Insurance valuation with comparable evidence. Triggers: "appraise this vehicle", "what's it worth", "insurance valuation", "comparable analysis", "fair market value", "pre-loss value", "appraisal report", "settlement valuation", "vehicle valuation", "claims appraisal", building a defensible, comparable-backed vehicle valuation for insurance claims, total-loss determinations, or settlement pricing decisions.
From insurernpx claudepluginhub marketcheckhub/marketcheck-cowork-plugin --plugin insurerThis skill uses the workspace's default tool permissions.
Provides 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.
Load the marketcheck-profile.md project memory file if exists. Extract: zip, state, radius, total_loss_threshold_pct, default_comp_radius. If missing, ask for ZIP and radius. US-only (all tools: decode, predict, search, history); UK not supported. Confirm profile.
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 |
User is an insurance adjuster, claims analyst, or total-loss specialist needing a defensible, comparable-backed valuation for settlement offers and dispute resolution.
| Required | Field | Source |
|---|---|---|
| Yes | VIN or YMMT | Ask |
| Yes | Odometer reading | Ask |
| Auto/Ask | ZIP, radius | Profile or ask |
| Recommended | Pre-loss condition (Clean/Average/Rough), purpose | Ask |
| Optional | CPO status | Ask |
VIN provided → decode first. Assumed trims lose credibility in disputes.
Use this for formal insurance valuations, total-loss claims, or any situation where the valuation must be supported by cited comparables for dispute resolution.
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=100 (wider for defensible claims), 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:
Total-loss threshold calculation — Using the condition-adjusted FMV:
Present the insurance valuation report — Deliver a structured report with the valuation, every cited comparable (VIN, price, miles, dealer, distance), total-loss threshold, methodology notes, and confidence assessment.
Use this when the user needs to understand how values differ across geographies, important for understanding settlement variation by region and ensuring fair market value reflects the claimant's local market.
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. Regional price variance directly impacts settlement values — the same vehicle may warrant a higher settlement in a premium market.
Present the regional map — Show the price variance table and highlight any market where the price delta exceeds 5%. Note: "Settlement offers should reflect the claimant's local market. Regional variance of X% supports adjusting the FMV for geographic factors."
Use this when understanding the gap between wholesale and retail values, critical for determining fair settlement amounts and salvage value estimates.
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=100, 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:
Use this when the user asks "what has this VIN been listed at over time" or needs to understand the pricing history of a specific unit for claims documentation.
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, MSRP. 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. Note any pre-loss listing history that establishes the vehicle's market value trajectory — useful for supporting or challenging settlement offers.
Present: vehicle ID summary, valuation table (franchise/independent/condition-adjusted FMV/comp ranges/confidence), total-loss threshold and determination, settlement range (low/mid/high), comparable data tables (active retail + sold transactions with VIN/price/miles/dealer), and methodology notes with condition adjustments, threshold source, and caveats.