ev-transition-monitor - SKILL.md Agent Skill

name: ev-transition-monitor description: > EV investment thesis and equity research intelligence. Triggers: "EV market update", "EV adoption rate", "EV vs ICE pricing", "Tesla market position", "EV investment signal", "electric vehicle trends", "EV depreciation", "EV price parity", "hybrid adoption", "electrification progress", "EV days supply", "which OEMs are winning EV", "EV penetration by state", "EV investment thesis", "EV stock signal", "electrification transition risk", tracking electric vehicle market dynamics for investment thesis development and equity research. version: 0.1.0

Date anchor: Today's date comes from the # currentDate system 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.

get_sold_summary parameter safety:

Always set inventory_type explicitly (New or Used) — omitting it defaults to New, returning zero results for used-vehicle queries

Always set limit: 5000 — the default (1000) silently truncates when (months × states × ranking combos) exceeds 1000 rows

For volume totals, use ranking_dimensions: dealership_group_name (or the single relevant dimension) — never use the default make,model,body_type which creates ~150K rows for national 3-month queries

Use separate calls for totals vs breakdowns — don't combine in one call

EV Transition Monitor — EV Investment Thesis Intelligence

User Profile (Load First)

Load the marketcheck-profile.md project memory file if exists. Extract: tracked_tickers, tracked_states, benchmark_period_months, country. If missing, ask for EV focus area and geography. US-only. Confirm profile.

User Context

Financial analyst tracking EV pure-plays (TSLA, RIVN, LCID) or legacy OEM electrification progress for investment thesis development. Every metric framed as investment signal tied to stock tickers with BULLISH/BEARISH/NEUTRAL/CAUTION ratings.

Built-in Ticker → Makes Mapping

EV PURE-PLAY TICKERS:
TSLA  → Tesla
RIVN  → Rivian
LCID  → Lucid

LEGACY OEM TICKERS (with EV exposure):
F     → Ford, Lincoln
GM    → Chevrolet, GMC, Buick, Cadillac
TM    → Toyota, Lexus
HMC   → Honda, Acura
STLA  → Chrysler, Dodge, Jeep, Ram
HYMTF → Hyundai, Kia, Genesis
NSANY → Nissan, Infiniti
MBGAF → Mercedes-Benz
BMWYY → BMW, MINI
VWAGY → Volkswagen, Audi, Porsche

Workflow: EV Market Scorecard

The comprehensive EV market analysis. Use for "EV market update" or "EV investment signal."

Step 1 — EV penetration rate

Call mcp__marketcheck__get_sold_summary with:

fuel_type_category: EV
state: from profile or user input (omit for national)
inventory_type: New (or Used if analyzing used EV market)
date_from / date_to: current month
ranking_dimensions: fuel_type_category
ranking_measure: sold_count
top_n: 5
limit: 5000

Repeat for total market (no fuel_type_category filter, but keep inventory_type and limit: 5000). Also repeat for prior month and 3 months ago. → Extract only: sold_count per fuel_type_category per period. Discard full response.

Calculate:

EV Penetration % = EV sold / total sold × 100
Hybrid Penetration % = Hybrid sold / total sold × 100 (if available)
Combined Electrified % = (EV + Hybrid) / total × 100
MoM Change (bps) = (current % - prior %) × 100
3-Month Trend (bps) = (current % - 3mo %) × 100
Signal: ACCELERATING if MoM > +20 bps AND 3mo > +50 bps; DECELERATING if MoM < -10 bps; PLATEAU if stable ±10 bps for 2+ months

Step 2 — EV vs ICE pricing parity

Call mcp__marketcheck__get_sold_summary for each fuel type:

fuel_type_category: EV → get average_sale_price
No filter (or fuel_type_category: Gas) → get average_sale_price for ICE
inventory_type: New (or Used — always set explicitly)
limit: 5000

Repeat for prior periods (same parameters). → Extract only: average_sale_price per fuel type per period. Discard full response.

