logistics-expert
Expert guidance for supply chain management, logistics optimization, warehouse management systems, and transportation planning.
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Installation Guide
How to use logistics-expert on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
logistics-expert
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches logistics-expert from personamanagmentlayer/pcl and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate logistics-expert. Access via /logistics-expert in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Logistics Expert
Expert guidance for supply chain management, logistics optimization, warehouse management systems, and transportation planning.
Core Concepts
Supply Chain Management
- Inventory management
- Demand forecasting
- Procurement and sourcing
- Warehouse management (WMS)
- Transportation management (TMS)
- Order fulfillment
- Last-mile delivery
Optimization
- Route optimization
- Load planning
- Inventory optimization
- Network design
- Cost minimization
- Delivery scheduling
Technologies
- RFID and barcode scanning
- GPS tracking
- IoT sensors
- Predictive analytics
- Automated warehouses
- Drone delivery
Warehouse Management System
from dataclasses import dataclass
from typing import List, Optional
from datetime import datetime
from enum import Enum
class StorageType(Enum):
PALLET = "pallet"
SHELF = "shelf"
BULK = "bulk"
COLD = "cold_storage"
@dataclass
class Location:
location_id: str
zone: str
aisle: str
rack: str
level: int
storage_type: StorageType
capacity: float
current_load: float
@dataclass
class Product:
sku: str
name: str
category: str
weight: float
volume: float
storage_requirements: str
@dataclass
class InventoryItem:
item_id: str
sku: str
quantity: int
location_id: str
received_date: datetime
expiry_date: Optional[datetime]
batch_number: str
class WMS:
"""Warehouse Management System"""
def __init__(self, db):
self.db = db
def receive_shipment(self, shipment):
"""Process incoming shipment"""
items_received = []
for item in shipment.items:
# Find optimal storage location
location = self.find_optimal_location(item)
# Create inventory record
inventory_item = InventoryItem(
item_id=generate_id(),
sku=item.sku,
quantity=item.quantity,
location_id=location.location_id,
received_date=datetime.now(),
expiry_date=item.expiry_date,
batch_number=item.batch_number
)
self.db.save_inventory(inventory_item)
self.update_location_capacity(location, item)
items_received.append(inventory_item)
return {
'shipment_id': shipment.shipment_id,
'items_received': len(items_received),
'status': 'completed'
}
def find_optimal_location(self, item):
"""Find best storage location for item"""
product = self.db.get_product(item.sku)
available_locations = self.db.get_available_locations(
storage_type=product.storage_requirements,
min_capacity=product.volume * item.quantity
)
# Prioritize locations
# 1. Same SKU for efficient picking
# 2. Closest to shipping area for fast-moving items
# 3. Maximize space utilization
same_sku_locations = [
loc for loc in available_locations
if self.has_same_sku(loc, item.sku)
]
if same_sku_locations:
return same_sku_locations[0]
# Select closest to shipping for fast-moving items
if product.category == 'fast-moving':
return min(available_locations, key=lambda l: l.distance_to_shipping)
# Otherwise, optimize space utilization
return max(available_locations, key=lambda l: l.utilization_score)
def pick_order(self, order_id):
"""Generate picking list and route"""
order = self.db.get_order(order_id)
picking_list = []
for line_item in order.line_items:
inventory = self.db.find_inventory(
sku=line_item.sku,
quantity=line_item.quantity
)
picking_list.append({
'sku': line_item.sku,
'quantity': line_item.quantity,
'location': inventory.location_id,
'batch': inventory.batch_number
})
# Optimize picking route
optimized_route = self.optimize_picking_route(picking_list)
return {
'order_id': order_id,
'picking_list': optimized_route,
'estimated_time': self.estimate_picking_time(optimized_route)
}
def optimize_picking_route(self, picking_list):
"""Optimize warehouse picking route"""
# Sort by zone, aisle, rack for efficient walking path
sorted_picks = sorted(
picking_list,
key=lambda x: (
self.get_location_zone(x['location']),
self.get_location_aisle(x['location']),
self.get_location_rack(x['location'])
)
)
return sorted_picks
def check_stock_level(self, sku):
"""Check current stock level"""
total_quantity = self.db.sum_quantity_by_sku(sku)
product = self.db.get_product(sku)
status = 'normal'
if total_quantity <= product.reorder_point:
status = 'reorder'
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Use Cases
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- DDhruvi Jain★★★★★Dec 8, 2024
I recommend logistics-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- AAarav Agarwal★★★★★Dec 8, 2024
logistics-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- LLayla Mehta★★★★★Dec 4, 2024
logistics-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- OOshnikdeep★★★★★Nov 27, 2024
Useful defaults in logistics-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- LLayla Ramirez★★★★★Nov 27, 2024
Keeps context tight: logistics-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
- EEmma White★★★★★Nov 23, 2024
Registry listing for logistics-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
- GGanesh Mohane★★★★★Oct 18, 2024
logistics-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
- LLayla Kim★★★★★Oct 18, 2024
Registry listing for logistics-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
- YYusuf Smith★★★★★Oct 14, 2024
Keeps context tight: logistics-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
- SSakshi Patil★★★★★Sep 25, 2024
logistics-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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