Rooftop-vs-rooftop performance comparison. Triggers: "compare my stores", "best performing location", "benchmark rooftops", "rank my locations", "which store is best", "location comparison", "store performance ranking", "rooftop efficiency", compare operational metrics across locations, identify best practices and underperformers.
From marketcheck-cowork-pluginnpx claudepluginhub marketcheckhub/marketcheck-cowork-pluginThis 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.
This skill requires a dealer group profile with at least 2 locations.
marketcheck-profile.md project memory file.user_type:
dealer_group: use dealer_group.locations[] — requires minimum 2 locationsdealer or other: "This skill is for multi-location dealer groups. Run /onboarding as a Dealer Group."locations[] with dealer_id, name, zip, state, dealer_type, franchise_brands for eachpreferences for group defaultscountry ← location.countryThe primary user is a dealer group executive (CEO, VP Operations, Regional Director) who needs to identify which stores are outperforming and which need intervention. The goal is to surface best practices from top performers and flag underperformers with specific metrics for improvement.
For each location, use the Agent tool to spawn the marketcheck-cowork-plugin:lot-scanner agent in facets mode:
Fetch inventory stats for dealer_id=[dealer_id], country=[country]. Mode: full (with DOM stats) Return: total_units, avg_dom, median_dom, units_under_30_dom, units_30_60_dom, units_over_60_dom, avg_price
From the scanner results, calculate per location:
For each US location, use the Agent tool to spawn the marketcheck-cowork-plugin:lot-pricer agent on a SAMPLE of units (e.g., the 10 oldest and 5 newest per location):
Price these vehicles: [sample VINs with miles and listed_price] zip: [location zip], dealer_type: [location dealer_type]
From results, calculate per location:
For each location, call mcp__marketcheck__get_sold_summary with:
state: location's stateinventory_type: Usedranking_measure: average_days_on_marketdate_from / date_to: prior monthThis gives the LOCAL market average DOM — which provides context for whether a location's DOM is good or bad RELATIVE to its market.
Calculate:
Create rankings (1 = best):
GROUP BENCHMARKING — [Group Name]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
[N] locations compared | Period: [Month Year]
PERFORMANCE RANKINGS (1 = best)
Location | Units | Avg DOM | Turn Rate | Aged % | Price Eff | DOM vs Mkt | Composite
-------------------|-------|---------|-----------|--------|-----------|------------|----------
★ [Location 1] | XXX | XX | X.XX | X% | +X.X% | -X days | 1
[Location 2] | XXX | XX | X.XX | XX% | +X.X% | +X days | 2
[Location 3] | XXX | XX | X.XX | XX% | +X.X% | +X days | 3
...
⚠ [Location N] | XXX | XX | X.XX | XX% | +X.X% | +X days | N
★ = Top performer | ⚠ = Needs attention
GROUP AVERAGES
Avg DOM: XX days | Turn Rate: X.XX | Aged %: XX% | Price-to-Market: +X.X%
KPI DEEP DIVE
Turn Rate:
Best: [Location] at X.XX (XX units sold per 30 days equivalent)
Worst: [Location] at X.XX
Gap: X.XX (X.Xx difference — [worst] is turning XXx slower)
Aging:
Best: [Location] at X% aged (only X units over 60 days)
Worst: [Location] at XX% aged (XX units — $X,XXX/day in floor plan)
Gap: XX percentage points
Pricing Efficiency:
Best: [Location] at +X.X% vs market (tight, competitive pricing)
Worst: [Location] at +XX.X% (significantly overpriced, contributing to aging)
BEST PRACTICES (from top performer)
- [e.g., "[Location 1] prices within 3% of market on 90% of units — aggressive day-1 pricing prevents aging"]
- [e.g., "[Location 1] has only 4% aged inventory — suggests strong reconditioning-to-frontline speed"]
IMPROVEMENT OPPORTUNITIES (for bottom performers)
- [e.g., "[Location N] has 25% aged inventory costing $X,XXX/day. Immediate action: reduce prices on XX aged units by avg $X,XXX to reach market level"]
- [e.g., "[Location N-1] is priced XX% above market on average. Aligning to market could reduce avg DOM by XX days"]
RECOMMENDED ACTIONS
1. [Most impactful action with specific location, metric, and dollar impact]
2. [Second action]
3. [Third action]
dealer_id for all locations. Locations without a dealer_id are excluded with a note./weekly-review on the underperforming location.