| name | pricing-power-tracker |
| description | Discount-to-MSRP trend tracking for margin signals. Triggers: "pricing power", "discount rate", "discount trend", "MSRP vs sale price", "who's discounting", "incentive activity", "pricing erosion", "discount velocity", "which OEMs are discounting most aggressively", "pricing power over time", tracking OEM/nameplate discount-to-MSRP trends over multiple months as a leading indicator of margin pressure.
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| version | 0.1.0 |
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.
get_sold_summary parameter safety:
- Always set
inventory_type explicitly (New or Used) — omitting it defaults to New, returning zero results for used-vehicle queries
- Always set
limit: 5000 — the default (1000) silently truncates when (months × states × ranking combos) exceeds 1000 rows
- For volume totals, use
ranking_dimensions: dealership_group_name (or the single relevant dimension) — never use the default make,model,body_type which creates ~150K rows for national 3-month queries
- Use separate calls for totals vs breakdowns — don't combine in one call
Pricing Power Tracker — Discount-to-MSRP Trend Intelligence for Investment Decisions
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 missing, ask for OEM/ticker and geography. US-only. Confirm profile.
User Context
Financial analyst (equity researcher, hedge fund analyst, portfolio manager) needing to track pricing power trends over time. Discount-to-MSRP trajectory is a leading indicator of margin pressure — widening discounts signal incentive spend acceleration and earnings risk. This skill focuses exclusively on TRENDS over multiple periods, not snapshots.
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
If the user provides a ticker, map it to makes. If the user provides a make name, reverse-map to the ticker. For dealer group tickers, note that pricing power analysis is most relevant for OEM tickers.
Workflow 1: OEM Discount Rate Trend (Multi-Period)
Use when user asks "discount trend for Ford" or "pricing power over time for GM."
Step 1 — Resolve entity and periods
Map ticker to makes. Determine 5 time periods:
- Period 1 (current): most recent complete month
- Period 2: 1 month prior
- Period 3: 2 months prior
- Period 4: 3 months prior
- Period 5: 6 months prior (from
benchmark_period_months or default 6)
Step 2 — Pull discount data per period
For EACH period, call mcp__marketcheck__get_sold_summary with:
make: each make in the ticker's mappingstate: from profile or user input (or omit for national)inventory_type: Newdate_from / date_to: the period's date rangeranking_dimensions: makeranking_measure: price_over_msrp_percentagetop_n: 1limit: 5000
→ Extract only: price_over_msrp_percentage, sold_count per make per period. Discard full response.
Step 3 — Calculate trend metrics
- Discount Rate per Period: Average
price_over_msrp_percentage across the ticker's makes (weighted by sold_count)
- MoM Discount Change (bps): (current_% - prior_%) × 100
- 3-Month Discount Velocity (bps/month): (current_% - 3mo_%) / 3 × 100
- 6-Month Trajectory: Direction of change from Period 5 → Period 1
Step 4 — Signal assignment
| Signal | Threshold |
|---|
| BULLISH | Discount narrowing >20 bps/month OR above-MSRP (positive price_over_msrp_percentage) |
| NEUTRAL | Discount stable within ±20 bps/month |
| CAUTION | Discount widening 20–50 bps/month |
| BEARISH | Discount widening >50 bps/month OR >5% below MSRP |
Workflow 2: Nameplate Discount Ranking
Use when user asks "which Ford models are discounted most" or "nameplate pricing power."
Step 1 — Pull nameplate-level data
Call mcp__marketcheck__get_sold_summary with:
make: each make in the ticker's mappingstate: from profileinventory_type: Newdate_from / date_to: current monthranking_dimensions: make,modelranking_measure: price_over_msrp_percentageranking_order: asc (deepest discounts first)top_n: 15limit: 5000
→ Extract only: make, model, price_over_msrp_percentage, sold_count per model. Discard full response.
Step 2 — Identify pricing leaders and laggards
- Pricing Leaders: Models selling above MSRP (positive %) — strong demand, no incentive needed
- Pricing Laggards: Models >3% below MSRP — incentive-dependent, margin drag
- Volume Impact: Weight by sold_count to determine which models drive the ticker's aggregate discount
Workflow 3: Within-Segment Comparison
Use when user asks "who has pricing power in SUVs" or "pickup truck discount comparison."
Step 1 — Pull segment-specific data
For a given body_type (SUV, Pickup, Sedan, etc.), call mcp__marketcheck__get_sold_summary with:
body_type: the target segmentstate: from profileinventory_type: Newdate_from / date_to: current monthranking_dimensions: makeranking_measure: price_over_msrp_percentageranking_order: desctop_n: 15limit: 5000
→ Extract only: make, price_over_msrp_percentage, sold_count per make. Discard full response.
Step 2 — Rank by pricing power within segment
Map makes to tickers. Rank by price_over_msrp_percentage (highest = strongest pricing power). Flag tracked tickers. Identify segment leaders and laggards with investment signal per ticker.
Workflow 4: Discount Velocity Alert
Use when user asks "which OEMs are discounting faster" or "pricing erosion acceleration."
Step 1 — Pull two-period data for all OEMs
Call mcp__marketcheck__get_sold_summary for current month and 3 months ago with:
state: from profileinventory_type: Newdate_from / date_to: the period's date range (one call per period)ranking_dimensions: makeranking_measure: price_over_msrp_percentageranking_order: asctop_n: 25limit: 5000
→ Extract only: make, price_over_msrp_percentage, sold_count per period. Discard full response.
Step 2 — Calculate velocity and rank
- Discount Velocity = (current_% - 3mo_%) / 3 × 100 (bps per month)
- Rank all makes by velocity (most negative = fastest discounting)
- Map to tickers and aggregate
Step 3 — Signal and alert
Flag tickers with discount widening >50 bps/month (velocity ≤ -50) as BEARISH. Present ranked table with velocity, current discount rate, and signal.
Output
Present: discount trajectory chart data (5-period table with %, bps change, velocity), nameplate-level breakdown for target ticker, within-segment ranking, velocity alert table. Every metric includes BULLISH/BEARISH/NEUTRAL/CAUTION signal with rationale. Always tie to ticker and earnings implications (deeper discounting = lower gross margin per unit = earnings headwind).
Important Notes
- This skill is US-only. All data from
get_sold_summary requires US market data.
price_over_msrp_percentage is only meaningful for new vehicles (inventory_type=New). Do not apply to used vehicles.- Date ranges should use the most recent COMPLETE month.
- Low volume makes (<100 units/month) should be flagged with reduced confidence.
- Always cite actual numbers, not just signals. Analysts need to verify against their own models.
- Always map insights back to stock tickers.
- Differentiation from existing skills:
market-trends-reporter and depreciation-tracker do point-in-time MSRP snapshots. This skill tracks the TRAJECTORY over 5+ periods and calculates velocity (rate of change of discount rate).
