Fetch weather data for construction scheduling. Historical data, forecasts, and risk assessment for outdoor work.

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: claw.json, instructions.md, SKILL.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 2.1.0

Provenance

Publisher: datadrivenconstruction
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 6 sections Open source page

Overview

Weather impacts 50% of construction activities. This skill fetches weather data for scheduling, risk assessment, and productivity adjustments.

Python Implementation

import requests import pandas as pd from typing import Dict, Any, List, Optional from dataclasses import dataclass from datetime import datetime, timedelta from enum import Enum class WeatherRisk(Enum): """Weather risk levels for construction.""" LOW = "low" MODERATE = "moderate" HIGH = "high" CRITICAL = "critical" @dataclass class WeatherCondition: """Weather condition at a point in time.""" timestamp: datetime temperature: float # Celsius humidity: float # Percent wind_speed: float # m/s precipitation: float # mm conditions: str @dataclass class WorkabilityAssessment: """Assessment of weather workability.""" date: datetime risk_level: WeatherRisk workable_hours: int affected_activities: List[str] recommendations: List[str] class WeatherAPIClient: """Client for weather APIs.""" # Free tier endpoints OPEN_METEO_BASE = "https://api.open-meteo.com/v1" def __init__(self, api_key: Optional[str] = None): self.api_key = api_key def get_forecast(self, latitude: float, longitude: float, days: int = 7) -> List[WeatherCondition]: """Get weather forecast.""" url = f"{self.OPEN_METEO_BASE}/forecast" params = { 'latitude': latitude, 'longitude': longitude, 'hourly': 'temperature_2m,relative_humidity_2m,wind_speed_10m,precipitation', 'forecast_days': days } response = requests.get(url, params=params) if response.status_code != 200: raise Exception(f"API error: {response.status_code}") data = response.json() return self._parse_forecast(data) def get_historical(self, latitude: float, longitude: float, start_date: str, end_date: str) -> List[WeatherCondition]: """Get historical weather data.""" url = f"{self.OPEN_METEO_BASE}/archive" params = { 'latitude': latitude, 'longitude': longitude, 'start_date': start_date, 'end_date': end_date, 'hourly': 'temperature_2m,relative_humidity_2m,wind_speed_10m,precipitation' } response = requests.get(url, params=params) if response.status_code != 200: raise Exception(f"API error: {response.status_code}") data = response.json() return self._parse_forecast(data) def _parse_forecast(self, data: Dict) -> List[WeatherCondition]: """Parse API response to WeatherCondition list.""" conditions = [] hourly = data.get('hourly', {}) times = hourly.get('time', []) temps = hourly.get('temperature_2m', []) humidity = hourly.get('relative_humidity_2m', []) wind = hourly.get('wind_speed_10m', []) precip = hourly.get('precipitation', []) for i in range(len(times)): conditions.append(WeatherCondition( timestamp=datetime.fromisoformat(times[i]), temperature=temps[i] if i < len(temps) else 0, humidity=humidity[i] if i < len(humidity) else 0, wind_speed=wind[i] if i < len(wind) else 0, precipitation=precip[i] if i < len(precip) else 0, conditions=self._describe_conditions( temps[i] if i < len(temps) else 0, precip[i] if i < len(precip) else 0, wind[i] if i < len(wind) else 0 ) )) return conditions def _describe_conditions(self, temp: float, precip: float, wind: float) -> str: """Generate weather description.""" conditions = [] if temp < 0: conditions.append("Freezing") elif temp > 35: conditions.append("Extreme heat") elif temp > 30: conditions.append("Hot") elif temp < 10: conditions.append("Cold") if precip > 10: conditions.append("Heavy rain") elif precip > 2: conditions.append("Rain") elif precip > 0: conditions.append("Light rain") if wind > 15: conditions.append("Strong winds") elif wind > 10: conditions.append("Windy") return ", ".join(conditions) if conditions else "Clear" def to_dataframe(self, conditions: List[WeatherCondition]) -> pd.DataFrame: """Convert conditions to DataFrame.""" data = [{ 