last-mile-delivery-optimizer
Final delivery leg optimization skill including dynamic scheduling, time-window management, and delivery confirmation
Best use case
last-mile-delivery-optimizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Final delivery leg optimization skill including dynamic scheduling, time-window management, and delivery confirmation
Teams using last-mile-delivery-optimizer 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/last-mile-delivery-optimizer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How last-mile-delivery-optimizer Compares
| Feature / Agent | last-mile-delivery-optimizer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Final delivery leg optimization skill including dynamic scheduling, time-window management, and delivery confirmation
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
# Last-Mile Delivery Optimizer
## Overview
The Last-Mile Delivery Optimizer focuses on the final delivery leg optimization including dynamic scheduling, time-window management, and delivery confirmation. It maximizes delivery density, manages customer expectations, and coordinates various delivery methods including traditional carriers and crowdsourced options.
## Capabilities
- **Delivery Density Optimization**: Maximize deliveries per route by clustering nearby addresses
- **Time Window Scheduling**: Schedule deliveries within customer-preferred time windows
- **Driver Dispatch Optimization**: Assign deliveries to drivers based on location, capacity, and skills
- **Proof of Delivery Automation**: Capture signatures, photos, and timestamps for delivery confirmation
- **Failed Delivery Management**: Handle failed deliveries including rescheduling and alternative locations
- **Customer Communication Integration**: Send proactive updates on delivery status and ETA
- **Crowdsourced Delivery Coordination**: Integrate with gig economy platforms for flexible capacity
## Tools and Libraries
- Routing APIs
- Last-Mile Platforms
- Driver Apps
- Customer Notification Systems
## Used By Processes
- Last-Mile Delivery Optimization
- Route Optimization
- Multi-Channel Fulfillment
## Usage
```yaml
skill: last-mile-delivery-optimizer
inputs:
deliveries:
- delivery_id: "DEL001"
address: "123 Main St, Boston, MA"
coordinates: { lat: 42.3601, lng: -71.0589 }
time_window: { start: "14:00", end: "18:00" }
packages: 2
special_instructions: "Leave at front door"
customer_phone: "+1-555-0123"
- delivery_id: "DEL002"
address: "456 Oak Ave, Boston, MA"
coordinates: { lat: 42.3651, lng: -71.0549 }
time_window: { start: "10:00", end: "14:00" }
packages: 1
signature_required: true
drivers:
- driver_id: "DRV001"
current_location: { lat: 42.3501, lng: -71.0650 }
capacity_packages: 50
shift_end: "20:00"
skills: ["signature_capture", "heavy_items"]
optimization_parameters:
optimize_for: "delivery_density"
max_route_time_hours: 8
customer_notification: true
outputs:
delivery_routes:
- driver_id: "DRV001"
route:
- delivery_id: "DEL002"
sequence: 1
eta: "11:30"
eta_window: { start: "11:15", end: "11:45" }
- delivery_id: "DEL001"
sequence: 2
eta: "15:30"
eta_window: { start: "15:15", end: "15:45" }
total_deliveries: 2
total_distance_km: 12.5
estimated_completion: "16:00"
customer_notifications:
- delivery_id: "DEL001"
notification_type: "eta_update"
scheduled_time: "14:00"
message: "Your delivery is on the way. Expected arrival: 3:30 PM"
metrics:
deliveries_per_route: 15.5
average_delivery_time_minutes: 8
on_time_delivery_rate: 96.5
```
## Integration Points
- Transportation Management Systems (TMS)
- Order Management Systems
- Customer Communication Platforms
- Driver Mobile Apps
- GPS/Telematics Systems
## Performance Metrics
- Deliveries per route
- On-time delivery rate
- First-attempt success rate
- Cost per delivery
- Customer satisfaction scoreRelated Skills
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