Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add personamanagmentlayer/pcl --skill logistics-expertgit clone --depth 1 https://github.com/personamanagmentlayer/pclWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/personamanagmentlayer/pcl/logistics-expert)<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/logistics-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/logistics-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/logistics-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/logistics-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.02837 |
| Opus 5 | $0.00023 | $0.01418 |
| Sonnet 5 | $0.00009 | $0.00567 |
| Haiku 4.5 | $0.00005 | $0.00284 |
Grade A, and why
logistics-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 483 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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'
elif total_quantity <= product.safety_stock:
status = 'critical'
return {
'sku': sku,
'quantity': total_quantity,
'status': status,
'reorder_point': product.reorder_point
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 483 lines · 47 tokens per session scan A 8d1324fd6e39
logistics-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (40 stars, last pushed 2d ago), licensed Apache-2.0. It adds 47 tokens to every session and 2,837 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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