automotive-expert

personamanagmentlayer/pcl · updated Apr 8, 2026

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$npx skills add https://github.com/personamanagmentlayer/pcl --skill automotive-expert
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summary

Expert guidance for automotive systems, connected vehicles, fleet management, telematics, advanced driver assistance systems (ADAS), and automotive software development.

skill.md

Automotive Expert

Expert guidance for automotive systems, connected vehicles, fleet management, telematics, advanced driver assistance systems (ADAS), and automotive software development.

Core Concepts

Automotive Systems

  • Telematics and fleet management
  • Connected car platforms
  • Advanced Driver Assistance Systems (ADAS)
  • Electric Vehicle (EV) management
  • Vehicle-to-Everything (V2X) communication
  • Infotainment systems
  • Diagnostic systems (OBD-II)

Technologies

  • CAN bus and automotive networks
  • AUTOSAR architecture
  • Over-the-air (OTA) updates
  • Autonomous driving systems
  • Battery management systems
  • Computer vision for ADAS
  • Edge computing in vehicles

Standards and Protocols

  • ISO 26262 (functional safety)
  • AUTOSAR (automotive software architecture)
  • J1939 (heavy-duty vehicle communication)
  • UDS (Unified Diagnostic Services)
  • SOME/IP (service-oriented middleware)
  • MQTT for telematics
  • CAN, LIN, FlexRay protocols

Fleet Management System

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'
how to use automotive-expert

How to use automotive-expert on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add automotive-expert
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/personamanagmentlayer/pcl --skill automotive-expert

The skills CLI fetches automotive-expert from GitHub repository personamanagmentlayer/pcl and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/automotive-expert

Reload or restart Cursor to activate automotive-expert. Access the skill through slash commands (e.g., /automotive-expert) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.668 reviews
  • Tariq Robinson· Dec 20, 2024

    automotive-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Aarav Diallo· Dec 16, 2024

    automotive-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Fatima Li· Dec 4, 2024

    automotive-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yuki Chawla· Dec 4, 2024

    We added automotive-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Fatima Abbas· Nov 23, 2024

    Registry listing for automotive-expert matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Camila Reddy· Nov 23, 2024

    automotive-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Yash Thakker· Nov 19, 2024

    I recommend automotive-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Tariq White· Nov 11, 2024

    Registry listing for automotive-expert matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Chinedu Li· Nov 7, 2024

    Keeps context tight: automotive-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chinedu Thomas· Oct 26, 2024

    automotive-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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