Fleet Analytics Dashboard
SkillDatabases & dataComprehensive fleet performance analytics skill tracking KPIs across fuel, utilization, maintenance, and driver performance
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Fleet Analytics Dashboard skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/business/logistics/skills/fleet-analytics-dashboard/SKILL.md and read by ahel’s review.
Overview
The Fleet Analytics Dashboard provides comprehensive fleet performance analytics tracking KPIs across fuel efficiency, utilization, maintenance, and driver performance. It consolidates data from multiple sources to provide actionable insights and benchmarking capabilities for fleet optimization.
Capabilities
- Fuel Efficiency Analysis: Track and analyze fuel consumption patterns, identify inefficiencies, and benchmark performance
- Utilization Rate Tracking: Monitor vehicle utilization rates and identify underutilized assets
- Cost Per Mile Calculation: Calculate and track total cost per mile including fuel, maintenance, and labor
- Driver Scorecard Generation: Generate driver performance scorecards based on safety, efficiency, and compliance
- Idle Time Monitoring: Track idle time and identify opportunities for reduction
- Benchmark Comparison: Compare fleet performance against industry benchmarks and internal targets
- Trend Analysis and Alerting: Identify trends and generate alerts for performance deviations
Tools and Libraries
- Telematics Platforms
- BI Tools (Tableau, Power BI)
- Fuel Card Integration
- Data Analytics Libraries
Used By Processes
- Fleet Performance Analytics
- Vehicle Maintenance Planning
- Driver Scheduling and Compliance
Usage
skill: fleet-analytics-dashboard
inputs:
fleet:
fleet_id: "FLEET001"
vehicles: 50
analysis_period:
start: "2026-01-01"
end: "2026-01-24"
data_sources:
telematics: true
fuel_cards: true
maintenance_system: true
eld_data: true
benchmarks:
fuel_efficiency_mpg: 7.0
utilization_percent: 85
cost_per_mile: 1.85
outputs:
fleet_summary:
total_miles: 875000
total_fuel_gallons: 131250
average_mpg: 6.67
total_cost: 1575000
cost_per_mile: 1.80
utilization_analysis:
average_utilization: 82.5
vehicles_underutilized: 8
idle_hours_total: 2500
idle_cost_estimate: 12500
fuel_analysis:
average_mpg: 6.67
mpg_trend: "improving"
best_performing_vehicles: ["VH012", "VH023", "VH045"]
worst_performing_vehicles: ["VH008", "VH031", "VH019"]
fuel_cost_total: 525000
driver_scorecards:
- driver_id: "DRV001"
overall_score: 92
safety_score: 95
efficiency_score: 88
compliance_score: 94
miles_driven: 8500
mpg: 6.9
- driver_id: "DRV002"
overall_score: 78
safety_score: 75
efficiency_score: 82
compliance_score: 78
miles_driven: 7200
mpg: 6.4
alerts:
- type: "utilization"
message: "8 vehicles below 70% utilization target"
severity: "warning"
- type: "fuel"
message: "Vehicle VH008 fuel efficiency 15% below fleet average"
severity: "attention"
benchmark_comparison:
fuel_efficiency: { actual: 6.67, benchmark: 7.0, variance: -4.7 }
utilization: { actual: 82.5, benchmark: 85, variance: -2.9 }
cost_per_mile: { actual: 1.80, benchmark: 1.85, variance: 2.7 }
Integration Points
- Telematics Systems
- Fuel Card Providers
- Maintenance Management Systems
- ELD Providers
- Business Intelligence Platforms
Performance Metrics
- Fleet MPG
- Cost per mile
- Vehicle utilization rate
- Driver safety score
- Maintenance cost ratio
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
fleet-analytics-dashboard- Source
- github.com/a5c-ai/babysitter
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