Market share and competitive intelligence from sold data. Triggers: "market share", "who is winning in SUVs", "competitor analysis", "EV adoption rate", "dealer group ranking", "segment share breakdown", "brand performance comparison", "conquest analysis", "regional demand heatmap", "quarterly share change", "which brands are gaining share", "top dealer groups by volume", competitive intelligence, OEM benchmarking, segment-level market share tracking, EV penetration analysis.
From dealernpx claudepluginhub marketcheckhub/marketcheck-cowork-plugin --plugin dealerThis 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.
Calculates TAM/SAM/SOM using top-down, bottom-up, and value theory methodologies for market sizing, revenue estimation, and startup validation.
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.
Convert MarketCheck sold transaction data into real-time market share analytics. Track brand and model-level share, segment conquest patterns, dealer group performance, EV adoption curves, and regional demand distribution — all without waiting 60-90 days for traditional syndicated reports.
Load the marketcheck-profile.md project memory file if exists. Extract: state, franchise_brands, dealer_type, country. If missing, ask. US-only skill (get_sold_summary). If UK, inform: "Market share analysis requires US sold data and is not available for UK." Confirm: "Using profile context: [state], [franchise_brands]"
Competitive intelligence from sold data — brand share, segment conquest, dealer group benchmarking, EV adoption, regional demand.
| Field | Source | Notes |
|---|---|---|
| Geographic scope | Profile state or ask | National default if unspecified |
| Time period, comparison period | Ask | Month format: YYYY-MM-01 to YYYY-MM-DD; quarterly = 3 months aggregated |
| Brand focus, segment focus | Profile or ask | Optional |
| Inventory type | Ask | New/Used/Both (default Both) |
Calculate market share by make for a given period and compare against a prior period to identify gainers and losers.
Call mcp__marketcheck__get_sold_summary for the current period:
date_from / date_to: target month first-to-last daystate: user's state filter (omit for national)inventory_type: as specified (or omit for both)ranking_dimensions: makeranking_measure: sold_countranking_order: desctop_n: 20
→ Extract only: per make — sold_count, total sold_count. Discard full response.Repeat for the prior period with identical filters but adjusted dates.
→ Extract only: per make — sold_count, total sold_count. Discard full response.
Calculate for each make:
Present as a ranked table:
Add a summary paragraph: "The top 3 share gainers this period were [X], [Y], [Z], collectively picking up [N] basis points. The biggest losers were [A], [B], [C]. [User's brand] moved from #X to #Y position with a [+/-N] bps shift."
Determine which brands are winning within specific vehicle segments (body types) and identify conquest opportunities.
Call mcp__marketcheck__get_sold_summary with:
date_from / date_to: target periodstate: user's state filter (omit for national)body_type: target segment (e.g. SUV)ranking_dimensions: make,modelranking_measure: sold_countranking_order: desctop_n: 15
→ Extract only: per make/model — sold_count; plus total segment sold_count. Discard full response.Repeat for comparison period.
→ Extract only: per make/model — sold_count; plus total segment sold_count. Discard full response.
If the user wants multi-segment comparison, repeat step 1 for each body_type: SUV, Sedan, Pickup, Hatchback, Coupe, Van/Minivan.
For each segment, calculate:
Present per-segment tables:
Conquest insight: "In the SUV segment, [Brand A] gained 120 bps primarily through [Model X] (+3,200 units). [User's brand] lost share to [Brand A] and [Brand B]. To recapture, focus on [Model Y] which competes directly and currently has lower DOM."
Rank dealer groups by sales volume and operational efficiency to identify top performers and laggards.
Call mcp__marketcheck__get_sold_summary with:
date_from / date_to: target periodstate: user's state filter (omit for national)ranking_dimensions: dealership_group_nameranking_measure: sold_countranking_order: desctop_n: 20
→ Extract only: per group — sold_count. Discard full response.Same filters but ranking_measure: average_days_on_market, ranking_order: asc.
→ Extract only: per group — average_days_on_market. Discard full response.
Same filters but ranking_measure: average_sale_price, ranking_order: desc.
→ Extract only: per group — average_sale_price. Discard full response.
Merge the three result sets by dealership_group_name. Build a Dealer Group Leaderboard:
If the user specifies a make, add a make filter to all calls to see dealer group performance within a single brand's network.
Provide analysis: "AutoNation leads in volume with X units (Y% share) but Lithia has the lowest average DOM at Z days, suggesting faster inventory turns. For [Brand] specifically, the top 3 performing groups are..."
Monitor electric and hybrid vehicle penetration rates over time against the total market.
Call mcp__marketcheck__get_sold_summary for EV sales:
date_from / date_to: target periodstate: user's state filter (omit for national)fuel_type_category: EVranking_dimensions: make,modelranking_measure: sold_countranking_order: desctop_n: 15
→ Extract only: per make/model — sold_count; plus total EV sold_count. Discard full response.Same filters but fuel_type_category: Hybrid.
→ Extract only: per make/model — sold_count; plus total Hybrid sold_count. Discard full response.
Call for total market (no fuel_type_category): ranking_dimensions: make, ranking_measure: sold_count, top_n: 1.
→ Extract only: total sold_count. Discard full response.
Repeat steps 1-3 for the prior period to calculate trend. → Extract only: same fields per period. Discard full response.
Calculate:
Present:
Map sales volume and pricing by state for a specific make or model to reveal geographic demand patterns.
Call mcp__marketcheck__get_sold_summary with:
date_from / date_to: target periodmake: target make (required), model: optionalsummary_by: state, limit: 51
→ Extract only: per state — sold_count, average_sale_price, average_days_on_market. Discard full response.If pricing context needed, add ranking_dimensions: make,model, ranking_measure: average_sale_price, summary_by: state, limit: 51.
→ Extract only: per state — average_sale_price. Discard full response.
Calculate for each state:
Present as a State-Level Demand Table sorted by sold count descending:
If model specified, also pull ranking_dimensions: make for same body_type (no make/model filter) in top 3 states for competitive context.
Summary: "For [Make Model], Texas leads with X% of national volume at an average price $Y [above/below] the national average. The least penetrated large markets are [State A], [State B], [State C] — representing potential growth opportunities."
Present: competitive headline, ranked share tables (volume + share % + bps change), always include comparison period data, share change in basis points. For EV/Hybrid: penetration rate alongside volume. End with strategic implications tailored to dealer context. Cite data period and geography in every output.