fleet-analytics-dashboard

Comprehensive fleet performance analytics skill tracking KPIs across fuel, utilization, maintenance, and driver performance

509 stars

Best use case

fleet-analytics-dashboard is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Comprehensive fleet performance analytics skill tracking KPIs across fuel, utilization, maintenance, and driver performance

Teams using fleet-analytics-dashboard should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/fleet-analytics-dashboard/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/business/logistics/skills/fleet-analytics-dashboard/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/fleet-analytics-dashboard/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How fleet-analytics-dashboard Compares

Feature / Agentfleet-analytics-dashboardStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Comprehensive fleet performance analytics skill tracking KPIs across fuel, utilization, maintenance, and driver performance

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Fleet Analytics Dashboard

## 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

```yaml
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

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