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-allocationInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
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.
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.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| labor-allocation (this skill)by datadrivenconstruction | 91 | 333 | 56d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 98.3k | 2d ago | CLAUDE.md |
| taste-skillby Leonxlnx | 100 | 98.1k | 2d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 75.5k | today | CLAUDE.md |
| ai-job-searchby MadsLorentzen | 100 | 48.8k | 1d ago | CLAUDE.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.
Skill content
View source on GitHubname: "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.
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