'timestamp': c.timestamp, 'temperature': c.temperature, 'humidity': c.humidity, 'wind_speed': c.wind_speed, 'precipitation': c.precipitation, 'conditions': c.conditions } for c in conditions] return pd.DataFrame(data) class ConstructionWeatherRisk: """Assess weather risk for construction activities.""" # Activity-specific thresholds THRESHOLDS = { 'concrete_pour': { 'min_temp': 5, 'max_temp': 35, 'max_wind': 12, 'max_precip': 0.5 }, 'crane_work': { 'min_temp': -10, 'max_temp': 40, 'max_wind': 10, 'max_precip': 5 }, 'exterior_paint': { 'min_temp': 10, 'max_temp': 35, 'max_wind': 8, 'max_precip': 0 }, 'roofing': { 'min_temp': 5, 'max_temp': 38, 'max_wind': 12, 'max_precip': 0 }, 'earthwork': { 'min_temp': -5, 'max_temp': 40, 'max_wind': 20, 'max_precip': 10 } } def assess_workability(self, condition: WeatherCondition, activities: List[str] = None) -> WorkabilityAssessment: """Assess workability for given conditions.""" if activities is None: activities = list(self.THRESHOLDS.keys()) affected = [] recommendations = [] for activity in activities: if activity in self.THRESHOLDS: thresh = self.THRESHOLDS[activity] reasons = [] if condition.temperature < thresh['min_temp']: reasons.append(f"Too cold ({condition.temperature}°C)") if condition.temperature > thresh['max_temp']: reasons.append(f"Too hot ({condition.temperature}°C)") if condition.wind_speed > thresh['max_wind']: reasons.append(f"High wind ({condition.wind_speed} m/s)") if condition.precipitation > thresh['max_precip']: reasons.append(f"Precipitation ({condition.precipitation} mm)") if reasons: affected.append(activity) recommendations.append(f"{activity}: " + ", ".join(reasons)) # Determine overall risk level if len(affected) >= len(activities) * 0.8: risk = WeatherRisk.CRITICAL workable = 0 elif len(affected) >= len(activities) * 0.5: risk = WeatherRisk.HIGH workable = 4 elif len(affected) > 0: risk = WeatherRisk.MODERATE workable = 6 else: risk = WeatherRisk.LOW workable = 8 return WorkabilityAssessment( date=condition.timestamp, risk_level=risk, workable_hours=workable, affected_activities=affected, recommendations=recommendations ) def weekly_forecast_risk(self, conditions: List[WeatherCondition], activities: List[str] = None) -> pd.DataFrame: """Assess risk for week of weather data.""" # Group by date daily_conditions = {} for c in conditions: date = c.timestamp.date() if date not in daily_conditions: daily_conditions[date] = [] daily_conditions[date].append(c) assessments = [] for date, day_conditions in daily_conditions.items(): # Use midday condition as representative midday = [c for c in day_conditions if 10 <= c.timestamp.hour <= 16] representative = midday[len(midday)//2] if midday else day_conditions[0] assessment = self.assess_workability(representative, activities) assessments.append({ 'date': date, 'risk_level': assessment.risk_level.value, 'workable_hours': assessment.workable_hours, 'affected_count': len(assessment.affected_activities) }) return pd.DataFrame(assessments)

Quick Start

# Initialize client weather = WeatherAPIClient() # Get forecast for site conditions = weather.get_forecast(latitude=52.52, longitude=13.41, days=7) df = weather.to_dataframe(conditions) print(df.head()) # Assess construction risk risk = ConstructionWeatherRisk() weekly_risk = risk.weekly_forecast_risk(conditions) print(weekly_risk)

1. Schedule Planning

conditions = weather.get_forecast(52.52, 13.41, days=14) risk = ConstructionWeatherRisk() # Check concrete pour window for c in conditions: assessment = risk.assess_workability(c, ['concrete_pour']) if assessment.risk_level == WeatherRisk.LOW: print(f"Good for concrete: {c.timestamp}")

2. Historical Analysis

historical = weather.get_historical(52.52, 13.41, '2024-01-01', '2024-03-31') df = weather.to_dataframe(historical) # Count rain days rain_days = df[df['precipitation'] > 2]['timestamp'].dt.date.nunique() print(f"Rain days in Q1: {rain_days}")

Resources

DDC Book: Chapter 2.2 - Open Data Integration Open-Meteo API: https://open-meteo.com/

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

2 Docs1 Config

SKILL.md Primary doc
instructions.md Docs
claw.json Config