Monthly auto market overview for OEM strategy. Triggers: "market report", "sector overview", "monthly auto market", "market scorecard", "auto industry health", "market momentum", "strategic planning context", "which brands are winning", "pricing power index", "market context", comprehensive monthly overview for strategic planning, product decisions, or industry reporting.
From manufacturernpx claudepluginhub marketcheckhub/marketcheck-cowork-plugin --plugin manufacturerThis 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.
Fetches up-to-date documentation from Context7 for libraries and frameworks like React, Next.js, Prisma. Use for setup questions, API references, and code examples.
Analyzes competition with Porter's Five Forces, Blue Ocean Strategy, and positioning maps to identify differentiation opportunities and market positioning for startups and pitches.
Date anchor: Today's date comes from the
# currentDatesystem 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.
Load the marketcheck-profile.md project memory file if exists. Extract: brands (highlight with star), states, competitor_brands, country. Works without profile (national overview). US-only; if UK, inform not available. Confirm profile.
User is an OEM strategist or brand manager needing sector-level market context for strategic planning -- what is the overall market doing, and how does your brand fit within it?
Total volume: Call mcp__marketcheck__get_sold_summary with:
date_from / date_to: current monthranking_dimensions: inventory_typeranking_measure: sold_counttop_n: 5Repeat for prior month and 3 months ago.
→ Extract only: sold_count, average_sale_price, average_days_on_market per inventory_type per period. Discard full response.
Calculate:
EV penetration: Call with fuel_type_category=EV for current and prior. Calculate penetration rate and bps change.
→ Extract only: sold_count for EV per period. Discard full response.
Call mcp__marketcheck__get_sold_summary with:
ranking_dimensions: makeranking_measure: sold_countranking_order: desctop_n: 25make, sold_count per period. Discard full response.Calculate share % and bps change for each make. Identify:
Call mcp__marketcheck__get_sold_summary with:
inventory_type: Newranking_dimensions: makeranking_measure: price_over_msrp_percentageranking_order: desctop_n: 20make, price_over_msrp_percentage per brand. Discard full response.Categorize:
Highlight where YOUR brands and COMPETITOR brands fall in the pricing power spectrum.
Track overall: what % of new vehicles sell above/below MSRP? Compare to prior month.
Call mcp__marketcheck__get_sold_summary with:
inventory_type: Usedranking_dimensions: body_typeranking_measure: average_sale_priceranking_order: asctop_n: 10body_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):
ranking_dimensions: make,modelranking_measure: average_sale_priceranking_order: asctop_n: 20
→ Extract only: make, model, average_sale_price per period. Discard full response.Cross-reference with 3-month-ago data. Flag any of YOUR models or COMPETITOR models in the list.
Call mcp__marketcheck__get_sold_summary with:
summary_by: stateranking_measure: average_sale_priceranking_order: desctop_n: 10
→ Extract only: state, average_sale_price, sold_count per state. Discard full response.Focus on states in the user's profile if available.
Identify:
Call mcp__marketcheck__search_active_cars with:
car_type: new, stats: price,dom, rows: 0And separately with car_type=used.
→ Extract only: num_found, stats.dom.mean per car_type. Discard full response.
Calculate:
Present: macro signals table (volume, price, DOM, EV penetration, mix), winners/losers by share change (star your brands), pricing power index, depreciation alerts, optional regional snapshot, composite health signal (EXPANDING/STABLE/CONTRACTING/MIXED), and 3 strategic implications for your brand.
get_sold_summary for sold data.