
daily-report-generator
Automatically generate daily construction reports from field data, worker inputs, weather, and progress photos. Creates professional PDF reports.
- Installs
- 114
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
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-report-generator- 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-report-generator 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 Report Generator for Construction Sites
Automate the creation of comprehensive daily construction reports by aggregating data from multiple sources into professional documentation.
Business Case
Problem: Site managers spend 45-60 minutes daily on:
- Collecting information from foremen
- Checking weather conditions
- Compiling worker counts and hours
- Writing narrative summaries
- Formatting and distributing reports
Solution: Automated system that:
- Pulls data from Google Sheets/project database
- Integrates weather API data
- Aggregates worker timesheets
- Generates professional PDF reports
- Distributes to stakeholders automatically
ROI: 80% reduction in daily reporting time (45 min → 9 min for review)
Report Structure
┌──────────────────────────────────────────────────────────────────────┐
│ DAILY CONSTRUCTION REPORT │
│ │
│ Project: ЖК Солнечный, Корпус 2 Date: 24.01.2026 │
│ Report #: DCR-2026-024 Weather: ☁️ -5°C │
├──────────────────────────────────────────────────────────────────────┤
│ │
│ 1. WEATHER CONDITIONS │
│ ┌────────────┬────────────┬────────────┬────────────┐ │
│ │ Morning │ Afternoon │ Evening │ Impact │ │
│ │ -8°C ☀️ │ -5°C ☁️ │ -7°C 🌙 │ Normal │ │
│ └────────────┴────────────┴────────────┴────────────┘ │
│ │
│ 2. WORKFORCE │
│ ┌────────────────────────────────────────────────────┐ │
│ │ Category │ Planned │ Actual │ Hours │ │
│ ├────────────────────────────────────────────────────┤ │
│ │ GC Supervision │ 3 │ 3 │ 27 │ │
│ │ Electrical │ 12 │ 11 │ 88 │ │
│ │ Plumbing │ 8 │ 8 │ 64 │ │
│ │ HVAC │ 6 │ 6 │ 48 │ │
│ │ TOTAL │ 29 │ 28 │ 227 │ │
│ └────────────────────────────────────────────────────┘ │
│ │
│ 3. WORK COMPLETED TODAY │
│ • Electrical: Completed rough-in floors 5-6 │
│ • Plumbing: Installed risers section A │
│ • HVAC: Ductwork installation 60% complete │
│ │
│ 4. WORK PLANNED FOR TOMORROW │
│ • Electrical: Begin rough-in floor 7 │
│ • Plumbing: Continue risers section B │
│ • HVAC: Complete ductwork, begin testing │
│ │
│ 5. ISSUES / DELAYS │
│ • Material delay: Electrical panels (ETA: 26.01) │
│ • Weather: Expected snow may delay exterior work │
│ │
│ 6. SAFETY │
│ ✅ No incidents │
│ ✅ Toolbox talk completed: Fall protection │
│ │
│ 7. PHOTOS │
│ [Photo 1: Floor 5 electrical] [Photo 2: Riser installation] │
│ │
│ ───────────────────────────────────────────────────────────────── │
│ Prepared by: Иван Петров, Site Manager │
│ Approved by: ___________________ │
│ Distribution: Owner, Architect, PM │
└──────────────────────────────────────────────────────────────────────┘
Python Implementation
import pandas as pd
from datetime import datetime, date
from typing import Optional, List, Dict
import requests
from reportlab.lib import colors
from reportlab.lib.pagesizes import A4
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, Spacer, Image
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
import os
class DailyReportGenerator:
"""Generate professional daily construction reports"""
def __init__(self, config: dict):
self.config = config
self.weather_api_key = config.get('weather_api_key')
self.project_name = config.get('project_name')
self.report_date = config.get('report_date', date.today())
def get_weather_data(self, location: str) -> dict:
