automotive-v2x

automotive-v2x is a skill for Claude Code, Codex from pangzhenying2025/hermes-automotive-skills. It costs 50 tokens per session (42,164 once invoked), scanned A, original, MIT.

A guide to vehicle-to-everything communication, where cars exchange information with other vehicles, roadside equipment, and mobile networks. It covers C-V2X, 5G integration, infrastructure links, safety applications, communication standards, and security certificates.

In plain words
What is it for?
Use it to design vehicle-to-vehicle and vehicle-to-infrastructure features, integrate 5G and roadside systems, select communication modes, and manage V2X security certificates.
Why use it?
It helps engineers choose how vehicles communicate when cellular coverage is available or absent, while accounting for timing, reliability, coverage, and trust.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to design vehicle-to-vehicle and vehicle-to-infrastructure features, integrate 5G and roadside systems, select communication modes, and manage V2X security certificates.

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Install with agentmods
npx agentmods add skills/pangzhenying2025/hermes-automotive-skills/automotive-v2x
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 pangzhenying2025/hermes-automotive-skills --skill automotive-v2x
Clone the repo
git clone --depth 1 https://github.com/pangzhenying2025/hermes-automotive-skills

Made for: Claude Code, Codex.

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.

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README.md
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Your own site
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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-v2x

Your own site · 80×15
<a href="https://agentmods.dev/skills/pangzhenying2025/hermes-automotive-skills/automotive-v2x"><img src="https://agentmods.dev/badge/skills/pangzhenying2025/hermes-automotive-skills/automotive-v2x.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 42,164 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.
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 $0.00050 $0.42164
Opus 5 $0.00025 $0.21082
Sonnet 5 $0.00010 $0.08433
Haiku 4.5 $0.00005 $0.04216

Measured 9d ago against content hash a4332fdf732c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

automotive-v2x 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 9d 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.

skills/automotive-v2x/SKILL.md · 5,636 lines

How it starts

The opening of the file, as written. The whole thing — 5,636 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Automotive V2X

Cv2X 5G Integration

C-V2X and 5G Integration

Overview

C-V2X (Cellular V2X) integration with 5G networks including PC5 sidelink communication modes, network slicing, Multi-access Edge Computing (MEC), Ultra-Reliable Low-Latency Communication (URLLC), and 5G NR V2X features.

C-V2X Communication Modes

Mode 3 vs Mode 4 Comparison

Feature Mode 3 (Network Scheduled) Mode 4 (Autonomous/D2D)
Infrastructure Required Yes (eNodeB/gNodeB) No (direct sidelink)
Resource Allocation Network-scheduled (centralized) Distributed sensing
Coverage Dependency Requires cellular coverage Works without network
Typical Latency 20-50 ms (via network) 10-20 ms (direct)
QoS Guarantee Network-enforced QoS Best-effort coordination
Ideal Use Case Urban with good coverage Rural, tunnels, emergencies
Handover Network-managed Autonomous
Power Consumption Higher (constant network sync) Lower (periodic only)

5G NR-V2X Physical Layer

Frequency Bands:

5.9 GHz ITS Band:
- 5.855-5.925 GHz (US, EU, Asia harmonized)
- Channel bandwidth: 10/20 MHz
- Supports both DSRC coexistence and C-V2X

Licensed Spectrum:
- Band n78 (3.5 GHz): High capacity urban
- Band n79 (4.7 GHz): Regional deployments
- Mmwave (28/39 GHz): Ultra-high data rate applications

Numerology:

Subcarrier spacing: 15/30/60 kHz
- 15 kHz: Long range, low mobility
- 30 kHz: Standard V2X (recommended)
- 60 kHz: High mobility scenarios

Symbol duration: 66.7/33.3/16.7 μs
Cyclic prefix: 4.7/2.3/1.2 μs

Network Slicing for V2X

Slice Configuration

# network_slicing.py
"""
5G Network slicing for differentiated V2X services.
"""

from dataclasses import dataclass
from enum import Enum
from typing import List

class SliceServiceType(Enum):
    """Service and Slice Differentiator (SST)"""
    URLLC = 1  # Ultra-reliable low-latency
    EMBB = 2   # Enhanced mobile broadband
    MMTC = 3   # Massive machine-type communications

