labor-allocation

labor-allocation is a skill for Claude Code, Codex from jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction. It costs 24 tokens per session (2,923 once invoked), scanned A, a copy of labor-allocation, MIT.

A workforce planning tool for assigning construction workers to activities and tracking their status, skills, trade, and attendance.

In plain words
What is it for?
Use it to assign crews by trade and skill level, track attendance and leave, and monitor labor distribution across project activities.
Why use it?
It helps prevent uneven workloads, missing assignments, and poor visibility into who is available or already committed.

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 crews by trade and skill level, track attendance and leave, and monitor labor distribution across project activities.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/labor-allocation
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 jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction --skill labor-allocation
Clone the repo
git clone --depth 1 https://github.com/jdmorag97-rgb/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 labor-allocation

README.md
[![agentmods](https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/labor-allocation/github.svg)](https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/labor-allocation)
Your own site
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/labor-allocation"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/labor-allocation/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 labor-allocation

Your own site · 80×15
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/labor-allocation"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/labor-allocation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,923 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.
Origin 100% copy Near-identical to another mod 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 $0.00024 $0.02923
Opus 5 $0.00012 $0.01461
Sonnet 5 $0.00005 $0.00585
Haiku 4.5 $0.00002 $0.00292

Measured 9d ago against content hash db7224e16b17, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

labor-allocation 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 9d 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

This is a copy

100% identical to labor-allocation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

1_DDC_Toolkit/Resource-Management/labor-allocation/SKILL.md · 443 lines

How it starts

The opening of the file, as written. The whole thing — 443 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Labor Allocation Manager

Business Case

Problem Statement

Labor management challenges:

  • Assigning workers to activities
  • Balancing workload
  • Tracking attendance
  • Optimizing productivity

Solution

Systematic labor allocation and tracking to optimize resource utilization and maintain project schedule.

Technical Implementation

import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import date, timedelta
from enum import Enum
from collections import defaultdict


class Trade(Enum):
    CARPENTER = "carpenter"
    ELECTRICIAN = "electrician"
    PLUMBER = "plumber"
    CONCRETE = "concrete"
    MASON = "mason"
    IRONWORKER = "ironworker"
    HVAC = "hvac"
    PAINTER = "painter"
    LABORER = "laborer"
    OPERATOR = "operator"
    FOREMAN = "foreman"


class WorkerStatus(Enum):
    AVAILABLE = "available"
    ASSIGNED = "assigned"
    ON_LEAVE = "on_leave"
    SICK = "sick"
    TERMINATED = "terminated"


class SkillLevel(Enum):
    APPRENTICE = "apprentice"
    JOURNEYMAN = "journeyman"
    MASTER = "master"


@dataclass
class Worker:
    worker_id: str
    name: str
    trade: Trade
    skill_level: SkillLevel
    hourly_rate: float
    company: str
    status: WorkerStatus = WorkerStatus.AVAILABLE
    certifications: List[str] = field(default_factory=list)


@dataclass
class Assignment:
    assignment_id: str
    worker_id: str
    activity_id: str
    activity_name: str
    start_date: date
    end_date: date
    hours_per_day: float
    location: str


@dataclass
class AttendanceRecord:
    date: date
    worker_id: str
    activity_id: str
    hours_worked: float
    overtime_hours: float
    status: str  # present, absent, late


class LaborAllocation:
    """Manage labor allocation and tracking."""

    def __init__(self, project_name: str):
        self.project_name = project_name
        self.workers: Dict[str, Worker] = {}
        self.assignments: List[Assignment] = []
        self.attendance: List[AttendanceRecord] = []

    def add_worker(self,
                   worker_id: str,
                   name: str,
                   trade: Trade,
                   skill_level: SkillLevel,
                   hourly_rate: float,
                   company: str,
                   certifications: List[str] = None) -> Worker:
        """Add worker to pool."""

