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yashpalsince2004
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dockkit

Manage DockKit accessories and subject tracking on motorized camera docks.

Updated Jul 12, 2026
Search Tags:#machine learning#real-time control#DockKit#iPhone camera#camera tracking
One-click install
npx skills add https://github.com/yashpalsince2004/National_academy_app --skill dockkit-yashpalsince2004
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dockkit
Source: https://github.com/yashpalsince2004/National_academy_app/tree/main/Mobile/.agents/skills/dockkit
Command: npx skills add https://github.com/yashpalsince2004/National_academy_app --skill dockkit-yashpalsince2004

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires DockKit, AVFoundation, Vision, Spatial, and includes scripts (resource) and references (resource) components.

What problem does it solve?

The DockKit skill streamlines camera accessory interaction and subject tracking, simplifying video applications with automatic and customizable camera control on motorized stands.

Core Features & Use Cases

  • Automatic Camera Subject Tracking: Systematic tracking of faces and bodies, keeping subjects within frame without additional coding.
  • Custom Tracking & ML Integration: Allows apps to use custom ML models and integrate Vision framework observations for tracking.
  • Framing & Motor Control: Configure framing behavior, control camera tilt, and implement custom pan, tilt, and roll for advanced movements.
  • Real-Time Accessories Management: Real-time discovery of compatible accessories and monitoring of dock status and battery life.
  • Use Case: For developers of camera-based applications who require automatic camera control with minimal code overhead.

Quick Start

Enable DockKit system tracking to begin automatic camera control or set it to custom mode for personalized control.

Frequently Asked Questions about dockkit

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

FAQPage Schema
How do I implement automatic subject tracking for an iPhone camera using DockKit?▼

To implement automatic subject tracking, use AVFoundation with the DockKit SDK to enable system tracking mode. This automatically keeps faces and bodies within the camera frame on motorized stands without requiring additional tracking code.

Can I use custom machine learning models for iPhone camera tracking?▼

You can use custom machine learning models for camera tracking by setting DockKit to custom mode. This allows you to integrate Vision framework observations to personalize camera control and tracking behaviors.

How do I control camera pan, tilt, and roll on a motorized iPhone dock?▼

You can control camera pan, tilt, and roll by interacting directly with the dock motors using the DockKit SDK. This allows you to configure framing behavior and implement advanced custom movements.

What iOS version is required for DockKit battery monitoring and detailed tracking?▼

DockKit requires iOS 17 for system tracking and accessory event monitoring, while iOS 18 or later is required for detailed tracking features and battery state monitoring.

How do I discover compatible DockKit accessories and monitor their battery status?▼

You can discover compatible DockKit accessories and monitor their status by accessing real-time accessory events. The DockKit SDK provides interfaces to track dock connectivity and battery life states.

Does AVFoundation work with DockKit for system face and body tracking?▼

AVFoundation works natively with DockKit to provide system face and body tracking. This integration enables automatic framing of subjects using the motorized camera dock controls.

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