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Dockkit

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dpearson2699
dockkit

Control motorized camera docks and enable intelligent subject tracking using DockKit. Use when discovering DockKit-compatible accessories, implementing camera subject tracking for faces or bodies, controlling dock motors for pan and tilt, configuring framing behavior, setting regions of interest, or building video apps with automatic camera tracking.

Overview

Publisherdpearson2699
Repositoryswift-ios-skills
Skill namedockkit
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1.2K
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Bundled files
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  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by dpearson2699 on GitHub. Read the source before you install it.

Installation

Install the Dockkit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/dpearson2699/swift-ios-skills/tree/main/skills/dockkit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dpearson2699/swift-ios-skills.git /tmp/swift-ios-skills
mkdir -p .claude/skills
cp -r /tmp/swift-ios-skills/skills/dockkit .claude/skills/dockkit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dockkit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dockkit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dockkit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DockKit

Framework for integrating with motorized camera stands and gimbals that physically track subjects by rotating the iPhone. DockKit handles motor control, subject detection, and framing so camera apps get 360-degree pan and 90-degree tilt tracking with no additional code. Apps can override system tracking to supply custom observations, control motors directly, or adjust framing. iOS 17+, Swift 6.3.

Contents

Setup

Import DockKit:

swift
import DockKit

DockKit requires a physical DockKit-compatible accessory and a real device. The Simulator cannot connect to dock hardware.

DockKit itself requires no special entitlements or DockKit-specific Info.plist keys. Camera apps that use device cameras still need normal camera privacy handling, including NSCameraUsageDescription. The framework communicates with paired accessories automatically through the DockKit system daemon.

The app must use AVFoundation camera APIs. DockKit hooks into the camera pipeline to analyze frames for system tracking.

Discovering Accessories

Use DockAccessoryManager.shared to observe dock connections:

swift
import DockKit

func observeAccessories() async throws {
    for await stateChange in try DockAccessoryManager.shared.accessoryStateChanges {
        switch stateChange.state {
        case .docked:
            guard let accessory = stateChange.accessory else { continue }
            // Accessory is connected and ready
            configureAccessory(accessory)
        case .undocked:
            // iPhone removed from dock
            handleUndocked()
        @unknown default:
            break
        }
    }
}

accessoryStateChanges emits DockAccessory.StateChange values with state, accessory, and trackingButtonEnabled. Use accessory.identifier for the name, category, and UUID; hardware details are available via firmwareVersion and hardwareModel.

System Tracking

System tracking is DockKit's default mode. When enabled, the system analyzes camera frames through built-in ML inference, detects faces and bodies, and drives the motors to keep subjects in frame. Any app using AVFoundation camera APIs benefits automatically.

Enable or Disable

swift
// Enable system tracking (default)
try await DockAccessoryManager.shared.setSystemTrackingEnabled(true)

// Disable system tracking for custom control
try await DockAccessoryManager.shared.setSystemTrackingEnabled(false)

System tracking state does not persist across app termination, reboots, or background/foreground transitions. Set it explicitly whenever the app needs a specific value.

Tap to Select Subject

Allow users to select a specific subject by tapping:

swift
// Select the subject at a unit point in video-frame coordinates
try await accessory.selectSubject(at: CGPoint(x: 0.5, y: 0.5))

// Select specific subjects by identifier
try await accessory.selectSubjects([subjectUUID])

// Clear selection (return to automatic selection)
try await accessory.selectSubjects([])

Custom Tracking

Disable system tracking and provide your own observations when using custom ML models or the Vision framework.

Providing Observations

Construct DockAccessory.Observation values from your inference output and pass them to the accessory at 10-30 fps:

swift
import DockKit
import AVFoundation

func processFrame(
    _ sampleBuffer: CMSampleBuffer,
    accessory: DockAccessory,
    activeDevice: AVCaptureDevice
) async throws {
    let cameraInfo = DockAccessory.CameraInformation(
        captureDevice: activeDevice.deviceType,
        cameraPosition: activeDevice.position,
        orientation: .corrected,
        cameraIntrinsics: frameIntrinsics(from: sampleBuffer),
        referenceDimensions: frameDimensions(from: sampleBuffer)
    )

    let detection = try await detector.detect(sampleBuffer)
    let observationType: DockAccessory.Observation.ObservationType = switch detection.kind {
    case .face: .humanFace
    case .body: .humanBody
    case .object: .object
    }

    let observation = DockAccessory.Observation(
        identifier: detection.id,
        type: observationType,
        rect: detection.rect,       // normalized, lower-left origin
        faceYawAngle: detection.faceYawAngle
    )

    try await accessory.track([observation], cameraInformation: cameraInfo)
}

Observation Types

When reviewing custom tracking, explicitly choose among the only supported ObservationType cases: .humanFace, .humanBody, and .object. Do not answer with only .humanFace when body or object detections are possible.

The rect uses normalized coordinates with a lower-left origin (same coordinate system as Vision framework -- no conversion needed).