        worker = Worker(
            worker_id=worker_id,
            name=name,
            trade=trade,
            skill_level=skill_level,
            hourly_rate=hourly_rate,
            company=company,
            certifications=certifications or []
        )

        self.workers[worker_id] = worker
        return worker

    def assign_worker(self,
                      worker_id: str,
                      activity_id: str,
                      activity_name: str,
                      start_date: date,
                      end_date: date,
                      hours_per_day: float = 8,
                      location: str = "") -> Optional[Assignment]:
        """Assign worker to activity."""

        if worker_id not in self.workers:
            return None

        worker = self.workers[worker_id]

        # Check for conflicts
        conflicts = self.check_conflicts(worker_id, start_date, end_date)
        if conflicts:
            print(f"Warning: Worker has {len(conflicts)} conflicting assignments")

        assignment = Assignment(
            assignment_id=f"ASN-{len(self.assignments)+1:04d}",
            worker_id=worker_id,
            activity_id=activity_id,
            activity_name=activity_name,
            start_date=start_date,
            end_date=end_date,
            hours_per_day=hours_per_day,
            location=location
        )

        self.assignments.append(assignment)
        worker.status = WorkerStatus.ASSIGNED

        return assignment

    def check_conflicts(self,
                        worker_id: str,
                        start_date: date,
                        end_date: date) -> List[Assignment]:
        """Check for scheduling conflicts."""

        conflicts = []

        for assignment in self.assignments:
            if assignment.worker_id != worker_id:
                continue

            # Check overlap
            if not (end_date < assignment.start_date or start_date > assignment.end_date):
                conflicts.append(assignment)

        return conflicts

    def record_attendance(self,
                          date: date,
                          worker_id: str,
                          activity_id: str,
                          hours_worked: float,
                          overtime_hours: float = 0,
                          status: str = "present"):
        """Record worker attendance."""

        self.attendance.append(AttendanceRecord(
            date=date,
            worker_id=worker_id,
            activity_id=activity_id,
            hours_worked=hours_worked,
            overtime_hours=overtime_hours,
            status=status
        ))

    def get_workers_by_trade(self, trade: Trade) -> List[Worker]:
        """Get available workers by trade."""
        return [
            w for w in self.workers.values()
            if w.trade == trade and w.status in [WorkerStatus.AVAILABLE, WorkerStatus.ASSIGNED]
        ]

    def get_daily_roster(self, target_date: date) -> pd.DataFrame:
        """Get roster for specific date."""

        roster = []

        for assignment in self.assignments:
            if assignment.start_date <= target_date <= assignment.end_date:
                worker = self.workers.get(assignment.worker_id)
                if worker:
                    roster.append({
                        'Worker ID': worker.worker_id,
                        'Name': worker.name,
                        'Trade': worker.trade.value,
                        'Company': worker.company,
                        'Activity': assignment.activity_name,
                        'Location': assignment.location,
                        'Hours': assignment.hours_per_day
                    })

        return pd.DataFrame(roster)

    def get_activity_crew(self, activity_id: str) -> List[Dict[str, Any]]:
        """Get crew assigned to activity."""

        crew = []

        for assignment in self.assignments:
            if assignment.activity_id == activity_id:
                worker = self.workers.get(assignment.worker_id)
                if worker:
                    crew.append({
                        'worker_id': worker.worker_id,
                        'name': worker.name,
                        'trade': worker.trade.value,
                        'skill_level': worker.skill_level.value,
                        'hourly_rate': worker.hourly_rate,
                        'start_date': assignment.start_date,
                        'end_date': assignment.end_date
                    })

        return crew

    def calculate_labor_cost(self,
                              activity_id: str = None,
                              start_date: date = None,
                              end_date: date = None) -> Dict[str, Any]:
        """Calculate labor costs."""

        total_hours = 0
        total_overtime = 0
        total_cost = 0
        by_trade = defaultdict(float)

        for record in self.attendance:
            # Filter by activity
            if activity_id and record.activity_id != activity_id:
                continue