"""Fetch weather data from API"""
if not self.weather_api_key:
return self._mock_weather()
url = f"https://api.openweathermap.org/data/2.5/weather"
params = {
'q': location,
'appid': self.weather_api_key,
'units': 'metric',
'lang': 'ru'
}
response = requests.get(url, params=params)
if response.status_code == 200:
data = response.json()
return {
'temp': round(data['main']['temp']),
'description': data['weather'][0]['description'],
'humidity': data['main']['humidity'],
'wind_speed': round(data['wind']['speed']),
'icon': self._get_weather_icon(data['weather'][0]['main'])
}
return self._mock_weather()
def _get_weather_icon(self, condition: str) -> str:
icons = {
'Clear': '☀️',
'Clouds': '☁️',
'Rain': '🌧️',
'Snow': '❄️',
'Thunderstorm': '⛈️',
'Mist': '🌫️'
}
return icons.get(condition, '🌤️')
def _mock_weather(self) -> dict:
return {
'temp': -5,
'description': 'облачно',
'humidity': 65,
'wind_speed': 3,
'icon': '☁️'
}
def get_workforce_data(self, source: pd.DataFrame) -> dict:
"""Aggregate workforce data from timesheet"""
# Expected columns: trade, worker_name, hours_worked, planned_hours
summary = source.groupby('trade').agg({
'worker_name': 'count',
'hours_worked': 'sum',
'planned_hours': 'sum'
}).reset_index()
summary.columns = ['trade', 'actual_count', 'actual_hours', 'planned_hours']
# Calculate planned count (assuming 8-hour shifts)
summary['planned_count'] = (summary['planned_hours'] / 8).astype(int)
return {
'trades': summary.to_dict('records'),
'total_workers': summary['actual_count'].sum(),
'total_hours': summary['actual_hours'].sum(),
'total_planned': summary['planned_count'].sum()
}
def get_work_completed(self, tasks: pd.DataFrame) -> List[dict]:
"""Extract completed work from task system"""
# Filter completed tasks for today
completed = tasks[
(tasks['date'] == self.report_date.strftime('%d.%m.%Y')) &
(tasks['status'].isin(['Completed', 'Partial']))
]
work_items = []
for _, row in completed.iterrows():
work_items.append({
'trade': row['trade'],
'description': row['description'],
'status': row['status'],
'notes': row.get('notes', '')
})
return work_items
def get_work_planned(self, tasks: pd.DataFrame) -> List[dict]:
"""Get planned work for tomorrow"""
tomorrow = self.report_date + pd.Timedelta(days=1)
planned = tasks[
tasks['date'] == tomorrow.strftime('%d.%m.%Y')
]
work_items = []
for _, row in planned.iterrows():
work_items.append({
'trade': row['trade'],
'description': row['description'],
'priority': row.get('priority', 'Medium')
})
return work_items
def get_issues(self, issues_log: pd.DataFrame) -> List[dict]:
"""Get active issues and delays"""
active = issues_log[
(issues_log['status'] == 'Open') |
(issues_log['date_reported'] == self.report_date.strftime('%d.%m.%Y'))
]
return active[['category', 'description', 'impact', 'resolution_date']].to_dict('records')
def get_safety_data(self, safety_log: pd.DataFrame) -> dict:
"""Get safety information for the day"""
today_incidents = safety_log[
safety_log['date'] == self.report_date.strftime('%d.%m.%Y')
]
return {
'incidents': len(today_incidents[today_incidents['type'] == 'Incident']),
'near_misses': len(today_incidents[today_incidents['type'] == 'Near Miss']),
'toolbox_talk': today_incidents[
today_incidents['type'] == 'Toolbox Talk'
]['topic'].tolist(),
'observations': today_incidents[
today_incidents['type'] == 'Observation'
]['description'].tolist()
}
def generate_report(self, data: dict, output_path: str) -> str:
"""Generate PDF report"""
doc = SimpleDocTemplate(
output_path,
pagesize=A4,
rightMargin=2*cm,
leftMargin=2*cm,
topMargin=2*cm,
bottomMargin=2*cm
)
styles = getSampleStyleSheet()
title_style = ParagraphStyle(
'Title',
