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labor-allocation

Allocate and track labor resources across project activities. Balance workload, track attendance, and optimize crew assignments.

Install / Use

npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill labor-allocation

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

91/100

Supported Platforms

Universal

Our assessment of labor-allocation

labor-allocation scores 91/100 on our quality scale, 1297th of 4,601 Development & Engineering skills we index (top 29%).

Its SKILL.md is 14 KB long, well organised into 15 sections with 5 code examples: a thorough specification that gives an agent plenty to work with.

It has 333 GitHub stars, a meaningful sign that others use it.

Substance
30/30
Structure
20/20
Description
15/15
Adoption
11/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated about 2 months ago, so labor-allocation is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

labor-allocation compared with similar skills

All 4 of these similar skills score higher than labor-allocation; compare them before choosing.

SkillScoreStarsUpdatedFormat
labor-allocation (this skill)by datadrivenconstruction9133356d agoSKILL.md
Agent-Reachby Panniantong10098.3k2d agoCLAUDE.md
taste-skillby Leonxlnx10098.1k2d agoCLAUDE.md
headroomby headroomlabs-ai10075.5ktodayCLAUDE.md
ai-job-searchby MadsLorentzen10048.8k1d agoCLAUDE.md

Frequently asked questions

How do I install labor-allocation?
Run npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill labor-allocation. The install tabs above show the steps for each supported agent.
Which AI agents does labor-allocation work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is labor-allocation safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is labor-allocation still maintained?
The repository was last updated about 2 months ago, so labor-allocation is actively maintained.

name: "labor-allocation" description: "Allocate and track labor resources across project activities. Balance workload, track attendance, and optimize crew assignments." homepage: "https://datadrivenconstruction.io" metadata: {"openclaw": {"emoji": "👷", "os": ["darwin", "linux", "win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}}

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,
    

Truncated for display — read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars333
CategoryDevelopment
Updated1mo ago
Forks82

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions
labor-allocation — Universal Skill: Install & Safety Check | SkillAgent