Calculate:

EV Avg Price vs ICE Avg Price
Price Gap $ = EV - ICE
Price Gap % = (EV - ICE) / ICE × 100
Gap Trend = is the gap narrowing or widening?
Signal: APPROACHING PARITY if gap < 15% and narrowing (BULLISH for EV adoption thesis); STALLED if gap stable; DIVERGING if gap widening (BEARISH for mass-market EV thesis)

Break down by segment if possible:

SUV: EV vs ICE price
Sedan: EV vs ICE price
Pickup: EV vs ICE price

Step 3 — EV depreciation vs ICE (residual risk signal)

Call mcp__marketcheck__get_sold_summary with:

fuel_type_category: EV
inventory_type: Used
ranking_dimensions: make,model
ranking_measure: average_sale_price
ranking_order: asc
top_n: 15
limit: 5000
Current month AND 3 months ago

Repeat without fuel_type filter for ICE comparison (keep inventory_type: Used, limit: 5000). → Extract only: per make/model — average_sale_price per period. Discard full response.

Calculate:

EV Monthly Depreciation % = (3mo_price - current_price) / 3mo_price / 3 × 100
ICE Monthly Depreciation % = same for ICE
Depreciation Ratio = EV rate / ICE rate (e.g., 2.1x means EVs depreciate 2.1x faster)
Signal: HIGH RISK if EV depreciation > 2x ICE — BEARISH for OEM residual exposure, lessors, and auto lender stocks; MODERATE if 1.3x-2x — CAUTION; NORMALIZING if < 1.3x — BULLISH for EV maturation thesis

Step 4 — EV days supply

Call mcp__marketcheck__search_active_cars with:

fuel_type: Electric, car_type: new, stats: price,dom, rows: 0

Plus sold data from Step 1 for volume. → Extract only: num_found, stats.dom.mean. Discard full response.

Calculate:

EV New Days Supply = active EV new / monthly EV new sold × 30
EV Used Days Supply = active EV used / monthly EV used sold × 30
Compare to ICE equivalents
Signal: DEMAND > SUPPLY if < 30 days (BULLISH for EV OEMs — pricing power intact); BALANCED if 30-60; SUPPLY BUILDING if > 60 (BEARISH — production outpacing demand); GLUT if > 90 (BEARISH — expect incentive increases and margin pressure)

Step 5 — Brand-level EV share (competitive positioning)

Call mcp__marketcheck__get_sold_summary with:

fuel_type_category: EV
inventory_type: New (or Used — always set explicitly)
ranking_dimensions: make
ranking_measure: sold_count
ranking_order: desc
top_n: 15
limit: 5000
Current month AND prior month → Extract only: per make — sold_count per period. Discard full response.

Calculate:

Brand EV Share % = brand EV sold / total EV sold × 100
MoM Share Change (bps)
Highlight TSLA, RIVN, LCID vs legacy OEMs
Map each brand to its ticker
Signal per brand: GAINING / LOSING / STABLE

Step 6 — Regional EV adoption (optional)

Call mcp__marketcheck__get_sold_summary with:

fuel_type_category: EV
inventory_type: New (or Used — always set explicitly)
summary_by: state
ranking_measure: sold_count
ranking_order: desc
top_n: 15
limit: 5000 (critical: summary_by=state multiplies rows by ~50 states) → Extract only: per state — sold_count. Discard full response.

Calculate state-level EV penetration rate by also pulling total sold by state.

Identify: highest adoption states, fastest growing states, lowest adoption states.

Output

Present: EV investment thesis headline with ticker signals, adoption/parity/depreciation/supply data tables, brand EV share with ticker mapping for competitive positioning, and investment implications by ticker (EV pure-plays, legacy OEM transition progress, auto lender/lessor exposure).

Important Notes

US-only: All data requires US get_sold_summary.
Tesla dominates EV share nationally (~50-60%) — always contextualize other OEMs' share as "ex-Tesla" if helpful.
EV depreciation patterns differ significantly by brand: Tesla tends to hold value better than Nissan Leaf or Chevy Bolt. Break down by model when possible.
The EV-to-ICE price gap is the single most important metric for adoption forecasting. Once the gap drops below 10% in a segment, adoption typically accelerates nonlinearly — this is a key inflection point for the investment thesis.
Always map makes to stock tickers. An analyst tracking EV transition is evaluating specific stock positions, not abstract brand preferences.
For every metric, explicitly state the investment signal direction and which tickers are affected.