automotive-expert

automotive-expert is a skill for Claude Code from personamanagmentlayer/pcl. It costs 64 tokens per session (2,958 once invoked), scanned A, original, Apache-2.0.

A guide to automotive software and connected-vehicle systems, including vehicle communication networks, driver-assistance systems, electric vehicles, fleet tools, and diagnostics.

In plain words
What is it for?
It supports work on telematics, fleet management, infotainment, battery systems, over-the-air updates, vehicle diagnostics, and automotive protocols such as CAN and AUTOSAR.
Why use it?
It gives developers context for the specialized hardware, safety concerns, communication standards, and software used in vehicles.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: positional $N argument.

Good fit It supports work on telematics, fleet management, infotainment, battery systems, over-the-air updates, vehicle diagnostics, and automotive protocols such as CAN and AUTOSAR.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/personamanagmentlayer/pcl/automotive-expert
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add personamanagmentlayer/pcl --skill automotive-expert
Clone the repo
git clone --depth 1 https://github.com/personamanagmentlayer/pcl

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for automotive-expert

README.md
[![agentmods](https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/automotive-expert/github.svg)](https://agentmods.dev/skills/personamanagmentlayer/pcl/automotive-expert)
Your own site
<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/automotive-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/automotive-expert/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for automotive-expert

Your own site · 80×15
<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/automotive-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/automotive-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,958 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 18 Mar 2026
  • Snyk pass 15 Feb 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00064 $0.02958
Opus 5 $0.00032 $0.01479
Sonnet 5 $0.00013 $0.00592
Haiku 4.5 $0.00006 $0.00296

Measured 6d ago against content hash ef2c6eae1190, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

automotive-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

stdlib/domains/automotive-expert/SKILL.md · 422 lines

How it starts

The opening of the file, as written. The whole thing — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Connected Vehicle Platform

@dataclass
class VehicleTelemetry:
    """Real-time vehicle telemetry data"""
    vehicle_id: str
    timestamp: datetime
    location: tuple
    speed_kmh: float
    rpm: int
    engine_temp_c: float
    battery_voltage: float
    fuel_level_percent: float
    odometer_km: int
    dtc_codes: List[str]  # Diagnostic Trouble Codes

class ConnectedVehiclePlatform:
    """Connected car platform with OTA updates"""

    def __init__(self):
        self.vehicles = {}
        self.telemetry_buffer = []
        self.ota_updates = {}

    def process_telemetry(self, telemetry: VehicleTelemetry) -> dict:
        """Process incoming telemetry data"""
        self.telemetry_buffer.append(telemetry)

        # Analyze telemetry for anomalies
        alerts = []

        # Check engine temperature
        if telemetry.engine_temp_c > 110:
            alerts.append({
                'type': 'high_engine_temp',
                'severity': 'warning',
                'value': telemetry.engine_temp_c,
                'message': 'Engine temperature above normal'
            })

        # Check battery voltage
        if telemetry.battery_voltage < 12.0:
            alerts.append({
                'type': 'low_battery',
                'severity': 'warning',
                'value': telemetry.battery_voltage,
                'message': 'Battery voltage low'
            })

        # Check for diagnostic trouble codes
        if telemetry.dtc_codes:
            alerts.append({
                'type': 'dtc_codes',
                'severity': 'critical',
                'codes': telemetry.dtc_codes,
                'message': f'{len(telemetry.dtc_codes)} diagnostic code(s) detected'
            })

        # Check for harsh driving
        if len(self.telemetry_buffer) >= 2:
            prev = self.telemetry_buffer[-2]
            if telemetry.vehicle_id == prev.vehicle_id:
                time_diff = (telemetry.timestamp - prev.timestamp).total_seconds()
                if time_diff > 0:
                    acceleration = (telemetry.speed_kmh - prev.speed_kmh) / time_diff

                    if abs(acceleration) > 5:  # > 5 km/h per second
                        alerts.append({
                            'type': 'harsh_driving',
                            'severity': 'info',
                            'acceleration': acceleration,
                            'message': 'Harsh acceleration/braking detected'
                        })

        return {
            'vehicle_id': telemetry.vehicle_id,
            'timestamp': telemetry.timestamp.isoformat(),
            'alerts': alerts,
            'health_score': self._calculate_vehicle_health(telemetry)
        }

    def deploy_ota_update(self,
                         vehicle_ids: List[str],
                         update_package: dict) -> dict:
        """Deploy over-the-air software update"""
        update_id = self._generate_update_id()

        ota_update = {
            'update_id': update_id,
            'version': update_package['version'],
            'description': update_package['description'],
            'package_size_mb': update_package['size_mb'],
            'target_vehicles': vehicle_ids,
            'deployed_at': datetime.now(),
            'status_by_vehicle': {}
        }

        for vehicle_id in vehicle_ids:
            # Schedule update for vehicle
            ota_update['status_by_vehicle'][vehicle_id] = {
                'status': 'scheduled',
                'download_progress': 0,
                'install_progress': 0
            }

        self.ota_updates[update_id] = ota_update

        return {
            'update_id': update_id,
            'vehicles_targeted': len(vehicle_ids),
            'estimated_completion': 'Within 48 hours'
        }

    def diagnose_vehicle(self, vehicle_id: str, dtc_codes: List[str]) -> dict:
        """Diagnose vehicle issues from DTC codes"""
        diagnoses = []

        for code in dtc_codes:
            diagnosis = self._lookup_dtc_code(code)
            diagnoses.append(diagnosis)

