AgentSkillsCN

Assess Vehicle Recall Risk

评估车辆召回风险

SKILL.md

Skill: Assess Vehicle Recall Risk

Domain

automotive

Description

Evaluates vehicle components and systems for potential recall risk based on warranty claim patterns, field failure data, and regulatory compliance requirements specific to automotive OEM standards.

Tags

automotive, recall-management, quality-assurance, risk-assessment, nhtsa-compliance

Use Cases

  • •Proactive recall risk identification
  • •Warranty trend analysis
  • •Supplier quality monitoring
  • •NHTSA early warning compliance

Proprietary Business Rules

Rule 1: Failure Rate Threshold

Components exceeding proprietary failure rate thresholds (varying by safety criticality) trigger recall evaluation.

Rule 2: Geographic Clustering

Failures concentrated in specific regions may indicate environmental factors requiring targeted investigation.

Rule 3: Time-in-Service Correlation

Failure patterns correlated with vehicle age identify potential design life issues.

Rule 4: Safety System Prioritization

Brake, steering, and restraint system failures have accelerated review timelines.

Input Parameters

  • •component_id (string): Component part number
  • •failure_count (int): Number of reported failures
  • •vehicles_in_field (int): Total vehicles with component
  • •failure_reports (list): Detailed failure report data
  • •component_category (string): Safety criticality category
  • •affected_models (list): List of affected vehicle models
  • •production_date_range (dict): Start and end production dates

Output

  • •recall_risk_level (string): LOW, MEDIUM, HIGH, CRITICAL
  • •recommended_action (string): Investigation, monitoring, or recall initiation
  • •risk_score (float): Calculated risk score (0-100)
  • •contributing_factors (list): Identified risk factors
  • •regulatory_timeline (dict): NHTSA reporting requirements

Implementation

The risk assessment logic is implemented in recall_assessor.py and references thresholds from CSV files:

  • •categories.csv - Reference data
  • •risk_scoring.csv - Reference data
  • •geographic_regions.csv - Reference data
  • •nhtsa_thresholds.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from recall_assessor import assess_recall_risk

result = assess_recall_risk(
    component_id="BRK-2024-001",
    failure_count=45,
    vehicles_in_field=125000,
    failure_reports=[{"severity": "high", "region": "southwest", "mileage": 35000}],
    component_category="brake_system",
    affected_models=["Sedan-X", "SUV-Y"],
    production_date_range={"start": "2023-01-01", "end": "2024-06-30"}
)

print(f"Risk Level: {result['recall_risk_level']}")

Test Execution

python
from recall_assessor import assess_recall_risk

result = assess_recall_risk(
    component_id=input_data.get('component_id'),
    failure_count=input_data.get('failure_count', 0),
    vehicles_in_field=input_data.get('vehicles_in_field', 1),
    failure_reports=input_data.get('failure_reports', []),
    component_category=input_data.get('component_category'),
    affected_models=input_data.get('affected_models', []),
    production_date_range=input_data.get('production_date_range', {})
)