parent=styles['Heading1'],
fontSize=16,
alignment=1,
spaceAfter=12
)
heading_style = ParagraphStyle(
'Heading',
parent=styles['Heading2'],
fontSize=12,
spaceBefore=12,
spaceAfter=6
)
elements = []
# Title
elements.append(Paragraph(
f"DAILY CONSTRUCTION REPORT",
title_style
))
# Header info
header_data = [
['Project:', self.project_name, 'Date:', self.report_date.strftime('%d.%m.%Y')],
['Report #:', data.get('report_number', 'DCR-001'), 'Weather:', f"{data['weather']['icon']} {data['weather']['temp']}°C"]
]
header_table = Table(header_data, colWidths=[3*cm, 6*cm, 3*cm, 4*cm])
header_table.setStyle(TableStyle([
('FONTNAME', (0, 0), (-1, -1), 'Helvetica'),
('FONTSIZE', (0, 0), (-1, -1), 10),
('FONTNAME', (0, 0), (0, -1), 'Helvetica-Bold'),
('FONTNAME', (2, 0), (2, -1), 'Helvetica-Bold'),
]))
elements.append(header_table)
elements.append(Spacer(1, 12))
# Weather section
elements.append(Paragraph("1. WEATHER CONDITIONS", heading_style))
weather = data['weather']
weather_text = f"""
Temperature: {weather['temp']}°C | Humidity: {weather['humidity']}% |
Wind: {weather['wind_speed']} m/s | Conditions: {weather['description']}
"""
elements.append(Paragraph(weather_text, styles['Normal']))
# Workforce section
elements.append(Paragraph("2. WORKFORCE", heading_style))
workforce = data['workforce']
workforce_data = [['Trade', 'Planned', 'Actual', 'Hours']]
for trade in workforce['trades']:
workforce_data.append([
trade['trade'],
str(trade['planned_count']),
str(trade['actual_count']),
str(int(trade['actual_hours']))
])
workforce_data.append([
'TOTAL',
str(workforce['total_planned']),
str(workforce['total_workers']),
str(int(workforce['total_hours']))
])
workforce_table = Table(workforce_data, colWidths=[6*cm, 3*cm, 3*cm, 3*cm])
workforce_table.setStyle(TableStyle([
('BACKGROUND', (0, 0), (-1, 0), colors.grey),
('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
('FONTNAME', (0, -1), (-1, -1), 'Helvetica-Bold'),
('GRID', (0, 0), (-1, -1), 1, colors.black),
('ALIGN', (1, 0), (-1, -1), 'CENTER'),
]))
elements.append(workforce_table)
# Work completed
elements.append(Paragraph("3. WORK COMPLETED TODAY", heading_style))
for item in data.get('work_completed', []):
bullet = f"• {item['trade']}: {item['description']}"
if item.get('notes'):
bullet += f" ({item['notes']})"
elements.append(Paragraph(bullet, styles['Normal']))
# Work planned
elements.append(Paragraph("4. WORK PLANNED FOR TOMORROW", heading_style))
for item in data.get('work_planned', []):
bullet = f"• {item['trade']}: {item['description']}"
elements.append(Paragraph(bullet, styles['Normal']))
# Issues
elements.append(Paragraph("5. ISSUES / DELAYS", heading_style))
issues = data.get('issues', [])
if issues:
for issue in issues:
bullet = f"• {issue['category']}: {issue['description']}"
if issue.get('resolution_date'):
bullet += f" (ETA: {issue['resolution_date']})"
elements.append(Paragraph(bullet, styles['Normal']))
else:
elements.append(Paragraph("No significant issues reported.", styles['Normal']))
# Safety
elements.append(Paragraph("6. SAFETY", heading_style))
safety = data.get('safety', {})
if safety.get('incidents', 0) == 0:
elements.append(Paragraph("✅ No incidents reported", styles['Normal']))
else:
elements.append(Paragraph(f"⚠️ {safety['incidents']} incident(s) reported", styles['Normal']))
if safety.get('toolbox_talk'):
elements.append(Paragraph(f"✅ Toolbox talk: {', '.join(safety['toolbox_talk'])}", styles['Normal']))
# Signature block
elements.append(Spacer(1, 24))
elements.append(Paragraph("─" * 60, styles['Normal']))
elements.append(Paragraph(f"Prepared by: {data.get('prepared_by', '_________________')}", styles['Normal']))