        # Calculate severity
        max_severity = max(d['severity'] for d in diagnoses)

        return {
            'vehicle_id': vehicle_id,
            'dtc_codes': dtc_codes,
            'diagnoses': diagnoses,
            'overall_severity': max_severity,
            'service_recommended': max_severity in ['high', 'critical']
        }

    def _calculate_vehicle_health(self, telemetry: VehicleTelemetry) -> float:
        """Calculate overall vehicle health score"""
        score = 100.0

        # Engine temperature
        if telemetry.engine_temp_c > 110:
            score -= 15
        elif telemetry.engine_temp_c > 100:
            score -= 5

        # Battery voltage
        if telemetry.battery_voltage < 11.5:
            score -= 20
        elif telemetry.battery_voltage < 12.0:
            score -= 10

        # DTC codes
        score -= len(telemetry.dtc_codes) * 15

        return max(0.0, score)

    def _lookup_dtc_code(self, code: str) -> dict:
        """Lookup diagnostic trouble code"""
        # Simplified DTC lookup
        # In production, would use comprehensive OBD-II code database

        dtc_database = {
            'P0171': {
                'description': 'System Too Lean (Bank 1)',
                'severity': 'medium',
                'possible_causes': ['Vacuum leak', 'Faulty MAF sensor', 'Fuel filter clogged']
            },
            'P0300': {
                'description': 'Random/Multiple Cylinder Misfire Detected',
                'severity': 'high',
                'possible_causes': ['Faulty spark plugs', 'Ignition coil failure', 'Fuel injector issue']
            }
        }

        return dtc_database.get(code, {
            'description': f'Unknown code: {code}',
            'severity': 'medium',
            'possible_causes': ['Requires diagnostic scan']
        })

    def _generate_update_id(self) -> str:
        import uuid
        return f"OTA-{uuid.uuid4().hex[:8].upper()}"

Read the full file on GitHub · 422 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago Changed · -338 lines · +39 tokens per session ef2c6eae1190
  2. 11d ago First seen · 760 lines · 25 tokens per session scan A 591c99cd1eee

Subscribe to this mod's changes

automotive-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (40 stars, last pushed yesterday), licensed Apache-2.0. It adds 64 tokens to every session and 2,958 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

automotive-telematics

Expert skill in cellular focusing on telematics domain applications. Covers 40 topics across telematics domain. Includes 40 skill files covering ASPICE Level 3, AUTOSAR 4.4, ISO 21434, ISO 26262.

pangzhenying2025/hermes-automotive-skills · 56 tokens

clawrouter

Hosted-gateway LLM router — save 84% on inference costs. A local proxy that forwards each request to the blockrun.ai gateway, which routes to the cheapest capable model across 78 models from OpenAI, Anthropic, Google, DeepSeek, xAI, Z.AI, and more. 7 free open-weight models included. Also exposes realtime market data…

BlockRunAI/ClawRouter · 222 tokens

surf

Use this skill — NOT browser or webfetch — for ALL Surf crypto-data calls. 83 endpoints at localhost:8402/v1/surf/ covering CEX/DEX markets, on-chain SQL over 80+ ClickHouse tables (Ethereum, Base, Arbitrum, BSC, TRON, HyperEVM, Tempo), 100M+ labeled wallets, prediction markets (Polymarket + Kalshi), social/CT…

BlockRunAI/ClawRouter · 148 tokens

phone

Verify phone numbers (carrier + SIM-swap fraud signals) and place AI-powered outbound voice calls via BlockRun's gateway (Twilio + Bland.ai). Trigger when the user asks to look up a number, check fraud risk, buy/rent a phone number, or place an AI voice call. Payment is automatic via x402 from the wallet.

BlockRunAI/ClawRouter · 72 tokens

imagegen

Generate or edit images via BlockRun's image API. Trigger when the user asks to generate, create, draw, make an image — or to edit, modify, change, or retouch an existing image.

BlockRunAI/ClawRouter · 45 tokens

polymarket-trading

Use when the user wants to actually PLACE, manage, or redeem bets on Polymarket (not just read odds — that's the blockrunpredexon data tools). Covers setup (deposit wallet, funding, approvals), buy/sell with confirm gating, positions, redeeming winnings, geoblock handling, and the end-to-end flow.

BlockRunAI/ClawRouter · 76 tokens