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 for multi-location dealer groups.
From dealership-groupnpx claudepluginhub marketcheckhub/marketcheck-cowork-plugin --plugin dealership-groupThis skill uses the workspace's default tool permissions.
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Builds 3-5 year financial models for startups with cohort revenue projections, cost structures, cash flow, headcount plans, burn rate, runway, and scenario analysis.
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: group_name, locations[], preferences; from default location: state, franchise_brands, dealer_type, country. If missing, ask for fields. US-only (get_sold_summary); UK → not available. Confirm profile.
Dealer group executive, OEM analyst, or market researcher tracking brand/segment share, competitive positioning, and EV adoption from sold transaction data.
Auto-loaded from profile: state (geographic scope), franchise_brands (brand focus). Ask: time period (month range), comparison period, segment focus, inventory type (default Both). No geo specified → default national.
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]. [Group'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. Discard full response.Repeat for comparison period.
→ Extract only: per make/model — 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 with specific recommendations for each location's market.
Rank dealer groups by sales volume and operational efficiency to identify top performers and laggards. Useful for comparing the user's group against public peers.
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.
Highlight the user's own group in the leaderboard and provide comparative analysis.
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. Particularly useful for dealer groups operating in multiple states.
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. 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. Highlight states where the group has locations.
Summary with recommendations for each location's market.
Present: competitive headline with share % and bps change, ranked share tables (volume + share % + change bps), comparison period data for trend context, and strategic implications for the dealer group (allocation shifts, cross-location opportunities). Cite data period and geography.