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From auction-house
Deep-dive dealer profile for auction engagement. Triggers: "tell me about [dealer]", "dealer profile", "should I reach out to this dealer", "dealer engagement analysis", "profile this dealer", "is this dealer a good prospect", "dealer inventory analysis", "what does [dealer] need", deep-dive on one specific dealer to understand inventory health, likely buying needs, and consignment opportunities.
npx claudepluginhub marketcheckhub/marketcheck-cowork-plugin --plugin auction-houseHow this skill is triggered — by the user, by Claude, or both
Slash command
/auction-house:dealer-engagement-scorerThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
> **Date anchor:** Today's date comes from the `# currentDate` system 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.
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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: zip/postcode, state/region, buyer_fee_pct, seller_fee_pct, country, radius. If missing, ask minimum fields. US: search_active_cars, get_sold_summary. UK: search_uk_active_cars only (inventory profile only, no demand context). Confirm: "Using profile: [company], [state], [Country]". All preference values from profile — do not re-ask.
Auction house sales exec evaluating whether a specific dealer is worth pursuing — as a buyer, consigner, or both. Need inventory health, aging analysis, and a clear recommendation for engagement approach.
dealer_id vs source (domain) — If the user provides a dealer name, you need to find the dealer_id first. Use search_active_cars with the dealer's known domain (source=example.com) or city+state to locate listings and extract dealer_id from results. Do not guess dealer_id values.stats=price,dom will return nulls. Check num_found > 0 before computing any scores. If zero, report "No used inventory found — dealer may be new-only or recently cleared lot."facets) and market demand (from get_sold_summary with ranking_dimensions=body_type) may use different labels. Normalize both sides before computing gaps (see lane-planner Gotcha #3).dom field measures days since the listing appeared online, not how long the vehicle has physically been on the lot. A dealer who relists vehicles resets DOM to zero. Look for suspiciously low DOM on old model-year vehicles as a signal of relisting.search_uk_active_cars but cannot compute mix alignment (no get_sold_summary demand data). For UK, produce the inventory health section only and note "Engagement scoring requires US market data."Get full inventory profile — Call mcp__marketcheck__search_active_cars with dealer_id (or source for web domain), car_type=used, facets=body_type|0|20|1,make|0|30|1,year|0|10|1, stats=price,dom,miles, rows=0, price_min=1.
→ Extract only: total_count (this is num_found), facet breakdowns (body_type, make, year — name + count from each facet bucket), stats (avg_price, median_price, avg_dom, avg_miles from stats fields). If num_found=0, stop and report "No used inventory found for this dealer." Discard full response.
Get aged inventory — Call mcp__marketcheck__search_active_cars with same dealer filter, sort_by=dom, sort_order=desc, rows=10.
→ Extract only: per vehicle — vin, year, make, model, trim, price, miles, dom. Discard full response.
Get local market demand — Call mcp__marketcheck__get_sold_summary with state (dealer's state from results), inventory_type=Used, ranking_dimensions=body_type, ranking_measure=sold_count, ranking_order=desc, date_from (first of prior month), date_to (last of prior month), top_n=10.
→ Extract only: per body_type — sold_count. Discard full response.
Score and classify:
Inventory Health Score (0-100):
Mix Alignment:
Engagement Classification:
Engagement Score (0-100):
Generate recommended approach:
Dealer profile card: name, city, state, total_units, avg_price, avg_dom, health_score, engagement_type, engagement_score. Inventory mix breakdown (body_type with dealer % vs market %). Aged units list (top 10 by DOM with price and miles). Recommended approach with specific talking points. Estimated auction revenue potential (consignment fees + buyer fees from expected purchases).
-- Dealer Profile: [Dealer Name] --------------------------------------------------
Location: [City], [State]
Total Used Units: [N]
Avg Price: $[XX,XXX] | Avg DOM: [XX] days | Avg Miles: [XX,XXX]
-- Inventory Health ----------------------------------------------------------------
Health Score: [XX]/100 | Classification: [BUYER / CONSIGNER / DUAL / LOW PRIORITY]
Engagement Score: [XX]/100
-- Inventory Mix vs Market Demand --------------------------------------------------
| Body Type | Dealer Units | Dealer % | Market Sold % | Gap |
|-----------|-------------|----------|---------------|--------|
| SUV | 25 | 42% | 35% | +7% |
| Sedan | 10 | 17% | 28% | -11% |
| Pickup | 8 | 13% | 22% | -9% |
| ... | ... | ... | ... | ... |
-- Aged Inventory (Top 10 by DOM) --------------------------------------------------
| VIN (last 6) | Year | Make | Model | Trim | Price | Miles | DOM |
|--------------|------|--------|--------|--------|----------|--------|-----|
| ...A12 | 2021 | Toyota | Camry | SE | $22,500 | 45,200 | 112 |
| ... | ... | ... | ... | ... | ... | ... | ... |
-- Recommended Approach ------------------------------------------------------------
Type: [BUYER / CONSIGNER / DUAL]
Pitch: "[Specific talking points]"
Est. Revenue: $[X,XXX] (consignment fees) + $[X,XXX] (buyer fees) = $[X,XXX] total