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.

Dealer Group Health Monitor — Investment Signals for Publicly Traded Dealer Stocks

User Profile (Load First)

Load the marketcheck-profile.md project memory file. If missing, ask for dealer group or ticker. Extract: group_name, is_publicly_traded, ticker, country. US-only (UK → stop). Confirm profile.

User Context

Dealer group executive or equity analyst benchmarking publicly traded dealer groups (AN, LAD, PAG, SAH, GPI, ABG, KMX, CVNA) on operational health and investment signals.

Built-in Ticker → Dealer Group Mapping

AN    → AutoNation
LAD   → Lithia Motors
PAG   → Penske Automotive Group
SAH   → Sonic Automotive
GPI   → Group 1 Automotive
ABG   → Asbury Automotive Group
KMX   → CarMax
CVNA  → Carvana

Workflow: Single Dealer Group Health Check

Use when a user asks "How is AutoNation doing?" or "LAD health check."

Step 1 — Resolve the entity

Map ticker or name to the dealer group. Confirm: "Analyzing [Ticker] ([Group Name])"

Step 2 — Volume and efficiency (current month)

Call mcp__marketcheck__get_sold_summary with:

• ranking_dimensions: dealership_group_name
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 20
• date_from / date_to: current month

→ Extract only: target group's sold_count, average_sale_price, average_days_on_market. Discard full response.

Step 3 — Prior month comparison

Repeat Step 2 for prior month. → Extract only: same fields as Step 2 for prior month. Discard full response. Calculate:

• Volume MoM % = (current - prior) / prior x 100
• ASP MoM % = price change
• DOM MoM change = days change
• Efficiency Score = sold_count / average_days_on_market (higher = better capital efficiency)

Step 4 — Active inventory health

Call mcp__marketcheck__search_active_cars with:

• dealer_group: the group name
• car_type: used
• stats: price,dom
• rows: 0

→ Extract only: total count, avg price (from stats), avg DOM (from stats). Discard full response. Repeat with car_type=new.

Calculate:

• Days Supply (used) = active used inventory / monthly used sold x 30
• Days Supply (new) = active new inventory / monthly new sold x 30
• Inventory Build/Draw: Compare current active count to prior month's — is inventory building or drawing down?

Step 5 — Peer comparison

From the Step 2 results (which already include top 20 dealer groups), extract the top 8 publicly traded groups. Build a peer table with: volume, ASP, DOM, efficiency score.

Rank the target group against peers on each metric.

Step 6 — Segment mix (optional, for deeper analysis)

Call mcp__marketcheck__get_sold_summary with:

• dealership_group_name: the group
• ranking_dimensions: body_type
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 10

And separately:

• ranking_dimensions: make
• ranking_measure: sold_count
• top_n: 15

→ Extract only: per body_type/make — sold_count, average_sale_price, average_days_on_market. Discard full response.

Output

DEALER GROUP HEALTH — [Group Name] ([Ticker])
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Period: [Current Month] vs [Prior Month]

OPERATIONAL KPIs
Metric                  | Current    | Prior Mo   | MoM Change | Signal
------------------------|------------|------------|------------|--------
Volume (units sold)     | XX,XXX     | XX,XXX     | +X.X%      | BULLISH/BEARISH
Avg Sale Price          | $XX,XXX    | $XX,XXX    | +X.X%      | signal
Avg Days on Market      | XX days    | XX days    | +X days    | signal
Efficiency Score        | XXX        | XXX        | +X.X%      | signal
(vol / DOM)             |            |            |            |

INVENTORY HEALTH
                    | Active Count | Days Supply | Trend       | Signal
--------------------|-------------|-------------|-------------|--------
Used Inventory      | XX,XXX      | XX days     | Building/Drawing | signal
New Inventory       | XX,XXX      | XX days     | Building/Drawing | signal

PEER COMPARISON (Top 8 Public Dealer Groups)
Rank | Group            | Ticker | Volume  | ASP     | DOM  | Efficiency | Signal
-----|------------------|--------|---------|---------|------|------------|--------
  1  | [Group]          | XX     | XX,XXX  | $XX,XXX | XX   | XXX        | —
  2  | [Group]          | XX     | XX,XXX  | $XX,XXX | XX   | XXX        | —
  ...
★ = [Target Group]

[If segment data available:]
TOP SEGMENTS (by volume)
Segment   | Volume  | % of Total | ASP       | DOM
----------|---------|------------|-----------|------
SUV       | XX,XXX  | XX%        | $XX,XXX   | XX
Pickup    | XX,XXX  | XX%        | $XX,XXX   | XX
Sedan     | XX,XXX  | XX%        | $XX,XXX   | XX

TOP BRANDS SOLD
Make      | Volume  | % of Total | ASP       | DOM
----------|---------|------------|-----------|------
Toyota    | XX,XXX  | XX%        | $XX,XXX   | XX
Ford      | XX,XXX  | XX%        | $XX,XXX   | XX

INVESTMENT THESIS SIGNAL: [BULLISH / BEARISH / MIXED / NEUTRAL]

Positive:
- [e.g., "Volume up 4.2% MoM outpacing industry growth of 1.8%"]
- [e.g., "DOM improvement of 3 days signals better inventory management"]

Negative:
- [e.g., "Days supply building to 52 — may require price reductions"]

Watchpoints:
- [e.g., "Used car ASP declining while volume rises — margin compression risk"]

Signal Logic

Metric	BULLISH	NEUTRAL	CAUTION	BEARISH
Volume MoM	> +3%	-1% to +3%	-3% to -1%	< -3%
ASP MoM	> +1%	-1% to +1%	-3% to -1%	< -3%
DOM Change	< -2 days	-2 to +2	+2 to +5	> +5 days
Days Supply (used)	< 35	35-55	55-75	> 75
Days Supply (new)	< 50	50-80	80-100	> 100
Efficiency MoM	> +5%	-2% to +5%	-5% to -2%	< -5%

Workflow: Peer Group Comparison

Use when the user asks "compare AutoNation vs Lithia" or "rank the top dealer groups."

1. Pull all 8 publicly traded groups from get_sold_summary rankings
2. Rank on: Volume, ASP, DOM, Efficiency Score
3. Calculate a composite rank (average of individual ranks)
4. Present side-by-side comparison table
5. Identify which group is gaining/losing relative position

Important Notes

• US-only: All data from get_sold_summary requires US market.
• The dealership_group_name field in MarketCheck may not exactly match the stock ticker name — use fuzzy matching if needed.
• Volume from MarketCheck represents listings activity, not necessarily closed transactions. Use as a proxy for retail velocity.
• DOM is a leading indicator of margin pressure — rising DOM precedes price cuts which precede margin compression in quarterly earnings.
• Efficiency Score (volume / DOM) is the single best proxy for operational health — it captures both demand (volume) and execution (speed).