本文へ移動
cccskills
無料GitHub で公開

daily-progress-report

Generate automated daily progress reports from site data. Track work completed, labor hours, equipment usage, and weather conditions.

インストール方法を見る

含まれるファイル(3)

  • SKILL.md11.5 KB
  • claw.json516 B
  • instructions.md1.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3472026年8月22日 更新

Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3472026年8月22日 更新

Check construction AI systems against the EU AI Act (Regulation 2024/1689): classify risk of estimation, scheduling, CV and agent tools, document transparency, keep human oversight. Use when deploying or auditing AI in construction.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3472026年8月22日 更新

Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3472026年8月22日 更新

Create Apache Airflow DAGs for construction data pipelines. Orchestrate ETL, validation, and reporting workflows.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3472026年8月22日 更新

Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3472026年8月22日 更新

datadrivenconstruction のスキルをすべて見る

このスキルの問題を報告する