
daily-progress-report
Generate automated daily progress reports from site data. Track work completed, labor hours, equipment usage, and weather conditions.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill daily-progress-report- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads daily-progress-report automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Daily Progress Report Generator
Business Case
Problem Statement
Site managers spend hours creating daily reports:
- Manual data collection
- Inconsistent formats
- Delayed submissions
- Missing information
Solution
Automated daily progress report generation from structured site data inputs.
Technical Implementation
import pandas as pd
from datetime import datetime, date
from typing import Dict, Any, List
from dataclasses import dataclass
from enum import Enum
class WeatherCondition(Enum):
CLEAR = "clear"
CLOUDY = "cloudy"
RAIN = "rain"
SNOW = "snow"
WIND = "wind"
EXTREME = "extreme"
class WorkStatus(Enum):
COMPLETED = "completed"
IN_PROGRESS = "in_progress"
DELAYED = "delayed"
NOT_STARTED = "not_started"
@dataclass
class WorkActivity:
activity_id: str
description: str
location: str
planned_qty: float
actual_qty: float
unit: str
status: WorkStatus
crew_size: int
hours_worked: float
notes: str = ""
@dataclass
class LaborEntry:
trade: str
company: str
workers: int
hours: float
overtime_hours: float = 0
@dataclass
class EquipmentEntry:
equipment_type: str
equipment_id: str
hours_used: float
status: str # active, idle, maintenance
operator: str = ""
@dataclass
class DailyReport:
report_date: date
project_name: str
project_number: str
weather: WeatherCondition
temperature_high: float
temperature_low: float
work_activities: List[WorkActivity]
labor: List[LaborEntry]
equipment: List[EquipmentEntry]
delays: List[str]
safety_incidents: int
visitors: List[str]
deliveries: List[str]
prepared_by: str
class DailyProgressReporter:
"""Generate daily progress reports."""
def __init__(self, project_name: str, project_number: str):
self.project_name = project_name
self.project_number = project_number
def create_report(self,
report_date: date,
weather: WeatherCondition,
temp_high: float,
temp_low: float,
prepared_by: str) -> DailyReport:
"""Create new daily report."""
return DailyReport(
report_date=report_date,
project_name=self.project_name,
project_number=self.project_number,
weather=weather,
temperature_high=temp_high,
temperature_low=temp_low,
work_activities=[],
labor=[],
equipment=[],
delays=[],
safety_incidents=0,
visitors=[],
deliveries=[],
prepared_by=prepared_by
)
def add_work_activity(self,
report: DailyReport,
activity_id: str,
description: str,
location: str,
planned_qty: float,
actual_qty: float,
unit: str,
crew_size: int,
hours_worked: float,
notes: str = ""):
"""Add work activity to report."""
# Determine status
if actual_qty >= planned_qty:
status = WorkStatus.COMPLETED
elif actual_qty > 0:
status = WorkStatus.IN_PROGRESS
elif actual_qty == 0 and planned_qty > 0:
status = WorkStatus.DELAYED
else:
status = WorkStatus.NOT_STARTED
activity = WorkActivity(
activity_id=activity_id,
description=description,
location=location,
planned_qty=planned_qty,
actual_qty=actual_qty,
unit=unit,
status=status,
crew_size=crew_size,
hours_worked=hours_worked,
notes=notes
)
report.work_activities.append(activity)
def add_labor(self,
report: DailyReport,
trade: str,
company: str,
workers: int,
hours: float,
overtime_hours: float = 0):
"""Add labor entry."""
report.labor.append(LaborEntry(
trade=trade,
company=company,
workers=workers,
hours=hours,
overtime_hours=overtime_hours
))
def add_equipment(self,
report: DailyReport,
equipment_type: str,
equipment_id: str,
hours_used: float,
status: str,
operator: str = ""):
"""Add equipment entry."""
report.equipment.append(EquipmentEntry(
equipment_type=equipment_type,
equipment_id=equipment_id,
hours_used=hours_used,
status=status,
operator=operator
))
def calculate_summary(self, report: DailyReport) -> Dict[str, Any]:
"""Calculate report summary metrics."""
