equipment-fleet-manager

equipment-fleet-manager is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 18 tokens per session (1,458 once invoked), scanned A, original, MIT.

A construction equipment tracker manages machines and other site equipment, including their status, assignments, use, and maintenance records.

In plain words
What is it for?
Use it to assign cranes, excavators, loaders, generators, and similar equipment to projects, track utilization, and manage maintenance.
Why use it?
It reduces confusion about where equipment is, whether it is available, and when it needs servicing or repair.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to assign cranes, excavators, loaders, generators, and similar equipment to projects, track utilization, and manage maintenance.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/equipment-fleet-manager
Install

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.

Any agent
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill equipment-fleet-manager
Clone the repo
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for equipment-fleet-manager

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/equipment-fleet-manager/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/equipment-fleet-manager)
Your own site
<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.

agentmods 80×15 button for equipment-fleet-manager

Your own site · 80×15
<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>
Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,458 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00018 $0.01458
Opus 5.5 $0.00007 $0.00583
Sonnet 5.5 $0.00004 $0.00292
Haiku 4.5 $0.00002 $0.00146

Measured 1mo ago against content hash 92fc910194d1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

1_DDC_Toolkit/Resource-Management/equipment-fleet-manager/SKILL.md · 196 lines

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)

Read the full file on GitHub · 196 lines

Files

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.

Changes

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.

  1. 1mo ago First seen · 196 lines · 18 tokens per session scan A 92fc910194d1

Subscribe to this mod's changes

equipment-fleet-manager is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (374 stars, last pushed 8d ago), licensed MIT. It adds 18 tokens to every session and 1,458 once invoked, about $0.0001 per session on Opus 5.5. 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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