Itinerary Budget Planning
SkillDev toolsPlanning multi-city travel itineraries with budget constraints, route optimization, and cuisine diversity.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Itinerary Budget Planning skill
What this skill tells your AI
The instructions your AI receives, as published by cxcscmu/skilllearnbench in skills/b1-one-shot-claude-opus-4-6/travel-planning/itinerary-budget-planning/SKILL.md and read by ahel’s review.
Route Optimization
- Choose cities that minimize total driving time
- Consider geographic clustering (e.g., Cleveland→Dayton→Cincinnati forms a logical loop)
- Start and end at the origin city for round trips
Budget Components
- Accommodations: price × nights per city
- Meals: restaurant average cost × number of meals
- Transportation: Self-driving costs (gas estimated from distances)
Cuisine Diversity Strategy
- Map required cuisines (American, Mediterranean, Chinese, Italian) across meals
- Use restaurant
Cuisinesfield to match — many restaurants serve multiple cuisines - Spread different cuisines across breakfast, lunch, and dinner slots
Accommodation Selection Criteria
- Check
maximum occupancy >= party size - Check
minimum nights <= planned stay duration - Filter by
house_rulesfor specific needs (pets, children, smoking) - Balance price vs. review rating
Day Planning Template
- Travel days: skip meals during long drives (use "-")
- City days: 3 meals + 2-3 attractions
- Transition days: breakfast in departing city, lunch/dinner in arriving city
Output Format
JSON with plan array (7 day objects) and data_sources array.
Each day: day, current_city, transportation, breakfast, lunch, dinner, attraction, accommodation.
Signals
- GitHub stars
- 83
- Forks
- 5
- Last commit
- Jul 2026
Advanced
- Catalog kind
- skill
- Gateway key
itinerary-budget-planning- Source
- github.com/cxcscmu/skilllearnbench