flight-search

Flight Search

Searches for multi-leg flight itineraries (e.g., Seattle -> Milan -> Hyderabad -> Seattle) using the Duffel API (with optional fast-flights Google Flights scraper fallback). Applies hard constraints and a weighted scoring model to rank results.

When to use

Use this skill when the user wants to search for flight options across multiple legs with constraint filtering and convenience-adjusted scoring.

Usage

DUFFEL_API_KEY=<your-key> python3 ${CLAUDE_PLUGIN_ROOT}/scripts/flight_optimizer.py

The script currently targets a specific trip (SEA -> MXP -> HYD -> SEA), but the functions are reusable for any multi-leg itinerary.

Environment variables

Variable	Required	Description
DUFFEL_API_KEY	Yes	Duffel API access token (live or test mode)

Constraints model

The optimizer applies hard constraints to filter out unsuitable flights:

• Max stops: 1 stop per leg (configurable via MAX_STOPS)
• Max layover: 4 hours (configurable via MAX_LAYOVER_HOURS)
• Single carrier per leg: No itineraries with unconnected carriers (comma in airline name)
• Valid pricing: Flights with unparseable prices are excluded

Scoring formula

Each flight leg is scored as:

leg_score = price + (duration_hours * $20) + (stops * $200)

Complete itineraries add a childcare penalty:

total_score = sum(leg_scores) + (weekdays_away * $200)

Scoring weights are configurable constants: COST_PER_HOUR, COST_PER_STOP, COST_PER_WEEKDAY.

Search strategies

The optimizer tries two strategies and ranks all results together:

1. Three one-way flights -- Independent one-way searches for each leg
2. Two round-trips -- SEA<->MXP and MXP<->HYD as round-trip bookings (often cheaper due to airline RT pricing)

Output

• Prints top 10 itineraries to stdout with detailed leg information
• Saves raw results to flight_results_spring2026.json (top 30 itineraries)

Dependencies

• requests (pip install requests) -- Duffel API calls
• fast-flights (pip install fast-flights) -- Optional Google Flights scraper fallback