market-momentum-report

name	market-momentum-report
description	Sector momentum for investment decisions. Triggers: "market report", "automotive market signal", "monthly auto market", "investment briefing auto sector", "sector overview", "auto industry health", "market scorecard", "which brands are winning", "pricing power index", "market momentum", "sector momentum", "auto sector thesis", "monthly sector intelligence", comprehensive monthly overview of the US automotive market for investment decisions and sector-level portfolio allocation.
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

Market Momentum Report — Sector Momentum for Investment Decisions

User Profile (Load First)

Load the marketcheck-profile.md project memory file if exists. Extract: tracked_tickers, tracked_states, benchmark_period_months, country. If missing, produces national overview. US-only. Confirm profile.

User Context

Macro analyst, fund manager, or sector specialist needing comprehensive sector-level view of US auto market for investment decisions and portfolio allocation. Broadest skill -- covers entire US auto sector with EXPANDING/STABLE/CONTRACTING/MIXED signals tied to auto-sector equities.

Workflow: Monthly Sector Momentum

Step 1 — Macro signals (total market)

Total volume: Call mcp__marketcheck__get_sold_summary with:

• date_from / date_to: current month
• inventory_type: New (then repeat with Used — always set explicitly, never omit)
• ranking_dimensions: make
• ranking_measure: sold_count
• top_n: 5
• limit: 5000

Repeat for prior month and 3 months ago (same parameters). → Extract only: sold_count, average_sale_price, average_days_on_market per inventory_type per period. Discard full response.

Calculate:

• Total units MoM %
• Avg transaction price MoM %
• New vs Used mix shift
• Industry-wide average DOM trend

EV penetration: Call with fuel_type_category=EV, inventory_type: New (or Used), limit: 5000 for current and prior. Calculate penetration rate and bps change. → Extract only: sold_count for EV per period. Discard full response.

Step 2 — Winners and losers (by market share)

Call mcp__marketcheck__get_sold_summary with:

• ranking_dimensions: make
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 25
• inventory_type: New (then repeat with Used — always set explicitly)
• limit: 5000
• Current month AND prior month → Extract only: make, sold_count per period. Discard full response.

Calculate share % and bps change for each make. Map each make to its ticker. Identify:

• Top 5 gainers (largest positive bps change) — with ticker
• Top 5 losers (largest negative bps change) — with ticker

Step 3 — Pricing power index

Call mcp__marketcheck__get_sold_summary with:

• inventory_type: New
• ranking_dimensions: make
• ranking_measure: price_over_msrp_percentage
• ranking_order: desc
• top_n: 20
• limit: 5000
• Current month AND prior month → Extract only: make, price_over_msrp_percentage per brand. Discard full response.

Categorize:

• Above MSRP (still commanding premiums): count and avg premium %
• At MSRP (within ±1%): count
• Below MSRP (discounting): count and avg discount %

Track overall: what % of new vehicles sell above/below MSRP? Compare to prior month.

Step 4 — Depreciation alert

Call mcp__marketcheck__get_sold_summary with:

• inventory_type: Used
• ranking_dimensions: body_type
• ranking_measure: average_sale_price
• ranking_order: asc
• top_n: 10
• limit: 5000
• Current month AND 3 months ago → Extract only: body_type, average_sale_price per period. Discard full response.

Calculate monthly depreciation rate per segment. Flag segments with > 1.5%/month as accelerating.

Also identify the 5 fastest depreciating specific models (by make/model):

• inventory_type: Used
• ranking_dimensions: make,model
• ranking_measure: average_sale_price
• ranking_order: asc
• top_n: 20
• limit: 5000
• Current month AND 3 months ago → Extract only: make, model, average_sale_price per period. Discard full response.

Cross-reference with 3-month-ago data. Map depreciating models to their OEM tickers.

Step 5 — Regional price variance (optional, if state data available)

Call mcp__marketcheck__get_sold_summary with:

• inventory_type: New (or Used — always set explicitly)
• summary_by: state
• ranking_measure: average_sale_price
• ranking_order: desc
• top_n: 10
• limit: 5000 (critical: summary_by=state multiplies rows by ~50 states) → Extract only: state, average_sale_price, sold_count per state. Discard full response.

Identify:

• Most expensive states (premium markets)
• Cheapest states (value markets)
• Price spread (highest vs lowest)

Step 6 — Supply health

Call mcp__marketcheck__search_active_cars with:

• car_type: new, stats: price,dom, rows: 0

And separately with car_type=used. → Extract only: num_found, stats.dom.mean per car_type. Discard full response.

Calculate:

• Total active new inventory nationally (or by state)
• Total active used inventory
• Implied days supply for each

Output

Present: sector health composite headline (EXPANDING/STABLE/CONTRACTING/MIXED), macro signals table, winners/losers by market share with tickers, pricing power index, depreciation alert with fastest depreciating segments/models mapped to tickers, and 3 key investor signals with portfolio implications.

Composite Health Signal Logic

• EXPANDING: Volume up > 2%, pricing stable or rising, days supply < 60
• STABLE: Volume ±2%, pricing ±1%, days supply 50-75
• CONTRACTING: Volume down > 2%, pricing falling, days supply > 75
• MIXED: Conflicting signals (e.g., volume up but days supply building)

Important Notes

• US-only: Requires get_sold_summary for sold data.
• Use the most recent COMPLETE month. If today is March 5, "current month" = February.
• This is the broadest report — keep it scannable. Fund managers need the signal in 30 seconds.
• If the user has tracked tickers in their profile, star those brands in the tables.
• Always map brands to tickers throughout. An analyst reads "Toyota (TM)" not just "Toyota."
• This report works well as a regular monthly deliverable. Suggest: "Want me to run this at the start of each month?"