Weather Outfit Advisor

Query weather and provide outfit recommendations. When information is insufficient, ask about destination, date, and clothing preferences. Uses free weather APIs.

KadiJIN@kadijin

Install

openclaw skills install @kadijin/weather-outfit-advisor

Weather Outfit Advisor

Overview

Provides personalized outfit recommendations based on real-time weather data for your destination and your personal clothing preferences.

Workflow

Step 1: Check Information Completeness

Before providing recommendations, must confirm the following three key pieces of information:

Destination (city name)
Date (specific date or date range)
Clothing Preferences (style preference, temperature sensitivity, special needs, etc.)

Information Extraction Strategy:

Extract explicit destination, date, and preferences from user input
Recognize relative date terms ("tomorrow", "day after tomorrow", "next Monday", etc.) and convert to specific dates
If any item is missing, must ask the user first

Relative Date Conversion Rules:

"tomorrow" → current date + 1 day
"day after tomorrow" → current date + 2 days
"three days later" → current date + 3 days
"next Monday" → next Monday's date
"this weekend" → this Saturday or Sunday (needs confirmation)

Example:

text

User: "I'm going to Hangzhou tomorrow"
→ Extract: destination = Hangzhou, date = tomorrow
→ Convert: get current date (e.g., 2026-04-01), calculate tomorrow = 2026-04-02
→ Missing: clothing preferences
→ Action: ask about clothing preferences

Step 2: Collect Necessary Information

Adopt different clarification strategies based on the number of missing items:

Strategy A: Missing Only 1 Item (Targeted Question)

Ask only about the missing item:

If only missing clothing preferences:

text

Sure, I'll help you check the weather for [city] on [date].

To make the recommendation more suitable for you, please tell me about your clothing preferences:
- Are you usually sensitive to cold or heat?
- What style do you prefer? (casual/business/sports, etc.)
- Any special needs? (such as formal occasions, outdoor activities, etc.)

If only missing date:

text

When are you planning to travel? (Please provide a specific date, or say "tomorrow", "day after tomorrow", etc.)

If only missing destination:

text

What is your travel destination? (Please provide the city name)

Strategy B: Missing 2-3 Items (Ask All at Once)

When multiple items are missing, list all questions at once:

text

To provide you with the most suitable outfit recommendations, I need some information:

1. What is your travel destination?
2. When exactly are you traveling? (You can say "tomorrow", "April 5th", etc.)
3. What are your usual clothing habits? (For example, sensitive to cold/heat, preferred style, etc.)

Relative Date Handling

When users use relative dates:

Recognize Keywords:

tomorrow, day after tomorrow, three days later
next Monday, next Tuesday... next Sunday
this weekend, next weekend
next week, next month

Conversion Logic:

Get current system date
Calculate specific date based on relative term
Confirm the converted date in the response

Example:

text

User: "I'm going to Hangzhou tomorrow"
→ System gets current date: 2026-04-01
→ Calculate: tomorrow = 2026-04-02
→ Response confirmation: "Sure, I'll check the weather for Hangzhou tomorrow (April 2nd)..."

Step 3: Query Weather Data

Use the provided Python script (recommended):

bash

# Basic usage
python scripts/get_weather.py <city> [date]

# Examples
python scripts/get_weather.py Hangzhou 2026-04-02
python scripts/get_weather.py Beijing tomorrow
python scripts/get_weather.py Shanghai tomorrow

Script Features:

✅ Automatic relative date parsing (tomorrow, day after tomorrow, next Monday, etc.)
✅ City name normalization (supports Chinese/English and common aliases)
✅ Calls wttr.in API to get weather data
✅ Returns structured JSON data
✅ Includes current weather and forecast information

Output Example (JSON format):

json

{
  "success": true,
  "city": "Hangzhou",
  "query_date": "2026-04-01",
  "target_date": "2026-04-02",
  "current": {
    "temp_c": 19,
    "feels_like_c": 19,
    "humidity": 56,
    "weather_desc": "Patchy rain nearby",
    "wind_speed_kmph": 6,
    "uv_index": 5
  },
  "forecast": {
    "max_temp_c": 21,
    "min_temp_c": 11,
    "avg_humidity": 60,
    "daily_chance_of_rain": 70,
    "weather_desc": "Light drizzle"
  }
}