@dataclass
class NetworkSliceDescriptor:
    """5G Network Slice Selection Assistance Information (NSSAI)"""
    sst: SliceServiceType  # Slice/Service Type
    sd: int  # Slice Differentiator (24 bits)

    # Performance KPIs
    target_latency_ms: int
    reliability_percent: float
    max_data_rate_mbps: int
    connection_density_per_km2: int

    # Resource allocation
    guaranteed_bit_rate_mbps: int
    priority_level: int  # 1 (highest) to 15 (lowest)

# V2V Safety Communications Slice
SLICE_V2V_SAFETY = NetworkSliceDescriptor(
    sst=SliceServiceType.URLLC,
    sd=0x000001,  # V2V safety specific
    target_latency_ms=5,
    reliability_percent=99.9999,  # Six nines
    max_data_rate_mbps=10,
    connection_density_per_km2=10000,
    guaranteed_bit_rate_mbps=2,
    priority_level=1
)

# V2I Traffic Management Slice
SLICE_V2I_TRAFFIC = NetworkSliceDescriptor(
    sst=SliceServiceType.URLLC,
    sd=0x000002,
    target_latency_ms=20,
    reliability_percent=99.99,
    max_data_rate_mbps=5,
    connection_density_per_km2=5000,
    guaranteed_bit_rate_mbps=1,
    priority_level=3
)

# V2N Infotainment Slice
SLICE_V2N_INFOTAINMENT = NetworkSliceDescriptor(
    sst=SliceServiceType.EMBB,
    sd=0x000003,
    target_latency_ms=100,
    reliability_percent=99.0,
    max_data_rate_mbps=100,
    connection_density_per_km2=1000,
    guaranteed_bit_rate_mbps=10,
    priority_level=10
)

class NetworkSliceManager:
    """Manage network slice selection for V2X traffic."""

    def __init__(self):
        self.available_slices = [
            SLICE_V2V_SAFETY,
            SLICE_V2I_TRAFFIC,
            SLICE_V2N_INFOTAINMENT
        ]
        self.current_slice = None

    def select_slice_for_message(self, message_type: str, qos_requirement: str) -> NetworkSliceDescriptor:
        """
        Select appropriate network slice based on message type and QoS.

        Args:
            message_type: "BSM", "DENM", "CAM", "SPaT", "MAP", etc.
            qos_requirement: "critical", "high", "medium", "low"

        Returns:
            NetworkSliceDescriptor
        """
        # Safety-critical messages
        if message_type in ["BSM", "CAM", "DENM", "EEBL"] or qos_requirement == "critical":
            return SLICE_V2V_SAFETY

        # Infrastructure messages
        elif message_type in ["SPaT", "MAP", "TIM"] or qos_requirement == "high":
            return SLICE_V2I_TRAFFIC

        # Non-critical services
        else:
            return SLICE_V2N_INFOTAINMENT

    def request_slice_activation(self, nssai: NetworkSliceDescriptor) -> bool:
        """
        Request slice activation from 5G core.

        In production: NGAP signaling to AMF (Access and Mobility Management Function)
        """
        print(f"Requesting slice activation:")
        print(f"  SST: {nssai.sst.name}")
        print(f"  SD: {nssai.sd:#08x}")
        print(f"  Latency: {nssai.target_latency_ms} ms")
        print(f"  Reliability: {nssai.reliability_percent}%")

        # Simulate AMF response
        self.current_slice = nssai
        return True

Read the full file on GitHub · 5,636 lines

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. 9d ago First seen · 5,636 lines · 50 tokens per session scan A a4332fdf732c

Subscribe to this mod's changes

automotive-v2x is a skill published in the GitHub repository pangzhenying2025/hermes-automotive-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 42,164 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 2026-09-03.

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