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.

OEM Stock Tracker — Brand Health Check & Competitive Threat Monitor

Manufacturer Profile (Load First)

Load the marketcheck-profile.md project memory file if exists. Extract: brands (operational KPIs), states, competitor_brands (competitive threats), country, user_name, company. If missing, ask brand, states, and competitors. US-only; if UK, inform not available and stop. Confirm profile.

User Context

User is an OEM regional manager, brand strategist, or distributor self-monitoring brand health and tracking competitive threats. Frame your-brand metrics as OPERATIONAL KPIs; competitor metrics as COMPETITIVE THREATS.

Built-in Brand Mapping

TOYOTA MOTOR  → Toyota, Lexus
HONDA MOTOR   → Honda, Acura
GM            → Chevrolet, GMC, Buick, Cadillac
FORD MOTOR    → Ford, Lincoln
STELLANTIS    → Chrysler, Dodge, Jeep, Ram, Fiat, Alfa Romeo, Maserati
TESLA         → Tesla
HYUNDAI MOTOR → Hyundai, Kia, Genesis
NISSAN MOTOR  → Nissan, Infiniti
MERCEDES-BENZ → Mercedes-Benz
BMW GROUP     → BMW, MINI, Rolls-Royce
VW GROUP      → Volkswagen, Audi, Porsche, Lamborghini, Bentley
RIVIAN        → Rivian
LUCID         → Lucid

If the user provides a parent company name, map it to makes. If the user provides a make name (e.g., "Toyota"), use directly.

Workflow: Brand Health Check

Use this when a user asks "How is my brand doing?" or "Brand health dashboard" or "Operational KPIs."

Step 1 — Resolve the entity

Map the user's input or profile brands to the list of makes. Confirm: "Analyzing brand health for [Company]: [Make1, Make2, ...]"

Determine date ranges:

• Current month: first day of the most recent complete month → last day
• Prior month: the month before current
• Baseline (3 months ago): default 3-month lookback

Step 2 — Volume momentum (YOUR BRANDS — Operational KPI)

For EACH make in your brands, call mcp__marketcheck__get_sold_summary with:

• make: the make
• state: from profile or user input (or omit for national)
• date_from / date_to: current month
• ranking_dimensions: make
• ranking_measure: sold_count
• top_n: 1

Repeat for prior month and 3-month-ago period. -> Extract only: sold_count, average_days_on_market per make per period. Discard full response.

Sum sold_count across all your makes.

Calculate:

• MoM Volume Change % = (current - prior) / prior x 100
• 3-Month Trend % = (current - 3mo_ago) / 3mo_ago x 100
• Status: HEALTHY if MoM > +3% AND 3mo > +5%; DECLINING if MoM < -3% AND 3mo < -5%; STABLE if mixed; WATCH if MoM positive but 3mo negative (short-term bounce)

Step 3 — Pricing power (YOUR BRANDS — Operational KPI)

For each make, call mcp__marketcheck__get_sold_summary with:

• make: the make
• state: from profile or user input
• date_from / date_to: current month
• ranking_dimensions: make
• ranking_measure: average_sale_price
• top_n: 1

Repeat for prior month. -> Extract only: average_sale_price per make per period. Discard full response.

Also call for new vehicles specifically:

• inventory_type: New
• ranking_measure: price_over_msrp_percentage -> Extract only: price_over_msrp_percentage per make per period. Discard full response.

Calculate:

• Avg Sale Price Change % = MoM change in average_sale_price
• Price vs MSRP % = price_over_msrp_percentage (positive = above sticker, negative = discounting)
• MSRP Trend (bps) = (current MSRP % - prior MSRP %) x 100
• Status: STRONG if price rising AND above MSRP; ERODING if price falling AND below MSRP (deepening discounts); WATCH if price stable but MSRP shifting negative

Step 4 — Inventory health / Days Supply (YOUR BRANDS — Operational KPI)

Call mcp__marketcheck__search_active_cars with:

• make: each make
• state (via seller_state) or national
• car_type: new
• stats: price,dom
• rows: 0 -> Extract only: num_found, stats.dom.mean per make. Discard full response.

