weather-api-1Fetch weather data for construction scheduling. Historical data, forecasts, and risk assessment for outdoor work.
Install via ClawdBot CLI:
clawdbot install phucanh08/weather-api-1Weather impacts 50% of construction activities. This skill fetches weather data for scheduling, risk assessment, and productivity adjustments.
```python
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)
```
```python
weather = WeatherAPIClient()
conditions = weather.get_forecast(latitude=52.52, longitude=13.41, days=7)
df = weather.to_dataframe(conditions)
print(df.head())
risk = ConstructionWeatherRisk()
weekly_risk = risk.weekly_forecast_risk(conditions)
print(weekly_risk)
```
```python
conditions = weather.get_forecast(52.52, 13.41, days=14)
risk = ConstructionWeatherRisk()
for c in conditions:
assessment = risk.assess_workability(c, ['concrete_pour'])
if assessment.risk_level == WeatherRisk.LOW:
print(f"Good for concrete: {c.timestamp}")
```
```python
historical = weather.get_historical(52.52, 13.41, '2024-01-01', '2024-03-31')
df = weather.to_dataframe(historical)
rain_days = df[df['precipitation'] > 2]['timestamp'].dt.date.nunique()
print(f"Rain days in Q1: {rain_days}")
```
Generated Mar 1, 2026
Project managers use the skill to fetch 7-day forecasts for a site's coordinates, assessing daily workability for activities like concrete pouring or roofing. They adjust schedules based on risk levels, minimizing delays and ensuring safety compliance.
Insurance adjusters retrieve historical weather data to verify claims related to weather damage on construction projects. This helps determine liability and validate timelines for incidents like storms or extreme temperatures.
Event planners coordinating groundbreakings or public unveilings use forecasts to select dates with low weather risk. They assess conditions like wind and precipitation to ensure attendee safety and smooth operations.
Logistics managers integrate weather data to plan deliveries of sensitive materials like concrete or paint to construction sites. They avoid routes or times with high precipitation or wind to prevent spoilage and delays.
Safety officers monitor real-time weather conditions to enforce protocols, such as halting crane work during high winds. They use the skill's risk levels to trigger alerts and maintain regulatory compliance on-site.
Offer the skill as part of a subscription-based platform for construction firms, providing API access with tiered plans based on data volume and features like historical archives. Revenue comes from monthly or annual fees.
Provide customized weather risk assessments and integration support for large construction projects, charging per project or hourly. This includes training teams and tailoring thresholds for specific activities.
License aggregated weather data and insights to third parties like insurance companies or research institutions, with pricing based on data sets or usage metrics. This leverages the skill's historical and forecast capabilities.
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Integrate with existing project management tools via APIs to automate schedule adjustments based on weather data, ensuring real-time updates without manual input.
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