varunr89 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:
- Three one-way flights -- Independent one-way searches for each leg
- 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