sourcing-quality-signal

name	sourcing-quality-signal
description	Mileage and vehicle age trends as margin signals. Triggers: "average mileage", "reconditioning signal", "sourcing quality", "used vehicle age", "inventory quality", "KMX sourcing", "CVNA inventory quality", "reconditioning cost signal", "used car quality signal", "dealer group mileage benchmark", "sourcing quality for [dealer name]", tracking average mileage and vehicle age mix for any dealer group (publicly traded or private) as a proxy for reconditioning costs and margin pressure.
version	0.1.0

Sourcing Quality Signal — Mileage & Vehicle Age Trends for Used-Vehicle Retailer Analysis

User Profile (Load First)

Load the marketcheck-profile.md project memory file if exists. Extract: tracked_tickers, tracked_makes, tracked_states, benchmark_period_months, country. If country is not US, halt with: "This skill is US-only — dealer-group inventory analysis requires US search_active_cars data." If profile is missing, ask for the target ticker (or dealer-group name) and confirm US geography. Confirm profile before proceeding.

User Context

Financial analyst covering publicly traded used-vehicle retailers (KMX, CVNA) or dealer groups with significant used-vehicle operations (AN, LAD, PAG). Average mileage in sourced inventory is a direct proxy for reconditioning costs — a signal that, tracked across periodic snapshots through 2025, would have flagged Carvana's Q4 2025 earnings miss (average mileage rose ~10%, from ~48K to ~54K miles, signaling rising reconditioning costs months before the earnings report). This skill is snapshot-based; build the trend yourself by re-running it across periods.

Built-in Ticker → Makes Mapping

OEM TICKERS:
F     → Ford, Lincoln
GM    → Chevrolet, GMC, Buick, Cadillac
TM    → Toyota, Lexus
HMC   → Honda, Acura
STLA  → Chrysler, Dodge, Jeep, Ram, Fiat, Alfa Romeo, Maserati
TSLA  → Tesla
RIVN  → Rivian
LCID  → Lucid
HYMTF → Hyundai, Kia, Genesis
NSANY → Nissan, Infiniti
MBGAF → Mercedes-Benz
BMWYY → BMW, MINI, Rolls-Royce
VWAGY → Volkswagen, Audi, Porsche, Lamborghini, Bentley

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

For this skill, dealer group tickers (KMX, CVNA, AN, LAD, etc.) are the primary focus. OEM tickers are secondary — used to analyze which brands the dealer groups are sourcing.

Workflow 1: Dealer Group Mileage & Age Profile

Use when user asks "sourcing quality for CarMax" or "Carvana inventory age."

Step 1 — Pull mileage stats

For each target dealer group, call mcp__marketcheck__search_active_cars with:

• mc_dealership_group_name: the dealer group name (e.g., "CarMax", "Carvana")
• car_type: used
• country: US
• stats: miles,price,dom
• rows: 0

→ Extract only: num_found, stats.miles.mean, stats.miles.min, stats.miles.max, stats.price.mean, stats.dom.mean. Discard full response.

Step 2 — Pull vehicle age mix

Call mcp__marketcheck__search_active_cars with:

• mc_dealership_group_name: the dealer group
• car_type: used
• country: US
• facets: year|0|10|1
• rows: 0

→ Extract only: year facets with counts. Discard full response.

Step 3 — Calculate age distribution

Group model years into bands:

• 0–2 year old (current_year - 0 to 2): Premium, low recon cost
• 3–5 year old: Core sweet spot, moderate recon
• 6–8 year old: Higher recon, larger discount needed
• 9+ year old: High recon, subprime segment

Calculate % of inventory in each band.

Step 4 — Reconditioning Risk Score

Calculate: Recon Risk Score = (avg_miles / 75,000) × 100, capped at 100. The divisor is the BEARISH mileage threshold, so the score saturates exactly when the signal table classifies the group as BEARISH on mileage.