Extract Key Information from Output:

python

import json

# Assume weather_data is the JSON returned by the script
current = weather_data['current']
forecast = weather_data['forecast']

temperature_range = f"{forecast['min_temp_c']}-{forecast['max_temp_c']}°C"
weather_condition = forecast['weather_desc']
humidity = current['humidity']
rain_chance = forecast['daily_chance_of_rain']

Alternative: Direct API Call (if script is unavailable):

Current Weather API:

text

https://wttr.in/{city}?format=j1

Example:

bash

curl "https://wttr.in/Hangzhou?format=j1"

Parse JSON Response for Key Information:

python

import json

# Current weather
current = data['current_condition'][0]
temperature = current['temp_C']  # Celsius
feels_like = current['FeelsLikeC']  # Feels like temperature
humidity = current['humidity']  # Humidity
weather_desc = current['weatherDesc'][0]['value']  # Weather description
wind_speed = current['windspeedKmph']  # Wind speed

# Weather forecast
forecast = data['weather'][0]
max_temp = forecast['maxtempC']  # Max temperature
min_temp = forecast['mintempC']  # Min temperature

Alternative: OpenWeatherMap API (requires free API key):

If wttr.in is unavailable, you can use OpenWeatherMap:

API Endpoint:

text

https://api.openweathermap.org/data/2.5/weather?q={city}&appid={API_KEY}&units=metric&lang=en

Forecast API:

text

https://api.openweathermap.org/data/2.5/forecast?q={city}&appid={API_KEY}&units=metric&lang=en

Note:

{API_KEY} needs to be requested from https://openweathermap.org/api (free version is sufficient)
units=metric uses Celsius
lang=en returns English description

Step 4: Analyze Weather Data

Extract key information from the API response:

json

{
  "main": {
    "temp": 25,        // Current temperature
    "feels_like": 27,  // Feels like temperature
    "humidity": 60     // Humidity
  },
  "weather": [
    {
      "description": "Clear",  // Weather condition
      "main": "Clear"
    }
  ],
  "wind": {
    "speed": 3.5       // Wind speed
  }
}

Step 5: Generate Outfit Recommendations

Generate recommendations based on weather data and user preferences:

Temperature Range Reference

Temperature Range	Outfit Recommendation
< 5°C	Heavy down jacket, thermal underwear, sweater, scarf, gloves, hat
5-10°C	Thick coat, padded jacket, knitwear, thin sweater
10-15°C	Trench coat, jacket, hoodie, long-sleeve T-shirt
15-20°C	Light jacket, denim jacket, long-sleeve shirt, T-shirt + cardigan
20-25°C	Long-sleeve T-shirt, thin shirt, single layer clothing
25-30°C	Short-sleeve T-shirt, shirt, skirt, shorts
> 30°C	Breathable short-sleeve, tank top, sun protection clothing, sun hat

Consider Other Factors

Rainy Days:

Carry umbrella or raincoat
Choose waterproof shoes
Avoid light-colored clothing

Windy Days:

Avoid skirts or choose anti-exposure styles
Wear windproof jacket
Secure hat properly

High Humidity:

Choose breathable quick-dry fabrics
Avoid heavy clothing

Strong UV:

Apply sunscreen
Wear sunglasses
Wear sun protection clothing or long sleeves

Add Image Reference (Optional but Recommended)

After providing outfit recommendations, call the image search script to provide visual references:

bash

# Search city street style
python scripts/search_images.py "{city} street style fashion" 5

# Search seasonal outfits
python scripts/search_images.py "{city} {season} outfit" 5

# Search occasion-specific outfits
python scripts/search_images.py "{city} casual wear" 5

Examples:

bash

# Paris street style
python scripts/search_images.py "Paris street style fashion" 5

# Hangzhou casual outfits
python scripts/search_images.py "Hangzhou casual outfit" 5

Step 6: Output Format

Output recommendations in the following format:

markdown

## 📍 Destination: [City Name]
## 📅 Date: [Travel Date]
## 🌤️ Weather Overview

- **Temperature**: X°C (feels like Y°C)
- **Weather Condition**: [Clear/Cloudy/Rainy, etc.]
- **Humidity**: X%
- **Wind Speed**: X m/s

## 👔 Outfit Recommendations

### Recommended Outfit
- Top: [Specific recommendation]
- Bottom: [Specific recommendation]
- Shoes: [Specific recommendation]
- Accessories: [Specific recommendation]