total_workers = sum(l.workers for l in report.labor)
total_manhours = sum(l.workers * l.hours for l in report.labor)
total_overtime = sum(l.workers * l.overtime_hours for l in report.labor)
equipment_hours = sum(e.hours_used for e in report.equipment)
completed = sum(1 for a in report.work_activities if a.status == WorkStatus.COMPLETED)
in_progress = sum(1 for a in report.work_activities if a.status == WorkStatus.IN_PROGRESS)
delayed = sum(1 for a in report.work_activities if a.status == WorkStatus.DELAYED)
return {
'total_workers': total_workers,
'total_manhours': round(total_manhours, 1),
'total_overtime': round(total_overtime, 1),
'equipment_hours': round(equipment_hours, 1),
'activities_completed': completed,
'activities_in_progress': in_progress,
'activities_delayed': delayed,
'safety_incidents': report.safety_incidents,
'deliveries_count': len(report.deliveries)
}
def export_to_excel(self, report: DailyReport, output_path: str) -> str:
"""Export report to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Header
header_df = pd.DataFrame([{
'Project': report.project_name,
'Project #': report.project_number,
'Date': report.report_date,
'Weather': report.weather.value,
'High Temp': report.temperature_high,
'Low Temp': report.temperature_low,
'Prepared By': report.prepared_by
}])
header_df.to_excel(writer, sheet_name='Summary', index=False)
# Work Activities
if report.work_activities:
activities_df = pd.DataFrame([
{
'Activity ID': a.activity_id,
'Description': a.description,
'Location': a.location,
'Planned': a.planned_qty,
'Actual': a.actual_qty,
'Unit': a.unit,
'Status': a.status.value,
'Crew': a.crew_size,
'Hours': a.hours_worked,
'Notes': a.notes
}
for a in report.work_activities
])
activities_df.to_excel(writer, sheet_name='Work Activities', index=False)
# Labor
if report.labor:
labor_df = pd.DataFrame([
{
'Trade': l.trade,
'Company': l.company,
'Workers': l.workers,
'Hours': l.hours,
'Overtime': l.overtime_hours,
'Total Hours': l.workers * (l.hours + l.overtime_hours)
}
for l in report.labor
])
labor_df.to_excel(writer, sheet_name='Labor', index=False)
# Equipment
if report.equipment:
equip_df = pd.DataFrame([
{
'Type': e.equipment_type,
'ID': e.equipment_id,
'Hours': e.hours_used,
'Status': e.status,
'Operator': e.operator
}
for e in report.equipment
])
equip_df.to_excel(writer, sheet_name='Equipment', index=False)
return output_path
def generate_text_report(self, report: DailyReport) -> str:
"""Generate text version of report."""
summary = self.calculate_summary(report)
lines = [
f"DAILY PROGRESS REPORT",
f"=" * 50,
f"Project: {report.project_name}",
f"Project #: {report.project_number}",
f"Date: {report.report_date}",
f"Prepared by: {report.prepared_by}",
f"",
f"WEATHER CONDITIONS",
f"-" * 30,
f"Conditions: {report.weather.value}",
f"Temperature: {report.temperature_low}°C - {report.temperature_high}°C",
f"",
f"SUMMARY",
f"-" * 30,
f"Total Workers: {summary['total_workers']}",
f"Total Man-hours: {summary['total_manhours']}",
f"Equipment Hours: {summary['equipment_hours']}",
f"Activities Completed: {summary['activities_completed']}",
f"Activities In Progress: {summary['activities_in_progress']}",
f"Activities Delayed: {summary['activities_delayed']}",
f"Safety Incidents: {summary['safety_incidents']}",
]
if report.delays:
lines.extend([f"", f"DELAYS", f"-" * 30])
for delay in report.delays:
lines.append(f"• {delay}")
return "\n".join(lines)
Quick Start
from datetime import date
# Initialize reporter
reporter = DailyProgressReporter("Office Tower A", "PRJ-2024-001")
# Create report
report = reporter.create_report(
report_date=date.today(),
weather=WeatherCondition.CLEAR,
temp_high=28,
temp_low=18,
prepared_by="John Smith"
)
# Add activities
reporter.add_work_activity(
report,
activity_id="A-101",
description="Pour concrete slab Level 3",
location="Level 3, Zone A",
planned_qty=150,
actual_qty=150,
unit="m3",
crew_size=8,
hours_worked=10
)
# Add labor
reporter.add_labor(report, "Concrete", "ABC Concrete Co", 8, 10, 2)
# Export
reporter.export_to_excel(report, "daily_report.xlsx")
Common Use Cases
1. Generate Text Summary
text = reporter.generate_text_report(report)
print(text)
2. Track Delays
report.delays.append("Weather delay - rain from 14:00-16:00")
report.delays.append("Material delivery late by 2 hours")
3. Calculate Metrics
summary = reporter.calculate_summary(report)
print(f"Productivity: {summary['total_manhours']} man-hours")
Resources
- DDC Book: Chapter 4.1 - Site Data Collection
FAQ
What does the daily-progress-report skill do?
Generate automated daily progress reports from site data. Track work completed, labor hours, equipment usage, and weather conditions.
How do I install daily-progress-report?
Run npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill daily-progress-report in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/daily-progress-report/ (personal) or .claude/skills/daily-progress-report/ (project).
Which AI agents can use daily-progress-report?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is daily-progress-report safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains daily-progress-report?
It is published by datadrivenconstruction in datadrivenconstruction/ddc_skills_for_ai_agents_in_construction. It has 122 recorded installs.
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