elements.append(Paragraph(f"Date: {datetime.now().strftime('%d.%m.%Y %H:%M')}", styles['Normal']))
# Build PDF
doc.build(elements)
return output_path
# Usage Example
def generate_daily_report(
project_name: str,
location: str,
timesheet_path: str,
tasks_path: str,
output_dir: str
) -> str:
"""Generate daily report from source files"""
# Initialize generator
generator = DailyReportGenerator({
'project_name': project_name,
'weather_api_key': os.environ.get('WEATHER_API_KEY'),
'report_date': date.today()
})
# Load data
timesheet = pd.read_excel(timesheet_path)
tasks = pd.read_excel(tasks_path)
# Compile report data
report_data = {
'report_number': f"DCR-{date.today().strftime('%Y-%j')}",
'weather': generator.get_weather_data(location),
'workforce': generator.get_workforce_data(timesheet),
'work_completed': generator.get_work_completed(tasks),
'work_planned': generator.get_work_planned(tasks),
'issues': [], # Load from issues log if available
'safety': {
'incidents': 0,
'toolbox_talk': ['Fall Protection'],
'near_misses': 0
},
'prepared_by': 'Site Manager'
}
# Generate PDF
output_path = os.path.join(
output_dir,
f"Daily_Report_{date.today().strftime('%Y%m%d')}.pdf"
)
return generator.generate_report(report_data, output_path)
if __name__ == "__main__":
report_path = generate_daily_report(
project_name="ЖК Солнечный, Корпус 2",
location="Moscow,RU",
timesheet_path="timesheet.xlsx",
tasks_path="tasks.xlsx",
output_dir="./reports"
)
print(f"Report generated: {report_path}")
Data Sources Integration
From n8n Project Management System
# Connect to Google Sheets used by n8n bot
def get_data_from_project_management(spreadsheet_id: str) -> dict:
"""Pull data from n8n project management system"""
import gspread
gc = gspread.service_account()
sh = gc.open_by_key(spreadsheet_id)
# Get completed tasks
tasks_sheet = sh.worksheet('Tasks')
tasks = pd.DataFrame(tasks_sheet.get_all_records())
# Get workforce from worker responses
workers_sheet = sh.worksheet('Workers')
workers = pd.DataFrame(workers_sheet.get_all_records())
return {
'tasks': tasks,
'workers': workers
}
From Timesheet System
# Integrate with common timesheet formats
def import_timesheet(source: str, format: str = 'excel') -> pd.DataFrame:
"""Import timesheet data from various sources"""
if format == 'excel':
df = pd.read_excel(source)
elif format == 'csv':
df = pd.read_csv(source)
elif format == 'procore':
df = fetch_procore_timesheet(source)
# Standardize columns
df = df.rename(columns={
'Trade': 'trade',
'Worker': 'worker_name',
'Hours': 'hours_worked',
'Planned Hours': 'planned_hours'
})
return df
n8n Workflow for Automation
name: Daily Report Automation
trigger:
type: cron
expression: "0 18 * * 1-6" # 6 PM daily
steps:
- collect_task_data:
node: Google Sheets
operation: readRows
sheet: Tasks
filter: Date = TODAY()
- collect_timesheet:
node: Google Sheets
operation: readRows
sheet: Timesheet
filter: Date = TODAY()
- get_weather:
node: HTTP Request
url: "https://api.openweathermap.org/data/2.5/weather"
params:
q: "Moscow,RU"
appid: "{{$env.WEATHER_API_KEY}}"
- generate_report:
node: Code (Python)
code: |
from daily_report import generate_report
return generate_report(items)
- upload_to_drive:
node: Google Drive
operation: upload
file: "={{$json.report_path}}"
folder: "Daily Reports"
- send_notification:
node: Telegram
operation: sendDocument
chatId: "MANAGERS_GROUP_ID"
document: "={{$json.drive_url}}"
caption: |
📋 Daily Report - {{$now.format('DD.MM.YYYY')}}
Workforce: {{$json.total_workers}} workers
Tasks completed: {{$json.completed_tasks}}
Issues: {{$json.open_issues}}
Report Distribution
def distribute_report(report_path: str, recipients: dict):
"""Distribute report to stakeholders"""
