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Get Started Free →Manage construction equipment fleet. Track utilization, maintenance, and assignments.
.claude/skills/datadrivenconstruction-equipment-fleet-manager/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 147% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -7% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 57% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 46% | 0% |
pythonimport 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)
pythonfleet = EquipmentFleetManager("ABC Construction") crane = fleet.add_equipment("Tower Crane #1", EquipmentType.CRANE, "Liebherr", "280 EC-H", 2020, 150, 1200) assignment = fleet.assign_equipment(crane.equipment_id, "Office Tower", "Site A", date.today(), "John Smith") report = fleet.get_utilization_report() print(f"Utilization: {report['utilization_rate']}%")
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-06 | pass→pass | 19,027 | 24,794 | +30% | 1 | 1 | 0% | 3,781 | 7,242 | +92% | 0 | 0 | — |
case-07 | fail→pass | 5,257 | 2,982 | -43% | 1 | 1 | 0% | 908 | 2,245 | +147% | 0 | 0 | — |
case-08 | fail→pass | 15,216 | 4,853 | -68% | 1 | 1 | 0% | 2,797 | 2,605 | -7% | 0 | 0 | — |
case-01 | fail→pass | 14,172 | 11,820 | -17% | 1 | 1 | 0% | 3,157 | 4,300 | +36% | 0 | 0 | — |
case-02 | fail→pass | 14,998 | 13,932 | -7% | 1 | 1 | 0% | 2,992 | 4,692 | +57% | 0 | 0 | — |
case-03 | fail→pass | 15,489 | 13,203 | -15% | 1 | 1 | 0% | 3,134 | 4,580 | +46% | 0 | 0 | — |
case-04 | pass→pass | 12,917 | 13,619 | +5% | 1 | 1 | 0% | 2,748 | 4,585 | +67% | 0 | 0 | — |
case-05 | pass→pass | 10,578 | 13,336 | +26% | 1 | 1 | 0% | 2,162 | 4,170 | +93% | 0 | 0 | — |
case-09 | fail→pass | 8,631 | 6,178 | -28% | 1 | 1 | 0% | 1,750 | 2,901 | +66% | 0 | 0 | — |
case-10 | fail→fail | 8,042 | 3,047 | -62% | 1 | 1 | 0% | 1,577 | 2,252 | +43% | 0 | 0 | — |
case-11 | fail→pass | 4,798 | 2,603 | -46% | 1 | 1 | 0% | 803 | 2,057 | +156% | 0 | 0 | — |
case-12 | fail→pass | 12,299 | 5,061 | -59% | 1 | 1 | 0% | 2,466 | 2,575 | +4% | 0 | 0 | — |
case-13 | fail→fail | 5,312 | 3,724 | -30% | 1 | 1 | 0% | 954 | 2,320 | +143% | 0 | 0 | — |
case-14 | fail→pass | 14,008 | 6,332 | -55% | 1 | 1 | 0% | 2,855 | 2,866 | +0% | 0 | 0 | — |
case-15 | fail→pass | 11,075 | 6,450 | -42% | 1 | 1 | 0% | 1,719 | 2,763 | +61% | 0 | 0 | — |
case-16 | fail→pass | 14,017 | 4,553 | -68% | 1 | 1 | 0% | 2,707 | 2,633 | -3% | 0 | 0 | — |
case-17 | fail→pass | 12,544 | 5,151 | -59% | 1 | 1 | 0% | 2,340 | 2,719 | +16% | 0 | 0 | — |
case-18 | fail→pass | 8,446 | 3,450 | -59% | 1 | 1 | 0% | 1,359 | 2,296 | +69% | 0 | 0 | — |
case-19 | fail→pass | 4,483 | 3,042 | -32% | 1 | 1 | 0% | 849 | 2,228 | +162% | 0 | 0 | — |
case-20 | pass→pass | 6,567 | 4,961 | -24% | 1 | 1 | 0% | 1,220 | 2,632 | +116% | 0 | 0 | — |
case-21 | fail→pass | 8,819 | 2,877 | -67% | 1 | 1 | 0% | 1,460 | 2,155 | +48% | 0 | 0 | — |
case-22 | pass→pass | 10,166 | 3,898 | -62% | 1 | 1 | 0% | 1,802 | 2,360 | +31% | 0 | 0 | — |
case-23 | fail→pass | 12,125 | 12,834 | +6% | 1 | 1 | 0% | 2,473 | 4,375 | +77% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +70 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.