Monthly portfolio risk and opportunity intelligence. Triggers: "market report", "automotive market signal", "monthly auto market", "portfolio risk briefing", "sector overview", "auto industry health", "market scorecard", "which brands are winning", "pricing power index", "market momentum", "lending risk overview", "residual risk summary", "collateral market health", comprehensive monthly overview of the US automotive market for lending risk assessment, residual forecasting, or portfolio management.
From lendernpx claudepluginhub marketcheckhub/marketcheck-cowork-plugin --plugin lenderThis 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.
Integrates PayPal payments with express checkout, subscriptions, refunds, and IPN. Includes JS SDK for frontend buttons and Python REST API for backend capture.
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: portfolio_focus, country, state, tracked_segments, risk_ltv_threshold, high_risk_ltv_threshold. If missing, produces national overview. US-only. Confirm profile.
Lender (auto finance director, residual value committee member, portfolio risk manager) needing a comprehensive sector-level view for lending policy, residual setting, and portfolio strategy. Broadest skill — covers entire US auto market through a lending risk lens.
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:
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 "RESIDUAL RISK ACCELERATING -- tighten advance rates."
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.
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.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: portfolio risk composite headline (FAVORABLE/STABLE/DETERIORATING/MIXED), macro signals table, brand performance gainers/losers, pricing power index, residual risk alert with fastest depreciating segments/models, and 3 lending policy signals (residual setting, advance rates, portfolio exposure).
get_sold_summary for sold data.