### Notes
- [Special reminder 1]
- [Special reminder 2]

### Backup Plan
[Alternative suggestions for weather changes or other situations]

## 🖼️ Outfit Reference Images

[Call search_images.py script to search related images]

### Style References
- [Image 1 description and URL]
- [Image 2 description and URL]
- [Image 3 description and URL]

Special Cases Handling

API Call Failure

If API call fails or returns error:

text

Sorry, temporarily unable to get weather data for [city name]. Possible reasons:
1. City name recognition failed, please check spelling
2. API service temporarily unavailable
3. Network connection issue

Suggestions:
- Check if city name is correct
- Try again later
- Or tell me the current weather conditions (temperature, weather), I can also provide recommendations

Extreme Weather Warning

If extreme weather is detected (typhoon, blizzard, heavy rain warning, etc.):

text

⚠️ **Weather Warning**: [City Name] is about to experience [extreme weather type]

According to latest weather forecast:
- [Describe extreme weather situation]
- [Impact level and timing]

**Recommendations**:
1. Reconsider if travel is necessary
2. If travel is required, prepare adequate protection:
   - [Specific protective measures]
3. Monitor latest warnings from local meteorological department
4. Recommended items: [Special equipment like life jacket, anti-slip shoes, etc.]

Multi-Day Trip

If user provides a date range:

text

Detected your trip is a multi-day travel (Month Day - Month Day), I will:
1. Query weather forecast for each day separately
2. Provide daily outfit recommendations
3. Suggest clothing packing list (considering laundry needs)

Personalization Adjustments

Adjust recommendations based on user's clothing habits:

Cold-sensitive users: Recommend adding extra warm layers to base outfit Heat-sensitive users: Recommend more breathable fabrics, reduce layers Business needs: Maintain professional appearance while ensuring comfort Outdoor sports: Prioritize functional clothing (quick-dry, waterproof, windproof) Pregnant/elderly/children: Pay extra attention to warmth and comfort

Example Dialogues

Example 1: Complete Information

User: I'm going to Hangzhou tomorrow, I'm usually sensitive to cold, prefer casual style.

Assistant: Step 1: Query weather

bash

python scripts/get_weather.py Hangzhou tomorrow

Step 2: Parse data Extract from returned JSON:

Temperature range: 11-21°C
Weather condition: Patchy rain nearby
Humidity: 56%
Rain chance: 70%

Step 3: Generate recommendations

According to weather forecast, Hangzhou tomorrow:

Temperature: 11-21°C
Weather: Patchy rain nearby
Humidity: 56%

Outfit recommendations:

Top: Long-sleeve T-shirt + knit cardigan + thin trench coat (considering you're sensitive to cold, recommend bringing thermal underwear as backup)
Bottom: Jeans or casual pants
Shoes: Sneakers or casual shoes
Accessories: Can bring a thin scarf, umbrella

Example 2: Incomplete Information

User: I'm going out to play in a few days, how should I dress?

Assistant: To provide you with the most suitable outfit recommendations, I need some information:

What is your travel destination?
When exactly are you traveling?
What are your usual clothing habits? Any special preferences (such as sensitive to cold/heat, prefer casual/business style, etc.)?

Example 3: Real Call Case

User: I'm going to Hangzhou tomorrow, I'm quite heat-sensitive

Assistant: Execution Flow:

Information Extraction:
- Destination: Hangzhou ✓
- Date: tomorrow (relative date) → convert to specific date
- Clothing preferences: heat-sensitive ✓
Date Conversion:
- Get current system date: 2026-04-01
- Calculate: tomorrow = 2026-04-02
- Confirm: "Sure, I'll check the weather for Hangzhou tomorrow (April 2nd)"
Use wttr.in API to query Hangzhou weather: curl "https://wttr.in/Hangzhou?format=j1"
Parse JSON to get data:
- Current temperature: 19°C
- Max temp: 21°C, Min temp: 11°C
- Weather: Patchy rain nearby
- Humidity: 56%
Adjust recommendations based on heat-sensitive preference

Then output complete outfit recommendations...

Example 4: Boundary Case Testing

Scenario A: Seriously Insufficient Information

User: How should I dress?