Camera Information

DockAccessory.CameraInformation describes the active camera; do not hardcode placeholder device, intrinsics, or frame-size values. Set orientation to .corrected when coordinates are already relative to the bottom-left corner. In review answers, reject opaque optional cameraInfo placeholders and show construction from the active AVCaptureDevice plus the current CMSampleBuffer.

Track variants also accept [AVMetadataObject] instead of observations. Use the image: CVPixelBuffer overloads when DockKit should combine observations or metadata with the captured image buffer; the image argument is required in those overloads.

Framing and Region of Interest

Framing Modes

Control how the system frames tracked subjects:

swift
try await accessory.setFramingMode(.automatic) // documented default
try await accessory.setFramingMode(.center)    // explicit opt-in
ModeBehavior
.automaticDocumented default; system decides optimal framing
.centerExplicit opt-in mode to keep subject centered
.leftFrame subject in left third
.rightFrame subject in right third

Default system behavior often centers the primary subject, but .center is never the default-like mode; .automatic is. Use .left or .right when graphic overlays occupy part of the frame.

Region of Interest

Constrain tracking to a specific area of the video frame:

swift
// Normalized coordinates, origin at upper-left
let squareRegion = CGRect(x: 0.25, y: 0.0, width: 0.5, height: 1.0)
try await accessory.setRegionOfInterest(squareRegion)

Use region of interest when cropping to a non-standard aspect ratio (e.g., square video for conferencing) so subjects stay within the visible area.

Motor Control

Disable system tracking before controlling motors directly.

Angular Velocity

Set continuous rotation speed in radians per second:

swift
import Spatial

// Pan right at 0.2 rad/s, tilt down at 0.1 rad/s
let velocity = Vector3D(x: 0.1, y: 0.2, z: 0.0)
try await accessory.setAngularVelocity(velocity)

// Stop all motion
try await accessory.setAngularVelocity(Vector3D())

Axes:

  • x -- pitch (tilt). Positive tilts down on iOS.
  • y -- yaw (pan). Positive pans right.
  • z -- roll (if supported by hardware).

Set Orientation

Move to a specific position over a duration:

swift
let target = Vector3D(x: 0.0, y: 0.5, z: 0.0)  // Yaw 0.5 rad
let progress = try accessory.setOrientation(
    target,
    duration: .seconds(2),
    relative: false
)

Also accepts Rotation3D for quaternion-based orientation. Set relative: true to move relative to the current position. The returned Progress object tracks completion.

Motion State

Monitor the accessory's current position and velocity:

swift
for await state in try accessory.motionStates {
    let positions = state.angularPositions   // Vector3D
    let velocities = state.angularVelocities // Vector3D
    let time = state.timestamp
    if let error = state.error {
        // Motor error occurred
    }
}

Setting Limits

Restrict range of motion and maximum speed per axis:

swift
let yawLimit = try DockAccessory.Limits.Limit(
    positionRange: -1.0 ..< 1.0,   // radians
    maximumSpeed: 0.5               // rad/s
)
let limits = DockAccessory.Limits(yaw: yawLimit, pitch: nil, roll: nil)
try accessory.setLimits(limits)

Animations

Built-in character animations that move the dock expressively:

swift
// Disable system tracking before animating
try await DockAccessoryManager.shared.setSystemTrackingEnabled(false)

let progress = try await accessory.animate(motion: .kapow)

// Wait for completion
while !progress.isFinished && !progress.isCancelled {
    try await Task.sleep(for: .milliseconds(100))
}

// Restore system tracking
try await DockAccessoryManager.shared.setSystemTrackingEnabled(true)
AnimationEffect
.yesNodding motion
.noShaking motion
.wakeupStartup-style motion
.kapowDramatic pendulum swing

Animations start from the accessory's current position and execute asynchronously. Always restore tracking state after completion. Keep animate(motion:) and setOrientation(_:duration:relative:) calls to no more than twice per second; higher call rates can throw .frameRateTooHigh.

Tracking State and Subject Selection

iOS 18+ exposes ML-derived tracking signals through the throwing trackingStates async sequence. Each state has time and trackedSubjects (.person or .object); persons include identifier, rect, speakingConfidence, lookingAtCameraConfidence, and saliencyRank (lower rank is more salient).

swift
if #available(iOS 18.0, *) {
    for await state in try accessory.trackingStates {
        var speaker: UUID?
        var engaged: UUID?
        var salient: (id: UUID, rank: Int)?
        for subject in state.trackedSubjects {
            switch subject {
            case .person(let person):
                let id = person.identifier, rect = person.rect
                let speaking = person.speakingConfidence
                let looking = person.lookingAtCameraConfidence
                let rank = person.saliencyRank
                updateSubjectOverlay(id: id, rect: rect)
                if let speaking, speaking > 0.7 { speaker = id }
                if let looking, looking > 0.7 { engaged = id }
                if let rank, salient == nil || rank < salient!.rank { salient = (id, rank) }
            case .object(let object):
                let id = object.identifier, rect = object.rect
                let rank = object.saliencyRank
                updateSubjectOverlay(id: id, rect: rect)
                if let rank, salient == nil || rank < salient!.rank { salient = (id, rank) }
            }
        }
        if let id = speaker ?? engaged ?? salient?.id { try await accessory.selectSubjects([id]) }
    }
}

Use selectSubjects(_:) to lock tracking by UUID; pass [] to return to automatic selection. Use speakingConfidence for speakers, lookingAtCameraConfidence for engagement, rect for overlays, and lower saliencyRank values as fallback. In review answers, consume lookingAtCameraConfidence and rect in code, not just prose.

