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.

Group Benchmarking — Rooftop-vs-Rooftop Performance Comparison

User Profile (Required)

Load the marketcheck-profile.md project memory file. If missing, prompt /onboarding. Requires 2+ locations. Extract: locations[] (dealer_id, name, zip, state, dealer_type, franchise_brands), preferences, country. Confirm: "Benchmarking [N] locations for [group_name]"

User Context

Dealer group executive (CEO, VP Ops, Regional Director) identifying top-performing and underperforming stores to surface best practices and flag intervention targets.

Workflow: Rooftop Benchmarking

Step 1 — Collect KPIs per location

For each location, use the Agent tool to spawn the dealership-group: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

→ Extract only: per location — total_units, avg_dom, median_dom, units by DOM bucket, avg_price. Discard full response.

From the scanner results, calculate per location:

• Total units on lot
• Avg DOM across all units
• Turn rate = 30 / avg_dom (higher = faster turns)
• Aged % = units_over_60 / total x 100
• Fresh % = units_under_30 / total x 100 (recently acquired)
• Avg listing price

Step 2 — Pricing efficiency (US only)

For each US location, use the Agent tool to spawn the dealership-group: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]

→ Extract only: per VIN — predicted_price, listed_price, gap %. Discard full response.

From results, calculate per location:

• Avg price-to-market % (are they priced competitively?)
• Overpriced % = units with gap > +5% / sampled units x 100
• Underpriced % = units with gap < -5% / sampled units x 100

Step 3 — Market context per location (US only)

For each location, call mcp__marketcheck__get_sold_summary with:

• state: location's state
• inventory_type: Used
• ranking_measure: average_days_on_market
• date_from / date_to: prior month

→ Extract only: local market average_days_on_market per state. Discard full response.

Calculate:

• DOM vs Market = location avg DOM - local market avg DOM (negative = better than market)
• Market-adjusted score = accounts for the fact that some markets are inherently slower

Step 4 — Rank locations on each KPI

Create rankings (1 = best):

1. Turn Rate Rank (highest turn rate = rank 1)
2. Aged % Rank (lowest aged % = rank 1)
3. Pricing Efficiency Rank (closest to market = rank 1)
4. DOM vs Market Rank (most below market = rank 1)
5. Composite Rank = average of all individual ranks

Step 5 — Identify patterns

• Best practices: What is the #1 ranked location doing differently? (e.g., lower avg DOM, tighter pricing, better mix)
• Underperformer root cause: For the lowest-ranked location, identify the primary driver (pricing? aging? mix?)
• Outliers: Any location with a metric > 2 standard deviations from the group mean

Output

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]

Important Notes

• This skill requires dealer_id for all locations. Locations without a dealer_id are excluded with a note.
• US vs UK: Full benchmarking (including pricing efficiency and market context) for US. UK locations get inventory metrics only (total, DOM, aging) without market pricing comparison.
• For groups with 10+ locations, consider grouping by state or region before ranking.
• The composite rank is a simple average — it weights all KPIs equally. If the user has a specific priority (e.g., "I care most about turn rate"), adjust the weighting.
• Sampling for pricing efficiency (Step 2) uses 15 units per location. For a more thorough analysis, suggest running /weekly-review on the underperforming location.