Here’s a problem specific to people who rip their own discs: you’re standing in a thrift store holding a $3 Blu-ray, and you genuinely cannot remember whether it’s already in your library. Pull out your phone, squint at a media app, type the title, hope you spelled it like the metadata does… by which point you’ve put it back.

So I built jf-barcode-search: point your phone camera at the barcode on the case, and it tells you instantly whether that title is already in your Jellyfin library. It’s a small Flask app, and this post covers both what it does and — because I decided to open-source it — the pre-flight review that makes a homelab repo safe to publish, which is a checklist worth having regardless of what you’re releasing.

I wanted one command — /ipad-internet off — that instantly cuts the household iPad’s internet, and /ipad-internet on to give it back. Simple ask. The interesting part is doing it correctly: instantly (no waiting on a config save), reversibly, without disturbing the LAN (AirPlay, printers, local media should all keep working), and — most importantly — in a way that can never accidentally knock a different device offline.

It ended up as a Claude Code skill backed by a careful bit of OPNsense / pf design. This post is about that design: why it uses a pf table instead of editing the config, how it survives the iPad’s shifting IP and Apple’s private Wi-Fi MAC, and the ownership guard that makes the “wrong device” failure mode structurally impossible.

I wanted my house to say, out loud, “the mail truck is out front” — the moment the USPS truck pulls up, spoken through the same Home Assistant TTS pipeline that runs the rest of the place. Not “a vehicle was detected.” Specifically the mail truck.

The gap between those two sentences is the whole project. My NVR (Frigate) is very good at “there’s a truck in the driveway zone” and completely incapable of “that’s a USPS LLV.” Closing that gap meant adding a small second-stage classifier that looks at the snapshot Frigate already captured and decides what kind of truck it is — running on hardware I already own, with no cloud vision API. It ended up as a zero-shot CLIP classifier, and along the way it picked up a second job I never planned: catching the garbage truck too.