flight-service

Coordinate flight lifecycle state transitions with Neo4j persistence and Kafka messaging.

11|Updated Mar 23, 2026

Search Tags:#event-driven #kafka #neo4j #state-machine #flight-service #air-traffic-simulation

Ready to run

npx skills add https://github.com/Jupiter41/arthur-airport --skill flight-service

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: flight-service
Source: https://github.com/Jupiter41/arthur-airport/tree/main/services/flight-service
Command: npx skills add https://github.com/Jupiter41/arthur-airport --skill flight-service

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill coordinates and automates the end-to-end lifecycle of flights within a simulated airport, unifying scheduling, gate/runway allocation, and turnaround workflows for research, testing, and agent-driven development.

Core Features & Use Cases

State-machine driven flight lifecycle: advances flights through scheduled, boarding, delayed, departed, airborne, approach, landed, taxiing, at_gate, arrived, and cancelled states.
Runway and gate orchestration: handles slot allocation, gate conflicts, and reassignments under varying weather and incident conditions.
Turnaround and cascade propagation: models inbound/outbound aircraft turnarounds and ripple delay effects across paired flights, with optional event propagation.

Quick Start

Start the flight-service simulation and query the REST API to observe flight lifecycles.

Frequently Asked Questions about flight-service

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I model flight state transitions in an event-driven airport simulation?▼

Model flight state transitions by advancing flights through a deterministic finite state machine covering scheduled, boarding, departed, airborne, landed, and arrived states. This ensures observable, testable lifecycle progression integrated with Kafka messaging.

How does Kafka handle event-driven flight lifecycle updates with Neo4j persistence?▼

Kafka handles event-driven flight lifecycle updates by propagating state changes and cascade delay effects across paired flights. Neo4j persists these transitions, creating analytics-ready data flows for simulation orchestration.

Can I simulate gate and runway allocation conflicts under varying weather conditions?▼

Yes, you can simulate gate and runway allocation conflicts. The system handles slot allocation, gate conflicts, and reassignments dynamically under varying weather and incident conditions within the digital airport twin.

What is the best way to model aircraft turnaround and cascade delay propagation?▼

The best way to model aircraft turnaround and cascade delay propagation is using an event-driven state machine that models inbound and outbound turnarounds and ripple delay effects across paired flights with optional event propagation.

Does this flight-service simulation require Kafka and Neo4j to run?▼

Kafka and Neo4j are integral to the flight-service simulation. Kafka provides event-driven messaging for state updates, while Neo4j handles persistence, ensuring the state machine remains observable and analytics-ready.