Quintile scorecard for public dealer groups. Triggers: "dealer group scorecard", "quintile scorecard", "public dealer group ranking", "cohort benchmarking", "dealer group composite score", "quintile ranking", "scorecard report", "industry benchmarking for dealer groups", "how do dealer groups rank against the industry", scoring publicly traded dealer groups against the full 400+ dealer group industry cohort using the MarketCheck quintile methodology.
From analystnpx claudepluginhub marketcheckhub/marketcheck-cowork-plugin --plugin analystThis 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.
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 = 2026-03-19, then "most recent complete month" = February 2026 (2026-02-01 to 2026-02-28), "same month last year" = February 2025 (2025-02-01 to 2025-02-28), "Q1 current year" = 2026-01-01 to 2026-03-31, "Q4 prior year" = 2025-10-01 to 2025-12-31. Never use training-data dates.
Score all 8 publicly traded US dealer groups on 6 operational KPIs against the full ~400 dealer group industry cohort. Each KPI is assigned a quintile (Q1-Q5) based on the group's position within the cohort distribution, then combined into a weighted composite score (1.0-5.0).
Architecture: This skill spawns the cohort-benchmarking-agent to fetch cohort data and compute quintile thresholds, then optionally fetches stock returns via WebFetch. The skill assembles the final scorecard with narrative summaries.
Load the marketcheck-profile.md project memory file if exists. Extract: tracked_tickers, country. If missing, proceed with defaults (all 8 public groups, US). US-only skill.
Tickers: SAH, CVNA, PAG, KMX, GPI, LAD, AN, ABG
Groups: Sonic Automotive, Carvana, Penske Automotive Group, CarMax,
Group 1 Automotive, Lithia Motors, AutoNation, Asbury Automotive Group
From # currentDate, compute:
Confirm: "Generating Public Dealer Group Quintile Scorecard | Period: [current month] | YoY baseline: [prior year month] | DOM trend: Q1 [year] → Q4 [year]"
Use the Agent tool to spawn the marketcheck-cowork-plugin:cohort-benchmarking-agent with this prompt:
Benchmark these target groups against the full industry cohort: Target groups: AutoNation, Lithia Motors, Penske Automotive Group, Sonic Automotive, Group 1 Automotive, Asbury Automotive Group, CarMax, Carvana
Date ranges:
- current_month_from: [date] | current_month_to: [date]
- prior_year_month_from: [date] | prior_year_month_to: [date]
- q1_from: [date] | q1_to: [date]
- q4_from: [date] | q4_to: [date]
Return: quintile thresholds, per-group KPI values, quintile assignments, and composite scores for all 8 groups.
For each ticker (SAH, CVNA, PAG, KMX, GPI, LAD, AN, ABG), attempt to fetch 12-month stock price returns using WebFetch. Try fetching from a financial data source.
If WebFetch is unavailable or fails, skip this column entirely and note: "Stock returns not available in this session." The scorecard is still valid without returns — they provide context but do not affect the composite score.
Before assembling the final output:
Present the scorecard in this format:
MarketCheck Investment Funds Practice
PUBLIC DEALER GROUP QUINTILE SCORECARD
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
KPI performance vs. ~[N] dealer group cohort
Trailing analysis: [period description]
Q5 Top 20% of cohort | Q4 60-80th pct | Q3 40-60th pct | Q2 20-40th pct | Q1 Bottom 20%
QUINTILE THRESHOLDS (from [N]-group cohort)
KPI | P20 | Median | P80 | Direction
─────────────────|──────────|──────────|──────────|──────────
Inv. Turns | [val] | [val] | [val] | Higher ↑
YoY Growth | [val]% | [val]% | [val]% | Higher ↑
New Pr/MSRP | [val]% | [val]% | [val]% | Higher ↑
L-to-S Spread | [val]% | [val]% | [val]% | Lower ↓
Avg DOM | [val]d | [val]d | [val]d | Lower ↓
DOM Trend | [val]d | [val]d | [val]d | Lower ↓
SCORECARD
Dealer | Turns | YoY | Pr/MSRP | L→S | DOM | Trend | Score | Rank | 12-mo Return
─────────────────|───────|──────|─────────|──────|──────|───────|──────────|──────|─────────────
[ticker/name] | Q[n] | Q[n] | Q[n] | Q[n] | Q[n] | Q[n] | [x.xx]/5 | #[n] | [+/-xx.x%]
[sorted by composite score descending]
For each of the 8 dealer groups, write a 3-5 sentence narrative summary following this structure:
[TICKER] — [Full Name] MC: [composite]/5 12-mo: [return or "N/A"]
The narrative should cover:
Sort narratives by composite score (highest first).
Append at the end:
METHODOLOGY
- Composite = (Turns × 0.20) + (YoY × 0.20) + (NewPr × 0.15) + (LtoS × 0.15) + (DOM × 0.20) + (Trend × 0.10)
- Quintile thresholds derived from [N] active dealer groups in MarketCheck data
- CVNA and KMX assigned Q3 on New Price/MSRP (business model exclusion for pure used-only retailers)
- Inventory Turns approximated as monthly sold count / active inventory count
- Listing-to-Sale Spread = (avg active listing price - avg sold price) / avg listing price
- DOM Trend = Q4 avg DOM - Q1 avg DOM (negative = improving velocity)
- Stock returns are price returns sourced via web lookup [or "not available"]
| KPI | Weight | Direction |
|---|---|---|
| Inventory Turns | 20% | Higher = better |
| YoY Unit Growth | 20% | Higher = better |
| New Price/MSRP | 15% | Higher = better |
| L-to-S Spread | 15% | Lower = better |
| Avg DOM | 20% | Lower = better |
| DOM Trend | 10% | Lower (more negative) = better |
get_sold_summary requires US market.