Call mcp__marketcheck__get_sold_summary for the same make/state/period to get monthly sold volume. -> Extract only: sold_count per make. Discard full response.

Calculate:

• Days Supply = (Active Inventory Count / Monthly Sold Count) x 30
• Status: TIGHT if < 45 days (strong demand); HEALTHY if 45-75 days; BUILDING if > 75 days (potential production adjustment needed); ALERT if rising rapidly (>15% MoM increase)

Step 5 — Market share (YOUR BRANDS vs COMPETITORS)

Call mcp__marketcheck__get_sold_summary with:

• state: from profile or user input
• date_from / date_to: current month
• ranking_dimensions: make
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 25

Repeat for prior month. -> Extract only: make, sold_count per brand per period, plus total_sold_count. Discard full response.

Calculate your aggregate share across your makes AND each competitor's share:

• Your Share % = sum of your makes sold / total sold x 100
• Competitor Share % = same for each competitor
• Share Change (bps) for each
• Net Share Flow = your bps change - competitor bps change (positive = you are winning)
• Status: GAINING if > +30 bps; LOSING if < -30 bps; HOLDING if within +/-30 bps

Step 6 — DOM trend (demand signal)

From the sold data in Step 2/3, extract average_days_on_market for each period.

Calculate:

• DOM MoM Change % = (current_dom - prior_dom) / prior_dom x 100
• Status: HEALTHY if DOM falling (selling faster); SOFTENING if DOM rising > 10% (demand weakening); STABLE if flat

Step 7 — EV transition (if applicable)

If your brands sell EVs:

Call mcp__marketcheck__get_sold_summary with:

• make: your makes
• fuel_type_category: EV
• Current and prior periods -> Extract only: sold_count, average_sale_price per make per period. Discard full response.

Calculate:

• EV % of your total sales = EV sold / total your brand sold x 100
• EV MoM change (bps) = trend direction
• EV avg price and MoM change
• Compare to competitor EV penetration

Step 8 — Segment mix

Call mcp__marketcheck__get_sold_summary with:

• make: each of your makes
• ranking_dimensions: body_type
• ranking_measure: sold_count
• Current period -> Extract only: body_type, sold_count, average_sale_price per segment. Discard full response.

Calculate share by segment (Pickup, SUV, Sedan, EV, etc.) and pricing trend per segment.

Output

Present: operational KPI table (volume, price, MSRP position, days supply, share, DOM with status labels), competitive threat monitor table, EV progress (if applicable), segment mix, composite health signal (HEALTHY/STABLE/DECLINING/MIXED), and actionable recommendations citing specific data.

Composite Health Logic

For the composite assessment:

• HEALTHY: 4+ of 6 operational KPIs are HEALTHY/STRONG/GAINING, no DECLINING signals
• DECLINING: 4+ are DECLINING/ERODING/LOSING, volume AND pricing both negative
• MIXED: Conflicting signals (volume up but pricing down, or vice versa)
• STABLE: Most indicators steady, no strong directional signal

Multi-Brand Comparison

If the user asks "compare my brand vs Honda" or "competitive deep dive":

1. Run the full workflow for your brand AND each competitor
2. Present a side-by-side comparison table:

   Metric              | Your Brand | Honda      | Advantage
   --------------------|------------|------------|----------
   Volume MoM          | +3.8%      | +1.2%      | You
   Pricing Power       | -0.9%      | +0.3%      | Honda
   Days Supply         | 72         | 58         | Honda
   Market Share Change  | +30 bps    | -15 bps    | You
   EV Penetration      | 4.2%       | 6.1%       | Honda
   

3. Deliver strategic insight: "You have stronger volume momentum but Honda has better inventory discipline. Their lower days supply suggests more efficient allocation — review your production planning for [segment]."

Important Notes

• This skill is US-only. All data comes from get_sold_summary and search_active_cars which require US market data.
• Date ranges should use the most recent COMPLETE month. If today is March 5, use February as "current month."
• If a make has very low volume (< 100 units/month nationally), note low sample size and reduce confidence.
• Always frame your brand metrics as operational KPIs and competitor metrics as competitive threats.
• Always cite the actual numbers, not just status labels. Brand managers need specifics to act on.