Forecast Skill

Forecast Skill

Get real-time weather data and forecasts for any location worldwide using OpenWeather.

Prerequisites

This skill requires an OpenWeather API key to be configured in config.json before uploading.

Setup (done once before uploading the skill):

1. Get a free API key at https://openweathermap.org/api
2. Edit config.json and replace PASTE_YOUR_API_KEY_HERE with your key
3. Ensure api.openweathermap.org is in your Claude environment's allowed network domains
4. Upload the skill to Claude

Network Requirements:

• This skill makes HTTPS requests to api.openweathermap.org
• Some Claude environments may require this domain to be added to allowed network lists
• Contact your administrator if you encounter connection errors

See SETUP.txt for detailed instructions.

Usage

Understanding API Limitations

Important: The OpenWeather free tier provides:

• Current weather for any date (today)
• 5-day forecast maximum (today + 4 future days)
• If a user asks for weather beyond 5 days, explain the limitation and suggest alternatives

Single Location Forecast

When the user asks about weather for one location:

Modern format (preferred):

python skills/get_weather.py current "<location>"          # Current weather
python skills/get_weather.py forecast "<location>" <days>  # Multi-day forecast (1-5 days)

Legacy format (still supported):

python skills/get_weather.py "<location>" "<YYYY-MM-DD>"

Important: The script is located at skills/get_weather.py (not scripts/)

Multi-Location Comparison

When the user asks to compare weather between locations:

Trigger phrases:

• "Compare weather in Paris vs London"
• "Paris or Berlin next week?"
• "Which city has better weather this week?"

Command format:

python skills/get_weather.py compare "<location1>" "<location2>" <days>

Important: Days parameter is limited to 1-5 due to API forecast limits.

Activity Recommendations

When the user asks about weather suitability for specific activities:

Trigger phrases:

• "Is it good for skiing in Aspen this week?"
• "When should I plan a picnic in the park?"
• "What are the best days for hiking in Colorado?"
• "Should I water my garden tomorrow?"

Supported activities: skiing, picnic, hiking, gardening, beach, cycling, plus general outdoor activities and sports

Flexible activity detection: The system can analyze weather for many activities, even if not explicitly listed. It will automatically map unfamiliar activities to the most appropriate general category.

Command format:

python skills/get_weather.py activity "<activity_type>" "<location>" <days>

Important: Days parameter is limited to 1-5 due to API forecast limits.

Multi-Location Comparison Commands

For location comparisons:

python skills/get_weather.py compare "<location1>" "<location2>" <days>

Present side-by-side comparison with recommendations for travel planning.

Error Handling

• missing_api_key: "The weather service isn't configured. Please edit config.json with your OpenWeather API key before uploading this skill."
• invalid_api_key: "Invalid OpenWeather API key. Please check your key in config.json."
• location_not_found: Ask for a more specific location (e.g., "Paris, France" instead of "Paris")
• quota_exceeded: "The weather service has reached its daily limit. Please try again later."
• date_not_available: Forecast is limited to 5 days maximum. When this occurs:
  • Explain the 5-day limit to the user
  • Offer to provide available forecast data for the timeframe that is available
  • Suggest checking back closer to the desired date for extended forecasts

Response Format

Single Location Response

The script returns JSON with these fields:

• location: Resolved location name
• date: Date of forecast
• temp_c, temp_f: Temperature in Celsius and Fahrenheit
• humidity: Relative humidity percentage
• wind_kph: Wind speed in km/h
• condition: Weather description (e.g., "partly cloudy", "light rain")
• precip_mm: Precipitation in millimeters
• sunrise_utc, sunset_utc: Sun times in UTC ISO format

Comparison Response

For multi-location comparisons, the script returns:

• comparison_type: "multi_day"
• num_days: Number of days compared
• location1/location2: Each containing:
  • name: Resolved location name
  • forecasts: Array of daily forecasts with temps, conditions, precipitation
• recommendations: Array of comparison insights and suggestions

Activity Recommendation Response

For activity analysis, the script returns:

• analysis_type: "activity_recommendation"
• activity: Activity name (e.g., "Skiing", "Picnic")
• overall_score: 0-100 suitability rating
• overall_rating: Text rating (Excellent/Good/Fair/Poor/Not Recommended)
• best_day: Date and score of most suitable day
• daily_analysis: Array of daily scores and recommendations
• overall_recommendation: Summary advice for the activity period

Present this information conversationally, highlighting the most relevant details for the user's query.

Error Handling

The script may return errors in JSON format:

• missing_api_key: API key not provided - ask user for their key
• invalid_api_key: API key is invalid - ask user to verify their key
• location_not_found: Location not recognized - ask for clarification (e.g., "Paris, France" instead of "Paris")
• quota_exceeded: API daily limit reached - inform user to try later
• invalid_date: Date format incorrect - use YYYY-MM-DD format
• unknown_activity: Activity not recognized - show supported activities list
• Network errors: Inform user the weather service is temporarily unavailable

Examples

User: "What's the weather in Boulder this weekend?" Claude should recognize this MAY need the skill, but users get better results with: User: "Using the forecast-skill, what's the weather in Boulder this weekend?" Action:

1. Extract: location="Boulder", dates=[Saturday, Sunday]
2. Call: python skills/get_weather.py forecast Boulder 2
3. Summarize: "This weekend in Boulder: Saturday will be sunny and 72°F, perfect for outdoor activities. Sunday looks cooler at 65°F with a chance of afternoon showers."

User: "Using forecast-skill, should I bring an umbrella to Tokyo tomorrow?" Action:

1. Extract: location="Tokyo", date=tomorrow
2. Call: python skills/get_weather.py forecast Tokyo 1
3. Check precipitation and respond: "Tomorrow in Tokyo has a 70% chance of rain with 5mm expected. Definitely bring an umbrella!"

User: "With the forecast-skill, is this week good for skiing in Vail?" Action:

1. Extract: activity="skiing", location="Vail", days=5 (limit to 5-day forecast)
2. Call: python skills/get_weather.py activity skiing Vail 5
3. Analyze response: "The next 5 days look excellent for skiing in Vail! Overall score: 85/100. Best conditions on Wednesday with fresh powder and temps around -2°C."

User: "Using forecast-skill, compare weather in Aspen this weekend vs next weekend" Action:

1. Recognize next weekend exceeds 5-day limit
2. Call: python skills/get_weather.py forecast Aspen 2 (for this weekend)
3. Respond: "I can provide this weekend's forecast for Aspen, but weather data beyond 5 days isn't available. This weekend looks great with sunny skies and 12°C. For next weekend, I'd suggest checking back in a few days for the most accurate forecast."

User: "Using the forecast-skill, compare weather in London vs Paris next week" Action:

1. Extract: location1="Paris", location2="London", days=7
2. Call: python skills/get_weather.py compare "Paris" "London" 7
3. Present comparison: "Comparing Paris and London for the next week: Paris will be warmer overall (averaging 18°C vs 14°C) but has more rainy days expected (4 vs 2). London has fewer rainy days expected, making it better for outdoor activities despite cooler temperatures."

Skill Activation Tips

Best Practice: Users should prefix queries with "Using the forecast-skill," or "With forecast-skill," to ensure reliable activation.

Examples of effective prompts:

• "Using the forecast-skill, [weather question]"
• "With forecast-skill, [weather question]"
• "Can you use forecast-skill to [weather question]"

Why explicit activation helps:

• Ensures Claude uses the weather API instead of general knowledge
• Gets real-time data rather than training data
• Provides more accurate and current forecasts