Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Added a comprehensive README.md with setup, usage, and implementation notes. - Clarified that the watcher script now prints no output when nothing changes, to reduce noise in cron jobs. - Updated documentation to detail input handling, polling strategies, and heuristic use of aircraft data.
What This Skill Does
Tracks Air France flights using the AFKL Open Data Flight Status API. Monitors delays, gate changes, and aircraft updates, and follows the previous-flight chain to catch upstream disruptions before they affect a departure.
Polling the previous-flight chain means you catch cascading delays before the airline officially updates the outbound flight status.
When to use it
- Checking if AF007 is on time before heading to JFK
- Getting an alert when a flight gate changes 2 hours out
- Finding out if an inbound aircraft is delayed and will cause a late departure
- Looking up the tail number to assess cabin Wi-Fi availability before upgrading
- Setting up automated polling that respects API rate limits around a departure window
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Is AF7 from JFK on January 29 running on time, and does the aircraft have Wi-Fi?
- 1Resolve flight identifier AF7 and departure date 2026-01-29 to API parameters (carrier AF, flight 7, origin JFK)
- 2Call afkl_flightstatus_query.mjs with those parameters using AFKL_API_KEY
- 3Parse the response for times.* fields to check scheduled vs. estimated departure
- 4Extract aircraft.registration (tail number) and infer sub-fleet and cabin generation
- 5Assess Wi-Fi likelihood based on tail number and sub-fleet data
AF7 is currently on schedule, departing 18:10. Aircraft is F-GSQJ (777-300ER, newer sub-fleet), Wi-Fi likely available.
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.