• Score 0–47: Low recon risk (avg miles in BULLISH zone, <35K)
• Score 47–73: Moderate recon risk (avg miles in NEUTRAL zone, 35–55K)
• Score 73–100: High recon risk (avg miles in CAUTION zone, 55–75K)
• Score 100 (capped): Very high recon risk (avg miles in BEARISH zone, ≥75K)

Step 5 — Signal assignment

Signal	Threshold
BULLISH	Avg miles < 35K AND >50% inventory 0–2yr old (premium sourcing, low recon)
NEUTRAL	Avg miles < 55K
CAUTION	Avg miles 55–75K (recon costs rising)
BEARISH	Avg miles > 75K OR >40% inventory 6yr+ (margin headwind from recon)

Workflow 2: Dealer Group Peer Comparison

Use when user asks "Carvana vs CarMax sourcing" or "compare dealer group inventory quality."

Step 1 — Run Workflow 1 for each dealer group

Pull mileage stats and age mix for both target groups (e.g., KMX and CVNA).

Step 2 — Side-by-side comparison

Present:

Metric              | CarMax (KMX) | Carvana (CVNA) | Advantage
--------------------|-------------|----------------|----------
Avg Mileage         | 42,300      | 54,800         | KMX
% Inventory 0-2yr   | 38%         | 22%            | KMX
% Inventory 6yr+    | 18%         | 35%            | KMX
Recon Risk Score    | 56          | 73             | KMX
Avg List Price      | $24,500     | $19,800        | -
Avg DOM             | 45          | 62             | KMX

Step 3 — Investment thesis

Translate sourcing quality differences into margin implications: higher mileage = higher reconditioning cost per unit = lower gross profit per vehicle = earnings headwind.

Workflow 3: Make-Level Sourcing Profile

Use when user asks "what brands is Carvana sourcing" or "CarMax inventory make mix."

Step 1 — Pull make distribution

Call mcp__marketcheck__search_active_cars with:

• mc_dealership_group_name: the dealer group
• car_type: used
• country: US
• facets: make|0|15|1
• stats: miles
• rows: 0

→ Extract only: make facets with counts, overall stats.miles. Discard full response.

Step 2 — Analyze make mix

Calculate: % of inventory by make, and cross-reference with which makes typically have lower/higher mileage and reconditioning costs. Premium brands (BMW, Mercedes) have higher recon costs even at same mileage. Volume brands (Toyota, Honda) have lower recon costs and better margin profiles.

Output

Present: mileage statistics table by dealer group, vehicle age distribution chart data, reconditioning risk score with signal, make-level sourcing profile, peer comparison (if applicable). Every metric includes investment signal. Connect sourcing quality to per-unit economics: avg mileage → reconditioning cost → gross profit per unit → earnings impact.

Important Notes

• This skill is US-only.
• This skill primarily uses search_active_cars (not get_sold_summary) since mileage stats require active inventory data.
• mc_dealership_group_name must match the dealer group's name in the MarketCheck database. Common names: "CarMax", "Carvana", "AutoNation", "Lithia Motors", "Penske Automotive".
• Setting mc_dealership_group_name reroutes the request through the Dealer Inventory Syndication endpoint. If a response is missing data.stats or data.facets for that reason, record num_found only and disclose the data gap in the output rather than fabricating distribution percentages.
• For KMX and CVNA, ALL inventory is used — no need to filter by car_type. For AN, LAD, PAG, filter to car_type=used to exclude their new vehicle inventory.
• Historical trending is limited by search_active_cars being a point-in-time snapshot. For trend analysis, compare current stats with get_sold_summary historical sold data for the same dealer group.
• By running this skill periodically through 2025, an analyst would have seen Carvana's avg mileage rise ~10% — a leading indicator that surfaces ~11 months before the Q4 2025 earnings miss when snapshots are compared side-by-side. This periodic-snapshot pattern is the core investment thesis for this skill.
• Always cite actual numbers. Always map to tickers.