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 maritime-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/maritime-expert)<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/maritime-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/maritime-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/maritime-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/maritime-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00059 | $0.01904 |
| Opus 5 | $0.00030 | $0.00952 |
| Sonnet 5 | $0.00012 | $0.00381 |
| Haiku 4.5 | $0.00006 | $0.00190 |
Grade A, and why
maritime-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 7d 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 — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Maritime Expert
Expert guidance for maritime systems, vessel tracking, port operations, cargo management, maritime logistics, and shipping industry software.
Core Concepts
Maritime Systems
- Vessel Traffic Services (VTS)
- Port Management Systems
- Cargo Management Systems
- Fleet Management
- Maritime Communication Systems
- Container Terminal Operating Systems (TOS)
- Ship Performance Monitoring
Maritime Technologies
- AIS (Automatic Identification System)
- ECDIS (Electronic Chart Display and Information System)
- Satellite communication (VSAT)
- Weather routing systems
- Ballast water management
- Engine monitoring systems
- Container tracking (IoT)
Standards and Protocols
- IMO regulations (International Maritime Organization)
- SOLAS (Safety of Life at Sea)
- MARPOL (Marine Pollution)
- ISM Code (International Safety Management)
- ISPS Code (International Ship and Port Facility Security)
- UN/EDIFACT for EDI
- NMEA protocols
Port Operations System
@dataclass
class BerthAllocation:
"""Berth allocation for vessel"""
allocation_id: str
vessel_imo: str
berth_id: str
scheduled_arrival: datetime
scheduled_departure: datetime
actual_arrival: Optional[datetime]
actual_departure: Optional[datetime]
cargo_operations: List[dict]
class PortOperationsSystem:
"""Port and terminal operations management"""
def __init__(self):
self.berths = {}
self.allocations = []
self.cargo_operations = []
def allocate_berth(self, vessel_imo: str, eta: datetime, cargo_type: str) -> dict:
"""Allocate berth for arriving vessel"""
# Find suitable berth
suitable_berth = self._find_suitable_berth(cargo_type, eta)
if not suitable_berth:
return {'error': 'No suitable berth available'}
# Estimate time at berth
time_at_berth = self._estimate_port_time(cargo_type)
allocation = BerthAllocation(
allocation_id=self._generate_allocation_id(),
vessel_imo=vessel_imo,
berth_id=suitable_berth['berth_id'],
scheduled_arrival=eta,
scheduled_departure=eta + timedelta(hours=time_at_berth),
actual_arrival=None,
actual_departure=None,
cargo_operations=[]
)
self.allocations.append(allocation)
return {
'allocation_id': allocation.allocation_id,
'berth_id': suitable_berth['berth_id'],
'scheduled_arrival': eta.isoformat(),
'scheduled_departure': allocation.scheduled_departure.isoformat(),
'estimated_hours_at_berth': time_at_berth
}
def track_container(self, container_number: str) -> dict:
"""Track container through port"""
# Container tracking using IoT sensors
container_data = {
'container_number': container_number,
'status': 'in_yard',
'location': 'Block A, Row 12, Tier 3',
'last_move': datetime.now() - timedelta(hours=2),
'vessel_loaded': None,
'customs_cleared': True,
'temperature': 5.0 # For reefer containers
}
return container_data
def optimize_yard_operations(self, expected_moves: int) -> dict:
"""Optimize container yard operations"""
# Simplified yard optimization
# In production, would use complex algorithms
return {
'expected_moves': expected_moves,
'optimal_sequence': 'calculated',
'estimated_time_hours': expected_moves * 0.1, # 6 minutes per move
'crane_allocation': {
'crane_1': expected_moves // 2,
'crane_2': expected_moves // 2
}
}
def _find_suitable_berth(self, cargo_type: str, eta: datetime) -> Optional[dict]:
"""Find suitable berth for vessel"""
# Check berth availability and suitability
for berth_id, berth in self.berths.items():
if cargo_type in berth['cargo_types']:
# Check if berth is available
if self._is_berth_available(berth_id, eta):
return berth
return None
def _is_berth_available(self, berth_id: str, time: datetime) -> bool:
"""Check if berth is available at given time"""
for allocation in self.allocations:
if allocation.berth_id == berth_id:
if allocation.scheduled_arrival <= time <= allocation.scheduled_departure:
return False
return True
def _estimate_port_time(self, cargo_type: str) -> float:
"""Estimate time vessel will spend in port (hours)"""
port_times = {
'container': 24,
'bulk': 48,
'tanker': 18,
'general_cargo': 36
}
return port_times.get(cargo_type, 24)
def _generate_allocation_id(self) -> str:
import uuid
return f"BERTH-{uuid.uuid4().hex[:8].upper()}"
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago Changed · -290 lines · +37 tokens per session 486b64032a0e
- 9d ago First seen · 567 lines · 22 tokens per session scan A 14886e5ddc76
maritime-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (40 stars, last pushed 2d ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,904 once invoked, about $0.0003 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 2026-09-03.
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