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 exists. Extract: tracked_tickers, tracked_states, country. If missing, ask for dealer group/ticker. US-only. Confirm profile.

User Context

Equity analyst covering automotive retail stocks (AN, LAD, PAG, SAH, GPI, ABG, KMX, CVNA). Each metric framed as investment signal with BULLISH/BEARISH/NEUTRAL/CAUTION ratings tied to stock tickers.

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: dealership_group_name, sold_count, average_sale_price, average_days_on_market per group. Discard full response.

Find the target group in results.

Step 3 — Prior month comparison

Repeat Step 2 for prior month. Calculate:

• Volume MoM % = (current - prior) / prior × 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

This gives total active used inventory count, avg price, avg DOM. Repeat with car_type=new. → Extract only: num_found, price and dom stats per car_type. Discard full response.

Calculate:

• Days Supply (used) = active used inventory / monthly used sold × 30
• Days Supply (new) = active new inventory / monthly new sold × 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: body_type/make, sold_count, average_sale_price per entry. Discard full response.

This shows the group's brand and segment mix -- critical for understanding revenue composition and OEM dependency.

Output

Present: investment thesis signal headline (BULLISH/BEARISH/MIXED/NEUTRAL) with ticker, operational KPIs table (volume, ASP, DOM, efficiency), inventory health (days supply, build/draw), peer comparison ranking, and earnings preview connecting operational data to stock implications.

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 with tickers
5. Identify which group is gaining/losing relative position
6. Deliver a relative thesis: "LAD has the strongest efficiency score, making it the best-positioned for margin expansion. AN leads in volume but DOM is rising — watch for inventory writedowns."

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 precedes 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).
• Always tie metrics back to the stock ticker and expected earnings impact. Analysts think in tickers and quarterly cadence.