Dealer deep-dive for sales call prep. Triggers: "tell me about [dealer]", "dealer brief", "prep for dealer meeting", "dealer intelligence", "profile this dealer for a sales call", "what does [dealer] sell", "dealer inventory analysis", "meeting prep for [dealer]", deep-dive on a specific dealer to prepare for a sales call with inventory profile, lending fit, and talking points.
From lender-salesnpx claudepluginhub marketcheckhub/marketcheck-cowork-plugin --plugin lender-salesThis skill uses the workspace's default tool permissions.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Designs, audits, and improves analytics tracking systems using Signal Quality Index for reliable, decision-ready data in marketing, product, and growth.
Enforces A/B test setup with gates for hypothesis locking, metrics definition, sample size calculation, assumptions checks, and execution readiness before implementation.
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.
Load the marketcheck-profile.md project memory file if exists. Extract: price_range_min, price_range_max, preferred_year_range, max_mileage, approved_makes, approved_segments, ltv_max_pct, lending_type, country, zip, radius. If missing, ask minimum fields. US: search_active_cars, predict_price_with_comparables, get_sold_summary. UK: search_uk_active_cars only (inventory profile only, no LTV analysis). Confirm: "Using profile: [company], [lending_type]". All preference values from profile — do not re-ask.
Lender sales rep preparing for a dealer meeting. Need to know: what does this dealer sell, how much of it can we finance, what are their pain points (aging, floor plan burden), and what should I say in the meeting.
dealer_id for precision. If only a name is given, use search_active_cars with seller_name to find the dealer_id first, but warn the user if multiple dealers match. If a domain is given, use source=[domain].predict_price_with_comparables returns a statistical prediction based on comparables, not a certified valuation. Always label LTV calculations as "estimated" and note the comp count backing each prediction. If a prediction returns fewer than 5 comps, flag it as "low confidence."floor_plan_rate, use that. Otherwise label as "(estimated at $35/day industry average)."predict_price_with_comparables is US-only. For UK dealers, skip steps 4 and 6 (LTV calculations) and note "LTV analysis unavailable for UK market."car_type=used unless the user explicitly asks about new. Mixing new and used produces misleading DOM and pricing metrics.Get full inventory profile — Call mcp__marketcheck__search_active_cars with dealer_id (or source for domain), car_type=used, facets=make|0|20|1,body_type|0|10|1,year|0|10|1, stats=price,dom,miles, rows=0.
→ Extract only: total_count, avg_price, median_price, avg_dom, avg_miles, make distribution, body_type distribution, year distribution. Discard full response.
Get aged inventory — Call mcp__marketcheck__search_active_cars with same dealer, sort_by=dom, sort_order=desc, rows=15.
→ Extract only: per vehicle — vin, year, make, model, trim, price, miles, dom. Discard full response.
Overlay lending criteria — Call mcp__marketcheck__search_active_cars with same dealer, price_range=[min]-[max], year=[year_range], miles_range=0-[max_mileage], rows=0, stats=price.
→ Extract only: matching_count, avg_price of matching units. Discard full response.
LTV spot-check — For the top 5 matching units by price (representative sample), call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip (dealer's zip), dealer_type matching dealer's type.
→ Extract only: predicted_price per VIN. Calculate estimated LTV = listed_price / predicted_price × 100. Discard full response.
Local market context — Call mcp__marketcheck__get_sold_summary with state (dealer's state), inventory_type=Used, ranking_dimensions=make,model, ranking_measure=sold_count, ranking_order=desc, top_n=10, prior month dates.
→ Extract only: top models by volume. Discard full response.
Calculate dealer brief metrics:
Generate talking points:
Suggest products:
── Dealer Intelligence Brief ── [Month Year] ───────────────
DEALER CARD
Name: [Dealer Name]
Location: [City], [State] [ZIP]
Type: [Franchise/Independent]
Total Used Units: [XX]
Avg Price: $XX,XXX
Avg DOM: XX days
Lending Fit: XX% ([X] of [Y] units qualify)
INVENTORY BREAKDOWN
Make Mix: [Make1] XX%, [Make2] XX%, [Make3] XX%, Other XX%
Body Types: [SUV] XX%, [Sedan] XX%, [Truck] XX%, Other XX%
Year Range: [oldest]-[newest], median [year]
Price Range: $X,XXX - $XX,XXX, median $XX,XXX
LENDING FIT ANALYSIS
Matching Units: [X] of [Y] (XX%)
Avg Listed Price (matching): $XX,XXX
Estimated Avg LTV: XX.X% (based on [N]-unit sample)
LTV Distribution:
Under 100% (under-advanced): X units
100-110% (standard): X units
110-[max]% (elevated): X units
Over [max]% (exceeds limit): X units
AGING ANALYSIS
Units > 60 DOM: [X] ([XX]% of lot)
Units > 90 DOM: [X]
Units > 120 DOM: [X]
Est. Floor Plan Burden: $X,XXX/month (aged units, est. $35/day)
TALKING POINTS
1. "[talking point with specific numbers]"
2. "[talking point with specific numbers]"
3. "[talking point with specific numbers]"
4. "[talking point with specific numbers]"
5. "[talking point with specific numbers]"
RECOMMENDED PRODUCTS
- [Product]: [reason based on data]
- [Product]: [reason based on data]
LOCAL MARKET CONTEXT
Top sellers in [state] last month: [Model1], [Model2], [Model3]
Source: MarketCheck market data, [Month Year].
Dealer brief card: Name, City, State, Total Units, Avg Price, Avg DOM, Lending Fit %. Inventory breakdown: make mix, body type mix, year mix. Lending fit analysis: matching units, avg LTV, LTV distribution. Aging analysis: aged units count, floor plan burden estimate. Top 5 talking points for the meeting. Recommended lending products. Local market context (what's selling).