Expert guidance for automotive systems, connected vehicles, fleet management, telematics, advanced driver assistance systems (ADAS), and automotive software development.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionautomotive-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches automotive-expert from personamanagmentlayer/pcl and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate automotive-expert. Access via /automotive-expert in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Expert guidance for automotive systems, connected vehicles, fleet management, telematics, advanced driver assistance systems (ADAS), and automotive software development.
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import List, Optional
from decimal import Decimal
from enum import Enum
import numpy as np
class VehicleStatus(Enum):
ACTIVE = "active"
IDLE = "idle"
MAINTENANCE = "maintenance"
OUT_OF_SERVICE = "out_of_service"
class FuelType(Enum):
GASOLINE = "gasoline"
DIESEL = "diesel"
ELECTRIC = "electric"
HYBRID = "hybrid"
CNG = "cng"
@dataclass
class Vehicle:
"""Fleet vehicle information"""
vehicle_id: str
vin: str # Vehicle Identification Number
make: str
model: str
year: int
license_plate: str
fuel_type: FuelType
status: VehicleStatus
odometer_km: int
last_service_km: int
next_service_km: int
assigned_driver_id: Optional[str]
location: tuple # (latitude, longitude)
fuel_level_percent: float
@dataclass
class Trip:
"""Vehicle trip record"""
trip_id: str
vehicle_id: str
driver_id: str
start_time: datetime
end_time: Optional[datetime]
start_location: tuple
end_location: Optional[tuple]
distance_km: float
fuel_consumed_liters: float
average_speed_kmh: float
max_speed_kmh: float
harsh_braking_count: int
harsh_acceleration_count: int
class FleetManagementSystem:
"""Fleet management and telematics system"""
def __init__(self):
self.vehicles = {}
self.trips = []
self.maintenance_schedules = []
def track_vehicle_location(self, vehicle_id: str) -> dict:
"""Track real-time vehicle location"""
vehicle = self.vehicles.get(vehicle_id)
if not vehicle:
return {'error': 'Vehicle not found'}
# Get GPS data from telematics device
location = self._get_gps_location(vehicle_id)
speed = self._get_current_speed(vehicle_id)
heading = self._get_heading(vehicle_id)
vehicle.location = location
return {
'vehicle_id': vehicle_id,
'location': {
'latitude': location[0],
'longitude': location[1]
},
'speed_kmh': speed,
'heading': heading,
'timestamp': datetime.now().isoformat(),
'status': vehicle.status.value
}
def start_trip(self, vehicle_id: str, driver_id: str) -> Trip:
"""Start a new trip"""
vehicle = self.vehicles.get(vehicle_id)
if not vehicle:
raise ValueError("Vehicle not found")
trip = Trip(
trip_id=self._generate_trip_id(),
vehicle_id=vehicle_id,
driver_id=driver_id,
start_time=datetime.now(),
end_time=None,
start_location=vehicle.location,
end_location=None,
distance_km=0.0,
fuel_consumed_liters=0.0,
average_speed_kmh=0.0,
max_speed_kmh=0.0,
harsh_braking_count=0,
harsh_acceleration_count=0
)
vehicle.status = VehicleStatus.ACTIVE
self.trips.append(trip)
return trip
def end_trip(self, trip_id: str) -> dict:
"""End trip and calculate metrics"""
trip = next((t for t in self.trips if t.trip_id == trip_id), None)
if not trip:
return {'error': 'Trip not found'}
vehicle = self.vehicles.get(trip.vehicle_id)
trip.end_time = datetime.now()
trip.end_location = vehicle.location
# Calculate trip metrics
duration_hours = (trip.end_time - trip.start_time).total_seconds() / 3600
trip.average_speed_kmh = trip.distance_km / duration_hours if duration_hours > 0 else 0
# Calculate fuel efficiency
fuel_efficiency = trip.distance_km / trip.fuel_consumed_liters if trip.fuel_consumed_liters > 0 else 0
# Calculate driver score
driver_score = self._calculate_driver_score(trip)
vehicle.status = VehicleStatus.IDLE
return {
'trip_id'Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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automotive-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
automotive-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
automotive-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added automotive-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for automotive-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
automotive-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend automotive-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for automotive-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: automotive-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
automotive-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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