Accessory Events

Physical buttons on the dock trigger events through the throwing accessoryEvents async sequence (iOS 17.4+):

swift
if #available(iOS 17.4, *) {
    for await event in try accessory.accessoryEvents {
        switch event {
        case .cameraShutter: break
        case .cameraFlip: break
        case .cameraZoom(factor: let factor): break
        case .button(id: let id, pressed: let pressed): break
        @unknown default: break
        }
    }
}

Third-party apps receive these events and implement behavior through AVFoundation.

Battery Monitoring

Monitor the dock's battery status through the throwing batteryStates async sequence (iOS 18+). A dock can report multiple batteries, each identified by name:

swift
if #available(iOS 18.0, *) {
    var batteryRows: [String: (Double, DockAccessory.BatteryChargeState, Bool)] = [:]
    for await battery in try accessory.batteryStates {
        batteryRows[battery.name] = (battery.batteryLevel, battery.chargeState, battery.lowBattery)
    }
}

Common Mistakes

DON'T: Control motors without disabling system tracking

swift
// WRONG -- system tracking fights manual commands
try await accessory.setAngularVelocity(velocity)

// CORRECT -- disable system tracking first
try await DockAccessoryManager.shared.setSystemTrackingEnabled(false)
try await accessory.setAngularVelocity(velocity)

DON'T: Assume tracking state persists across lifecycle events

swift
// WRONG -- state may have reset after backgrounding
func applicationDidBecomeActive() {
    // Assume custom tracking is still active
}

// CORRECT -- re-set tracking state on foreground
func applicationDidBecomeActive() {
    Task {
        try await DockAccessoryManager.shared.setSystemTrackingEnabled(false)
    }
}

DON'T: Call track() outside the recommended rate

swift
// WRONG -- calling once per second is too slow
try await accessory.track(observations, cameraInformation: cameraInfo)
// (called at 1 fps)

// CORRECT -- call at 10-30 fps
// Hook into AVCaptureVideoDataOutputSampleBufferDelegate for per-frame calls

DON'T: Spam orientation or animation calls

DockKit can throw .frameRateTooHigh if animate(motion:) or setOrientation(_:duration:relative:) is called more than twice per second. Set a trajectory, observe its Progress, and avoid tight command loops.

DON'T: Forget to restore tracking after animations

swift
// WRONG -- tracking stays disabled after animation
try await DockAccessoryManager.shared.setSystemTrackingEnabled(false)
let progress = try await accessory.animate(motion: .kapow)

// CORRECT -- restore tracking when animation completes
try await DockAccessoryManager.shared.setSystemTrackingEnabled(false)
let progress = try await accessory.animate(motion: .kapow)
while !progress.isFinished && !progress.isCancelled {
    try await Task.sleep(for: .milliseconds(100))
}
try await DockAccessoryManager.shared.setSystemTrackingEnabled(true)

DON'T: Use DockKit in Simulator

DockKit requires a physical DockKit-compatible accessory. Guard initialization and provide fallback behavior when no accessory is available.

Review Checklist

  • import DockKit present where needed
  • Subscribed to accessoryStateChanges to detect dock/undock events
  • Handled both .docked and .undocked states
  • System tracking disabled before custom tracking or motor control
  • System tracking restored after animations complete
  • Custom observations supplied at 10-30 fps
  • animate and setOrientation commands limited to 2 calls per second
  • Observation rect uses normalized coordinates (lower-left origin)
  • Camera information is built inline from the active AVCaptureDevice and current sample buffer
  • Observation type choice names .humanFace, .humanBody, and .object
  • @unknown default handled in all switch statements over DockKit enums
  • Motion limits set if restricting accessory range of motion
  • Tracking state re-applied after app returns to foreground
  • accessoryEvents guarded with #available(iOS 17.4, *)
  • trackingStates and batteryStates guarded with #available(iOS 18.0, *)
  • Battery UI preserves BatteryState.name for multi-battery docks
  • No DockKit code paths executed in Simulator builds

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dockkit AI skill do?

Control motorized camera docks and enable intelligent subject tracking using DockKit. Use when discovering DockKit-compatible accessories, implementing camera subject tracking for faces or bodies, controlling dock motors for pan and tilt, configuring framing behavior, setting regions of interest, or building video apps with automatic camera tracking.

Why use Dockkit on TypingMind?

Because you install it once and use it with any model. Dockkit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dockkit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dpearson2699/swift-ios-skills/tree/main/skills/dockkit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dockkit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dockkit?

As many as you like. As long as a model supports skills, you can use Dockkit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dockkit AI skill free?

It is published on GitHub by dpearson2699. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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