# Email distribution
for email in recipients.get('email', []):
send_email(
to=email,
subject=f"Daily Report - {date.today().strftime('%d.%m.%Y')}",
body="Please find attached the daily construction report.",
attachment=report_path
)
# Telegram distribution
for chat_id in recipients.get('telegram', []):
send_telegram_document(
chat_id=chat_id,
document_path=report_path,
caption=f"📋 Daily Report - {date.today().strftime('%d.%m.%Y')}"
)
# Upload to project portal
if portal_url := recipients.get('portal'):
upload_to_portal(portal_url, report_path)
Best Practices
- Data Collection: Set up automated data collection to minimize manual input
- Review Time: Allow 5-10 minutes for manager review before distribution
- Photos: Include 3-5 key photos showing progress
- Issues: Be specific about impacts and resolution dates
- Distribution: Send by 6-7 PM to allow stakeholder review
"A good daily report tells the story of the day in 2 minutes or less."
FAQ
What does the daily-report-generator skill do?
Automatically generate daily construction reports from field data, worker inputs, weather, and progress photos. Creates professional PDF reports.
How do I install daily-report-generator?
Run npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill daily-report-generator 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-report-generator/ (personal) or .claude/skills/daily-report-generator/ (project).
Which AI agents can use daily-report-generator?
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-report-generator 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-report-generator?
It is published by datadrivenconstruction in datadrivenconstruction/ddc_skills_for_ai_agents_in_construction. It has 114 recorded installs.
More from datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
warranty-tracker
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Track and manage construction warranties. Monitor expiration dates, claims, and manufacturer documentation.
cad-to-data
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Convert CAD/BIM files to structured data. Extract element data from Revit, IFC, DWG, DGN files.
dwg-to-excel
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Open the skill to read its SKILL.md instructions.
drawing-analyzer
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Open the skill to read its SKILL.md instructions.
permit-tracking-automation
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Automate construction permit tracking and management. Monitor application status, track renewal deadlines, manage document requirements, and integrate with municipal systems.
cost-estimation-resource
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Calculate construction costs using resource-based method. Estimate project costs from work items, physical resource norms, and current prices.
Popular docs & writing skills
grill-with-docs
mattpocock/skills
A relentless interview to sharpen a plan or design, which also creates docs (ADR's and glossary) as we go.
lark-markdown
open.feishu.cn
飞书 Markdown:查看、创建、上传、编辑和比较飞书中的原生 Markdown 文件。当用户要操作飞书 Markdown 文件,或比较其远端版本及本地草稿时使用。纯本地 Markdown 文件操作不触发本 skill。不负责将 Markdown 导入为飞书在线文档,也不负责文件搜索、权限、评论、移动、删除等云空间管理操作。
writing-beats
mattpocock/skills
Writing, exploit; assemble raw material into a journey of beats, grounding each term before a beat leans on it.
writing-shape
mattpocock/skills
Writing, exploit: shape raw material into an article, paragraph by paragraph.
writing-fragments
mattpocock/skills
Writing, explore: mine raw fragments, no structure yet.
writing-for-agents
mattpocock/skills
Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.