Assistant: To provide you with the most suitable outfit recommendations, I need some information:

What is your travel destination?
When exactly are you traveling? (You can say "tomorrow", "April 5th", etc.)
What are your usual clothing habits? (For example, sensitive to cold/heat, preferred style, etc.)

Scenario B: Only Partial Information

User: I'm going to Shanghai the day after tomorrow

Assistant: Sure, I'll help you check the weather for Shanghai the day after tomorrow.

To make the recommendation more suitable for you, please tell me about your clothing preferences:

Are you usually sensitive to cold or heat?
What style do you prefer? (casual/business/sports, etc.)
Any special needs? (such as formal occasions, outdoor activities, etc.)

(Wait for user response, then query weather and provide complete recommendations)

Scenario C: Multi-Day Trip

User: I'm going to Beijing on business from next Monday to Friday, I'm sensitive to cold

Assistant: Execution Flow:

Recognize this is a date range: next Monday to Friday (total 5 days)
Query weather forecast for each of the 5 days separately
Provide daily outfit recommendations
Give overall suggestions for clothing packing list (considering laundry and matching)

Output format:

markdown

## 📍 Destination: Beijing
## 📅 Trip: Next Monday to Friday (April 7th - April 11th)

### Daily Outfit Recommendations

#### Monday (April 7th)
- Weather: Clear, 8-18°C
- Recommendation: ...

#### Tuesday (April 8th)
- Weather: Cloudy, 10-20°C
- Recommendation: ...

...

### Clothing Packing List
Considering 5-day trip and temperature variations, recommend bringing:
- Tops: X pieces (including...)
- Bottoms: X pieces
- Outerwear: X pieces
- Other accessories: ...

Example 5: Special Cases Handling

Extreme Weather Warning

If extreme weather is detected (typhoon, heavy rain red warning, etc.):

text

⚠️ **Weather Warning**: [City Name] is about to experience [extreme weather type]

According to latest weather forecast:
- [Describe extreme weather situation]
- [Impact level and timing]

**Recommendations**:
1. Reconsider if travel is necessary
2. If travel is required, prepare adequate protection:
   - [Specific protective measures]
3. Monitor latest warnings from local meteorological department
4. Recommended items: [Special equipment like life jacket, anti-slip shoes, etc.]

API Call Failure

If API call fails or returns error:

text

Sorry, temporarily unable to get weather data for [city name]. Possible reasons:
1. City name recognition failed, please check spelling
2. API service temporarily unavailable
3. Network connection issue

Suggestions:
- Check if city name is correct
- Try again later
- Or tell me the current weather conditions (temperature, weather), I can also provide recommendations

Prompt Optimization

When actually using, can add the following prompt optimization suggestions based on situation:

Remind users that areas with large temperature differences between morning and evening need "onion-style" dressing
Suggest bringing easy-to-match items to deal with weather changes
Recommend practical layering combinations
Consider luggage space limitations, suggest versatile items

Appendix: Script Usage Instructions

get_weather.py Script

Location: scripts/get_weather.py

Features:

✅ Supports Chinese/English city names
✅ Automatic relative date parsing (tomorrow, day after tomorrow, next Monday, etc.)
✅ City name normalization (e.g., "Beijing" → "Beijing")
✅ Returns structured JSON data
✅ Includes current weather and forecast information
✅ No API key required (uses wttr.in free service)

Usage:

bash

# Basic syntax
python scripts/get_weather.py <city> [date]

# Specific date
python scripts/get_weather.py Hangzhou 2026-04-02

# Relative date (English)
python scripts/get_weather.py Beijing tomorrow
python scripts/get_weather.py Shanghai day after tomorrow

# Relative date (Chinese)
python scripts/get_weather.py Guangzhou tomorrow
python scripts/get_weather.py Shenzhen day after tomorrow

# No date specified (defaults to today)
python scripts/get_weather.py Chengdu

Output Format:

stdout: JSON format weather data (for program processing)
stderr: Human-readable formatted output (for direct viewing)

JSON Data Structure:

json

{
  "success": true,
  "city": "City English Name",
  "query_date": "Query Date",
  "target_date": "Target Date",
  "current": {
    "temp_c": Current Temperature (Celsius),
    "feels_like_c": Feels Like Temperature,
    "humidity": Humidity Percentage,
    "weather_desc": "Weather Description",
    "wind_speed_kmph": Wind Speed (km/h),
    "uv_index": UV Index
  },
  "forecast": {
    "max_temp_c": Max Temperature,
    "min_temp_c": Min Temperature,
    "avg_humidity": Average Humidity,
    "daily_chance_of_rain": Rain Chance,
    "weather_desc": "Weather Condition"
  }
}

