Integrate DockKit-compatible camera docks with automatic subject framing in iOS apps.
npx skills add https://github.com/FelixRauch/medat-figuren --skill dockkit-felixrauch
Please help me install this Agent Skill. Skill: dockkit Source: https://github.com/FelixRauch/medat-figuren/tree/main/.agents/skills/dockkit Command: npx skills add https://github.com/FelixRauch/medat-figuren --skill dockkit-felixrauch
DockKit eliminates the manual effort of keeping a motorized iPhone dock camera framed on moving subjects, enabling reliable subject tracking during capture.
Use case example: Build a camera or video app that automatically keeps a presenter’s face centered while recording, then switch to custom tracking (e.g., Vision-based detections) when you need specialized subjects.
Ask the AI to integrate DockKit by subscribing to accessoryStateChanges, enabling system tracking on dock events, and configuring framing/ROI before starting AVFoundation capture.
High-intent search queries and answers about installing and using this skill.
Integrate motorized dock subject tracking by subscribing to accessory state changes, enabling system tracking on dock events, and configuring framing or region of interest before starting AVFoundation capture. This handles accessory discovery, pan/tilt motor control, and automatic subject framing across face, body, and object scenarios.
You can disable built-in ML tracking and feed custom observations at 10–30 fps to control motorized docks. This allows specialized subject tracking using Vision-based detections while maintaining deterministic pan/tilt motor control workflows within the DockKit iOS frameworks.
DockKit accessory discovery and motor control frameworks require iOS 17+ and Swift 6.3. These versions provide the necessary APIs for async accessory lifecycle handling, system or custom subject tracking, and region of interest configuration for motorized camera docks.
Region of interest framing constrains subject tracking to a specific area, adjusting framing modes and pan/tilt motor control to keep subjects centered. It applies across face, body, and object tracking scenarios using DockKit-compatible stands for reliable capture.
The best way is subscribing to accessoryStateChanges to detect when a DockKit-compatible stand is docked or undocked, then asynchronously enabling system tracking and configuring framing upon dock events for reliable subject tracking during capture.
Custom observation tracking requires maintaining 10–30 fps feed rates for deterministic motor control, and disabling built-in ML tracking means you must supply your own Vision-based detections for subject selection, tracking state monitoring, and saliency signals.
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