            # Filter by date
            if start_date and record.date < start_date:
                continue
            if end_date and record.date > end_date:
                continue

            worker = self.workers.get(record.worker_id)
            if not worker:
                continue

            regular_cost = record.hours_worked * worker.hourly_rate
            overtime_cost = record.overtime_hours * worker.hourly_rate * 1.5

            total_hours += record.hours_worked
            total_overtime += record.overtime_hours
            total_cost += regular_cost + overtime_cost
            by_trade[worker.trade.value] += regular_cost + overtime_cost

        return {
            'total_hours': round(total_hours, 1),
            'total_overtime': round(total_overtime, 1),
            'total_cost': round(total_cost, 2),
            'by_trade': dict(by_trade)
        }

    def get_utilization_report(self,
                                start_date: date,
                                end_date: date) -> pd.DataFrame:
        """Get worker utilization report."""

        data = []
        work_days = (end_date - start_date).days + 1
        available_hours = work_days * 8

        for worker in self.workers.values():
            # Get attendance records
            records = [
                r for r in self.attendance
                if r.worker_id == worker.worker_id
                and start_date <= r.date <= end_date
            ]

            worked_hours = sum(r.hours_worked + r.overtime_hours for r in records)
            utilization = (worked_hours / available_hours * 100) if available_hours > 0 else 0

            data.append({
                'Worker ID': worker.worker_id,
                'Name': worker.name,
                'Trade': worker.trade.value,
                'Available Hours': available_hours,
                'Worked Hours': round(worked_hours, 1),
                'Utilization %': round(utilization, 1)
            })

        return pd.DataFrame(data).sort_values('Utilization %', ascending=False)

    def forecast_labor_needs(self,
                              activities: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Forecast labor needs for activities."""

        needs = defaultdict(lambda: {'hours': 0, 'workers': 0})

        for activity in activities:
            trade = activity.get('trade', 'laborer')
            hours = activity.get('manhours', 0)
            duration = activity.get('duration_days', 1)

            workers_needed = hours / (duration * 8) if duration > 0 else 0

            needs[trade]['hours'] += hours
            needs[trade]['workers'] = max(needs[trade]['workers'], int(workers_needed) + 1)

        # Check availability
        for trade_name, requirement in needs.items():
            try:
                trade = Trade(trade_name)
                available = len(self.get_workers_by_trade(trade))
                requirement['available'] = available
                requirement['shortage'] = max(0, requirement['workers'] - available)
            except ValueError:
                requirement['available'] = 0
                requirement['shortage'] = requirement['workers']

        return dict(needs)

    def export_to_excel(self, output_path: str) -> str:
        """Export labor data to Excel."""

        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Workers
            workers_df = pd.DataFrame([
                {
                    'ID': w.worker_id,
                    'Name': w.name,
                    'Trade': w.trade.value,
                    'Skill': w.skill_level.value,
                    'Rate': w.hourly_rate,
                    'Company': w.company,
                    'Status': w.status.value
                }
                for w in self.workers.values()
            ])
            workers_df.to_excel(writer, sheet_name='Workers', index=False)

            # Assignments
            assignments_df = pd.DataFrame([
                {
                    'ID': a.assignment_id,
                    'Worker': a.worker_id,
                    'Activity': a.activity_name,
                    'Start': a.start_date,
                    'End': a.end_date,
                    'Hours/Day': a.hours_per_day,
                    'Location': a.location
                }
                for a in self.assignments
            ])
            assignments_df.to_excel(writer, sheet_name='Assignments', index=False)

            # Roster for today
            roster = self.get_daily_roster(date.today())
            roster.to_excel(writer, sheet_name='Today Roster', index=False)

        return output_path

Read the full file on GitHub · 443 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. 9d ago First seen · 443 lines · 24 tokens per session scan A db7224e16b17

Subscribe to this mod's changes

labor-allocation is a skill published in the GitHub repository jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction (2 stars, last pushed 6mo ago), licensed MIT. It adds 24 tokens to every session and 2,923 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to labor-allocation, differing in 0 lines, and is treated as a copy.

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