Expert guidance for supply chain management, logistics optimization, warehouse management systems, and transportation planning.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlogistics-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches logistics-expert from personamanagmentlayer/pcl and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
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.
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.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
1
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1
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Run in your terminal
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Expert guidance for supply chain management, logistics optimization, warehouse management systems, and transportation planning.
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'
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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erichowens/some_claude_skills
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parcadei/continuous-claude-v3
cursor/plugins
I recommend logistics-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
logistics-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
logistics-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in logistics-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: logistics-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for logistics-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
logistics-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for logistics-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: logistics-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
logistics-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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