value, confidence level), active and sold comparable tables with VIN/price/miles/dealer, and methodology notes with caveats.