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 datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill equipment-fleet-managergit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote 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/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/equipment-fleet-manager)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/equipment-fleet-manager"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/equipment-fleet-manager/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/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/equipment-fleet-manager"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/equipment-fleet-manager.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 | $0.00018 | $0.01458 |
| Opus 5 | $0.00009 | $0.00729 |
| Sonnet 5 | $0.00004 | $0.00292 |
| Haiku 4.5 | $0.00002 | $0.00146 |
Grade A, and why
equipment-fleet-manager 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- equipment-fleet-manager — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Equipment Fleet Manager
Technical Implementation
import pandas as pd
from datetime import date, timedelta
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum
class EquipmentStatus(Enum):
AVAILABLE = "available"
IN_USE = "in_use"
MAINTENANCE = "maintenance"
REPAIR = "repair"
RETIRED = "retired"
class EquipmentType(Enum):
CRANE = "crane"
EXCAVATOR = "excavator"
LOADER = "loader"
FORKLIFT = "forklift"
GENERATOR = "generator"
COMPRESSOR = "compressor"
SCAFFOLDING = "scaffolding"
OTHER = "other"
@dataclass
class MaintenanceRecord:
record_id: str
equipment_id: str
maintenance_type: str
scheduled_date: date
completed_date: Optional[date]
cost: float
notes: str = ""
@dataclass
class Assignment:
assignment_id: str
equipment_id: str
project: str
location: str
start_date: date
end_date: Optional[date]
operator: str = ""
@dataclass
class Equipment:
equipment_id: str
name: str
equipment_type: EquipmentType
make: str
model: str
year: int
status: EquipmentStatus
hourly_rate: float
daily_rate: float
current_hours: float = 0
last_maintenance: Optional[date] = None
next_maintenance_hours: float = 500
assignments: List[Assignment] = field(default_factory=list)
class EquipmentFleetManager:
def __init__(self, company_name: str):
self.company_name = company_name
self.equipment: Dict[str, Equipment] = {}
self.maintenance_records: List[MaintenanceRecord] = {}
self._equip_counter = 0
self._assign_counter = 0
def add_equipment(self, name: str, equipment_type: EquipmentType,
make: str, model: str, year: int,
hourly_rate: float, daily_rate: float) -> Equipment:
self._equip_counter += 1
equip_id = f"EQ-{self._equip_counter:04d}"
equip = Equipment(
equipment_id=equip_id,
name=name,
equipment_type=equipment_type,
make=make,
model=model,
year=year,
status=EquipmentStatus.AVAILABLE,
hourly_rate=hourly_rate,
daily_rate=daily_rate
)
self.equipment[equip_id] = equip
return equip
def assign_equipment(self, equip_id: str, project: str, location: str,
start_date: date, operator: str = "") -> Assignment:
if equip_id not in self.equipment:
return None
self._assign_counter += 1
assign_id = f"ASN-{self._assign_counter:04d}"
assignment = Assignment(
assignment_id=assign_id,
equipment_id=equip_id,
project=project,
location=location,
start_date=start_date,
end_date=None,
operator=operator
)
self.equipment[equip_id].assignments.append(assignment)
self.equipment[equip_id].status = EquipmentStatus.IN_USE
return assignment
def return_equipment(self, equip_id: str, hours_used: float):
if equip_id in self.equipment:
equip = self.equipment[equip_id]
equip.status = EquipmentStatus.AVAILABLE
equip.current_hours += hours_used
if equip.assignments:
equip.assignments[-1].end_date = date.today()
def schedule_maintenance(self, equip_id: str, maintenance_type: str,
scheduled_date: date, cost: float):
if equip_id not in self.equipment:
return
record_id = f"MNT-{len(self.maintenance_records) + 1:04d}"
record = MaintenanceRecord(record_id, equip_id, maintenance_type,
scheduled_date, None, cost)
self.maintenance_records[record_id] = record
def get_available_equipment(self, equipment_type: EquipmentType = None) -> List[Equipment]:
available = [e for e in self.equipment.values()
if e.status == EquipmentStatus.AVAILABLE]
if equipment_type:
available = [e for e in available if e.equipment_type == equipment_type]
return available
def get_utilization_report(self) -> Dict[str, Any]:
in_use = sum(1 for e in self.equipment.values()
if e.status == EquipmentStatus.IN_USE)
total = len(self.equipment)
return {
'total_equipment': total,
'in_use': in_use,
'available': sum(1 for e in self.equipment.values()
if e.status == EquipmentStatus.AVAILABLE),
'maintenance': sum(1 for e in self.equipment.values()
if e.status == EquipmentStatus.MAINTENANCE),
'utilization_rate': round(in_use / total * 100, 1) if total > 0 else 0
}
def export_fleet(self, output_path: str):
data = [{
'ID': e.equipment_id,
'Name': e.name,
'Type': e.equipment_type.value,
'Make/Model': f"{e.make} {e.model}",
'Year': e.year,
'Status': e.status.value,
'Hours': e.current_hours,
'Daily Rate': e.daily_rate
} for e in self.equipment.values()]
pd.DataFrame(data).to_excel(output_path, index=False)
What ships with it
2 files 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.
- 8d ago First seen · 196 lines · 18 tokens per session scan A 92fc910194d1
equipment-fleet-manager is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (310 stars, last pushed 20d ago), licensed MIT. It adds 18 tokens to every session and 1,458 once invoked, about $0.0001 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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