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- 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 buil…
dpearson2699/swift-ios-skills/skills/dockkit/SKILL.md
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.
Decision brief
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…
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/dpearson2699/swift-ios-skills --skill "skills/dockkit"Inspect the Agent Skill "dockkit" from https://github.com/dpearson2699/swift-ios-skills/blob/90c9573272531337962fbb3505036d61ed23389a/skills/dockkit/SKILL.md at commit 90c9573272531337962fbb3505036d61ed23389a. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
DockKit requires a physical DockKit-compatible accessory and a real device. The Simulator cannot connect to dock hardware.
[ ] import DockKit present where needed
Use DockAccessoryManager.shared to observe dock connections:
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.
System tracking state does not persist across app termination, reboots, or background/foreground transitions. Set it explicitly whenever the app needs a specific value.
Permission review
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 81/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 933 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
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.
Import DockKit:
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.
Use DockAccessoryManager.shared to observe dock connections:
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 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 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.
Allow users to select a specific subject by tapping:
// 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([])
Disable system tracking and provide your own observations when using custom ML models or the Vision framework.
Construct DockAccessory.Observation values from your inference output
and pass them to the accessory at 10-30 fps:
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)
}
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).
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.
Control how the system frames tracked subjects:
try await accessory.setFramingMode(.automatic) // documented default
try await accessory.setFramingMode(.center) // explicit opt-in
| Mode | Behavior |
|---|---|
.automatic | Documented default; system decides optimal framing |
.center | Explicit opt-in mode to keep subject centered |
.left | Frame subject in left third |
.right | Frame 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.
Constrain tracking to a specific area of the video frame:
// 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.
Disable system tracking before controlling motors directly.
Set continuous rotation speed in radians per second:
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).Move to a specific position over a duration:
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.
Monitor the accessory's current position and velocity:
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
}
}
Restrict range of motion and maximum speed per axis:
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)
Built-in character animations that move the dock expressively:
// 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)
| Animation | Effect |
|---|---|
.yes | Nodding motion |
.no | Shaking motion |
.wakeup | Startup-style motion |
.kapow | Dramatic 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.
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).
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.
Physical buttons on the dock trigger events through the throwing
accessoryEvents async sequence (iOS 17.4+):
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.
Monitor the dock's battery status through the throwing batteryStates async
sequence (iOS 18+). A dock can report multiple batteries, each identified by
name:
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)
}
}
// 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)
// 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)
}
}
// 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
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.
// 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)
DockKit requires a physical DockKit-compatible accessory. Guard initialization and provide fallback behavior when no accessory is available.
import DockKit present where neededaccessoryStateChanges to detect dock/undock events.docked and .undocked statesanimate and setOrientation commands limited to 2 calls per secondrect uses normalized coordinates (lower-left origin)AVCaptureDevice and current sample buffer.humanFace, .humanBody, and .object@unknown default handled in all switch statements over DockKit enumsaccessoryEvents guarded with #available(iOS 17.4, *)trackingStates and batteryStates guarded with #available(iOS 18.0, *)BatteryState.name for multi-battery docks