Error Handling: If query fails, returns:

json

{
  "success": false,
  "error": "Error message",
  "message": "Failed to get weather data: specific reason"
}

Dependencies:

Python 3.6+
Standard library: sys, json, urllib.request, datetime
No additional third-party libraries required

search_images.py Script

Location: scripts/search_images.py

Features:

✅ Searches fashion/outfit images related to destination
✅ Supports multiple image APIs (Pexels, Pixabay, etc.)
✅ Returns structured image information (URL, photographer, dimensions, etc.)
✅ Automatically generates optimized search keywords
✅ Provides manual search alternative

Supported APIs:

API	Requires Key	Free Quota	Recommendation
Pexels	✅ Yes	20,000/month	⭐⭐⭐⭐⭐
Pixabay	✅ Yes	500/day	⭐⭐⭐⭐
Bing Search	❌ No	Limited	⭐⭐⭐

Usage:

bash

# Basic syntax
python scripts/search_images.py <query> [count]

# Search Paris street style
python scripts/search_images.py "Paris street style fashion" 5

# Search Hangzhou casual outfits
python scripts/search_images.py "Hangzhou casual outfit" 5

# Search specific season
python scripts/search_images.py "Beijing winter fashion" 10

Output Format:

stdout: JSON format image list (for program processing)
stderr: Human-readable formatted output (for viewing)

JSON Data Structure:

json

{
  "success": true,
  "query": "Search Keyword",
  "total_results": 1000,
  "images": [
    {
      "url": "Large Image URL",
      "thumbnail": "Thumbnail URL",
      "photographer": "Photographer Name",
      "width": 1920,
      "height": 1080,
      "alt": "Image Description"
    }
  ]
}

Smart Keyword Generation:

Script automatically generates optimized search terms based on city context:

{city} street style fashion - Street style
{city} outfit ideas - Outfit inspiration
{city} casual wear - Casual outfits
what to wear in {city} - Travel outfits

API Key Configuration:

Recommend using Pexels API (highest quality):

Apply for free API key: https://www.pexels.com/api/
Set in script:

python

pexels_api_key = "your_api_key_here"

Or via environment variable:

bash

export PEXELS_API_KEY="your_api_key_here"

Fallback Strategy:

If API key is not configured, script will:

Provide manual search link
List available API options
User can visit search link to view images manually

Usage Scenario:

Add image references in outfit recommendations:

markdown

## 🖼️ Outfit References

Based on Paris fashion style, recommend the following references:

📸 **Street Style**
- Image 1: [Description] - [View Image](URL)
- Image 2: [Description] - [View Image](URL)
- Image 3: [Description] - [View Image](URL)

💡 **Styling Tips**:
- Learn layering techniques from images
- Pay attention to color coordination (mainly neutral colors)
- Choose appropriate accessories (scarves, bags)

Notes:

Image copyright belongs to original photographer
For personal reference only, not for commercial use
Respect photographers' work
Recommend using high-quality image API for better experience

Weather Outfit Advisor

Install

Weather Outfit Advisor

Overview

Workflow

Step 1: Check Information Completeness

Step 2: Collect Necessary Information

Strategy A: Missing Only 1 Item (Targeted Question)

Strategy B: Missing 2-3 Items (Ask All at Once)

Relative Date Handling

Step 3: Query Weather Data

Step 4: Analyze Weather Data

Step 5: Generate Outfit Recommendations

Temperature Range Reference

Consider Other Factors

Add Image Reference (Optional but Recommended)

Step 6: Output Format

Special Cases Handling

API Call Failure

Extreme Weather Warning

Multi-Day Trip

Personalization Adjustments

Example Dialogues

Example 1: Complete Information

Example 2: Incomplete Information

Example 3: Real Call Case

Example 4: Boundary Case Testing

Scenario A: Seriously Insufficient Information

Scenario B: Only Partial Information

Scenario C: Multi-Day Trip

Example 5: Special Cases Handling

Extreme Weather Warning

API Call Failure

Prompt Optimization

Appendix: Script Usage Instructions

get_weather.py Script

search_images.py Script

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