Market share and competitive intelligence. 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 share tracking, EV penetration analysis.
From marketcheck-cowork-pluginnpx claudepluginhub marketcheckhub/marketcheck-cowork-pluginThis skill uses the workspace's default tool permissions.
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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.
→ Full procedure: read _references/profile-loading.md
Parse marketcheck-profile.md if it exists → extract: state, franchise_brands, dealer_type, country. This skill works fine without a profile.
US-only: Requires get_sold_summary. If country == UK: inform "Market share analysis requires US sold transaction data and is not available for the UK market." → Full limitations: _references/country-routing.md
Confirm: "Using profile context: [state], [franchise_brands]"
Before running any workflow, collect the following (auto-filled from dealer profile where available):
location.state if user says "my market", otherwise ask. National (omit state), single state (2-letter code), or multi-state (run each separately)2026-01-01 to 2026-01-31). If the user asks for "quarterly" data, run three consecutive months and aggregate.dealer.franchise_brands if available, otherwise askIf the user asks for "market share" without specifying a geographic scope, default to national and confirm.
get_sold_summary which is not available for UK. Inform UK users immediately.get_sold_summary works on monthly date ranges. For "Q1" analysis, call January, February, and March separately, then sum volumes and recalculate shares.state param for national data. Don't pass state=US or similar.top_n limits ranking results — set appropriately per workflow. For brand share use top_n=20, for segment conquest use top_n=15, for dealer group benchmarking use top_n=20.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: first of target month (e.g. 2026-01-01)date_to: last of target month (e.g. 2026-01-31)state: user's state filter (omit for national)inventory_type: as specified (or omit for both)ranking_dimensions: makeranking_measure: sold_countranking_order: desctop_n: 20Call mcp__marketcheck__get_sold_summary for the prior period with identical filters but adjusted date_from / date_to (e.g., prior month or same month prior year).
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: user's target segment (e.g. SUV)ranking_dimensions: make,modelranking_measure: sold_countranking_order: desctop_n: 15Repeat for the comparison period to calculate share change within the segment.
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: 20Call mcp__marketcheck__get_sold_summary with same filters but:
ranking_measure: average_days_on_marketranking_order: asctop_n: 20Call mcp__marketcheck__get_sold_summary with same filters but:
ranking_measure: average_sale_priceranking_order: desctop_n: 20Merge 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: 15Call mcp__marketcheck__get_sold_summary for Hybrid sales:
fuel_type_category: HybridCall mcp__marketcheck__get_sold_summary for total market (no fuel_type_category filter):
ranking_dimensions: makeranking_measure: sold_counttop_n: 1 (we just need the total count)Repeat steps 1-3 for the prior period (prior month or same month last year) to calculate trend.
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: user's target make (required for this workflow)model: user's target model (optional — omit for brand-level view)summary_by: statelimit: 51 (all US states + DC)If the user also wants pricing context, call mcp__marketcheck__get_sold_summary with:
ranking_dimensions: make,modelranking_measure: average_sale_pricesummary_by: statelimit: 51Calculate for each state:
Present as a State-Level Demand Table sorted by sold count descending:
If the user specifies a model, also call mcp__marketcheck__get_sold_summary with ranking_dimensions: make for the same body_type (without the make/model filter) in the top 3 states to show competitive context: "In Texas, [Model] sold X units but [Competitor] sold Y units in the same segment."
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."
→ After assembling results, read references/outcomes.md to frame recommendations with quantified business impact, KPI benchmarks, and action-to-outcome guidance.