# Send Weather Api to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "weather-api", "name": "Weather Api", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/datadrivenconstruction/weather-api", "canonicalUrl": "https://clawhub.ai/datadrivenconstruction/weather-api", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/weather-api", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=weather-api", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "claw.json", "instructions.md", "SKILL.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "weather-api", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-10T20:45:21.579Z", "expiresAt": "2026-05-17T20:45:21.579Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=weather-api", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=weather-api", "contentDisposition": "attachment; filename=\"weather-api-2.1.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "weather-api" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/weather-api" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/weather-api", "downloadUrl": "https://openagent3.xyz/downloads/weather-api", "agentUrl": "https://openagent3.xyz/skills/weather-api/agent", "manifestUrl": "https://openagent3.xyz/skills/weather-api/agent.json", "briefUrl": "https://openagent3.xyz/skills/weather-api/agent.md" } } ``` ## Documentation ### 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/ ## Trust - Source: tencent - Verification: Indexed source record - Publisher: datadrivenconstruction - Version: 2.1.0 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-05-10T20:45:21.579Z - Expires at: 2026-05-17T20:45:21.579Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/weather-api) - [Send to Agent page](https://openagent3.xyz/skills/weather-api/agent) - [JSON manifest](https://openagent3.xyz/skills/weather-api/agent.json) - [Markdown brief](https://openagent3.xyz/skills/weather-api/agent.md) - [Download page](https://openagent3.xyz/downloads/weather-api)