OEM investment leading indicators. Triggers: "OEM stock signal", "how is Ford doing", "Toyota demand trends", "brand health check", "investment signal for [OEM]", "pricing power analysis", "days supply", "OEM market share trends", "brand volume momentum", "inventory build", leading indicators for publicly traded automotive OEMs, investment decisions.
From marketcheck-cowork-pluginnpx claudepluginhub marketcheckhub/marketcheck-cowork-pluginThis skill uses the workspace's default tool permissions.
Provides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
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Builds 3-5 year financial models for startups with cohort revenue projections, cost structures, cash flow, headcount plans, burn rate, runway, and scenario analysis.
Before running any workflow, check for a saved user profile:
marketcheck-profile.md project memory file.user_type — if analyst, use analyst.tracked_tickers, analyst.tracked_states, analyst.benchmark_period_monthscountry ← location.country (this skill is US-only — requires get_sold_summary)state ← location.state or analyst.tracked_statescountry=UK, inform the user: "OEM investment signals require US sold transaction data. This skill is not available for UK market." Stop.The primary user is a financial analyst (equity researcher, hedge fund analyst, or portfolio manager) who needs leading indicators to inform investment decisions on publicly traded automotive entities. The secondary user is an OEM regional manager or dealer group strategist monitoring brand health.
This skill produces actionable investment signals — not just data. Each metric includes an explicit BULLISH / BEARISH / NEUTRAL / CAUTION signal with a brief rationale.
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
If the user provides a ticker, map it to makes using this table. If the user provides a make name (e.g., "Ford"), reverse-map to the ticker. For dealer group tickers, redirect to the dealer-group-health-monitor skill.
Use this when a user asks "How is Ford doing?" or "Investment signal for GM" or "Toyota demand trends."
Map the user's input (ticker or brand name) to the list of makes using the built-in mapping. Confirm: "Analyzing [Ticker] ([Company Name]): [Make1, Make2, ...]"
Determine date ranges:
analyst.benchmark_period_months or default 3For EACH make in the ticker's mapping, call mcp__marketcheck__get_sold_summary with:
make: the makestate: from profile or user input (or omit for national)date_from / date_to: current monthranking_dimensions: makeranking_measure: sold_counttop_n: 1Repeat for prior month and 3-month-ago period.
Sum sold_count across all makes for the ticker.
Calculate:
For each make, call mcp__marketcheck__get_sold_summary with:
make: the makestate: from profile or user inputdate_from / date_to: current monthranking_dimensions: makeranking_measure: average_sale_pricetop_n: 1Repeat for prior month.
Also call for new vehicles specifically to get MSRP positioning:
inventory_type: Newranking_measure: price_over_msrp_percentageCalculate:
Call mcp__marketcheck__search_active_cars with:
make: each makestate (via seller_state) or nationalcar_type: newstats: price,domrows: 0This gives total active NEW inventory count and average DOM.
Call mcp__marketcheck__get_sold_summary for the same make/state/period to get monthly sold volume.
Calculate:
Call mcp__marketcheck__get_sold_summary with:
state: from profile or user inputdate_from / date_to: current monthranking_dimensions: makeranking_measure: sold_countranking_order: desctop_n: 25Repeat for prior month.
Calculate the OEM's aggregate share across its makes:
From the sold data in Step 2/3, extract average_days_on_market for each period.
Calculate:
If the OEM sells EVs (Tesla, Rivian, Lucid, or legacy OEMs with EV models):
Call mcp__marketcheck__get_sold_summary with:
make: the OEM's makesfuel_type_category: EVCalculate:
For EV pure-plays (TSLA, RIVN, LCID), this IS the entire analysis. For legacy OEMs, it shows transition progress.
Call mcp__marketcheck__get_sold_summary with:
make: each of the OEM's makesranking_dimensions: body_typeranking_measure: sold_countCalculate share by segment (Pickup, SUV, Sedan, EV, etc.) and pricing trend per segment.
OEM INVESTMENT SIGNAL — [Company Name] ([Ticker])
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Market: [State or National] | Period: [Current Month] vs [Prior Month] vs [3mo Ago]
Makes: [Make1, Make2, ...]
LEADING INDICATORS
Metric | Current | Prior Mo | 3mo Ago | Trend | Signal
----------------------|------------|------------|------------|-------------|--------
Volume (sold units) | XXX,XXX | XXX,XXX | XXX,XXX | +X.X% MoM | BULLISH
Avg Sale Price | $XX,XXX | $XX,XXX | $XX,XXX | +X.X% MoM | NEUTRAL
Price vs MSRP (new) | +X.X% | +X.X% | +X.X% | ↓ XXX bps | BEARISH
Days Supply (new) | XX days | XX days | XX days | +X.X% | CAUTION
Market Share | XX.X% | XX.X% | XX.X% | +XX bps | BULLISH
Avg DOM | XX days | XX days | XX days | +X.X% | NEUTRAL
[If OEM has EV sales:]
EV TRANSITION
EV % of Sales | X.X% | X.X% | X.X% | +XX bps | Growing/Stalled
EV Avg Price | $XX,XXX | $XX,XXX | $XX,XXX | -X.X% | Compressing/Stable
EV Days Supply | XX days | XX days | | |
SEGMENT MIX (by volume)
Segment | Share | MoM Trend | Pricing Trend | Signal
----------|---------|-----------|---------------|--------
Pickup | XX% | stable | -X.X% | NEUTRAL
SUV | XX% | +X% | -X.X% | CAUTION
Sedan | XX% | -X% | flat | NEUTRAL
EV | X.X% | +XX% | -X.X% | BULLISH
COMPOSITE INVESTMENT THESIS: [BULLISH / BEARISH / MIXED / NEUTRAL]
Positive factors:
- [specific data-backed positive signal, e.g., "Volume growth of +3.8% MoM driven by SUV segment"]
- [second positive]
Negative factors:
- [specific data-backed negative signal, e.g., "MSRP position deteriorated 290 bps — deepening discounts signal weakening demand"]
- [second negative]
Key watchpoints:
- [forward-looking signal, e.g., "If days supply exceeds 80 next month, expect production cut announcement"]
- [second watchpoint]
For the composite thesis:
Always provide the specific data that drives each signal — analysts need to verify the reasoning, not just the conclusion.
If the user asks "compare Ford vs GM" or "which OEM is winning":
Metric | Ford (F) | GM (GM) | Advantage
--------------------|----------|---------|----------
Volume MoM | +3.8% | +1.2% | Ford
Pricing Power | -0.9% | +0.3% | GM
Days Supply | 72 | 58 | GM
Market Share Change | +30 bps | -15 bps | Ford
EV Penetration | 4.2% | 6.1% | GM
get_sold_summary and search_active_cars which require US market data.