travel-itinerary-planner

name	travel-itinerary-planner
description	Build a structured multi-day travel itinerary JSON from real CSV datasets. Use this skill whenever the user asks to create a travel plan, trip itinerary, or vacation schedule from a local database of cities, restaurants, accommodations, and attractions.

Travel Itinerary Planner

Purpose

Generate a valid itinerary.json from real-world CSV data files for a multi-city road trip, respecting budget, dietary, pet, and transportation constraints.

Input Requirements

Collect from user prompt:

• Origin city and destination cities
• Travel dates and duration (number of days)
• Total budget (USD)
• Party size and special needs (pets, dietary preferences)
• Transportation mode (road trip = no flights)
• Meal cuisine preferences

Dataset Locations

Dataset	Path
City list	data/background/citySet_with_states.txt
Restaurants	data/restaurants/clean_restaurant_2022.csv
Accommodations	data/accommodations/clean_accommodations_2022.csv
Attractions	data/attractions/attractions.csv
Distances	data/googleDistanceMatrix/distance.csv

Planning Workflow

Step 1 — Select Destination Cities

Query citySet_with_states.txt to confirm cities exist in the dataset for the target state.

Step 2 — Plan Route and Travel Days

• Look up driving distances/durations in distance.csv (columns: origin,destination,cost,duration,distance)
• Assign travel days for long drives; designate full days for city exploration
• No flights allowed if user specifies road trip

Step 3 — Select Pet-Friendly Accommodations

Filter clean_accommodations_2022.csv:

• city matches destination
• house_rules does NOT contain "No pets"
• minimum nights ≤ number of nights planned in that city
• Choose highest review rate number within budget

Accommodation cost = price × number of nights in that city.

Step 4 — Select Restaurants by Cuisine

Filter clean_restaurant_2022.csv:

• City matches current city
• Cuisines contains one of the preferred cuisines
• Spread meals across preferred cuisine types across the trip
• Skip meals ("-") on long travel days when meals aren't practical

Step 5 — Select Attractions

From attractions.csv:

• City matches current city
• Choose 2–4 per full day in city
• Format: "Name1;Name2;Name3;" (semicolon-separated, trailing semicolon)

Step 6 — Budget Check

Total = sum(accommodation nightly rate × nights per city) + sum(meal average costs)
Target: Total ≤ budget

Transport has no explicit cost in datasets (self-driving); gas/misc covered by remaining budget.

Output JSON Structure

{
  "plan": [
    {
      "day": 1,
      "current_city": "from A to B",
      "transportation": "Self-driving: from A to B",
      "breakfast": "Restaurant Name, City",
      "lunch": "-",
      "dinner": "Restaurant Name, City",
      "attraction": "-",
      "accommodation": "Accommodation Name, City"
    }
  ],
  "data_sources": [
    "background/citySet_with_states.txt",
    "accommodations/clean_accommodations_2022.csv",
    "restaurants/clean_restaurant_2022.csv",
    "attractions/attractions.csv",
    "googleDistanceMatrix/distance.csv"
  ]
}

Key Rules

• Travel days: current_city = "from A to B", skip attractions, include meals only when practical
• Staying days: current_city = city name, list attractions, full meals
• Last day (return): accommodation = "-" (back home)
• Always end attraction strings with a trailing semicolon
• All data must come from the CSV files — never invent restaurants, accommodations, or attractions