Covers 13+ Vision APIs across subject lifting, hand/body pose, person segmentation, text OCR, barcode detection, and document scanning with decision trees for choosing the right tool
Includes 15 production patterns: combining APIs to exclude hands from objects, real-time gesture recognition, multi-person segmentation, fitness action classif
Fetches axiom-vision from charleswiltgen/axiom and configures it for Cursor.
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Guides you through implementing computer vision: subject segmentation, hand/body pose detection, person detection, text recognition, barcode detection, document scanning, and combining Vision APIs to solve complex problems.
When to Use This Skill
Use when you need to:
☑ Isolate subjects from backgrounds (subject lifting)
☑ Detect and track hand poses for gestures
☑ Detect and track body poses for fitness/action classification
☑ Segment multiple people separately
☑ Exclude hands from object bounding boxes (combining APIs)
☑ Choose between VisionKit and Vision framework
☑ Combine Vision with CoreImage for compositing
☑ Decide which Vision API solves your problem
☑ Recognize text in images (OCR)
☑ Detect barcodes and QR codes
☑ Scan documents with perspective correction
☑ Extract structured data from documents (iOS 26+)
☑ Build live scanning experiences (DataScannerViewController)
Example Prompts
"How do I isolate a subject from the background?"
"I need to detect hand gestures like pinch"
"How can I get a bounding box around an object without including the hand holding it?"
"Should I use VisionKit or Vision framework for subject lifting?"
"How do I segment multiple people separately?"
"I need to detect body poses for a fitness app"
"How do I preserve HDR when compositing subjects on new backgrounds?"
"How do I recognize text in an image?"
"I need to scan QR codes from camera"
"How do I extract data from a receipt?"
"Should I use DataScannerViewController or Vision directly?"
"How do I scan documents and correct perspective?"
"I need to extract table data from a document"
Red Flags
Signs you're making this harder than it needs to be:
❌ Manually implementing subject segmentation with CoreML models
❌ Using ARKit just for body pose (Vision works offline)
❌ Writing gesture recognition from scratch (use hand pose + simple distance checks)
❌ Processing on main thread (blocks UI - Vision is resource intensive)
❌ Training custom models when Vision APIs already exist
❌ Not checking confidence scores (low confidence = unreliable landmarks)
❌ Forgetting to convert coordinates (lower-left origin vs UIKit top-left)
❌ Building custom text recognizer when VNRecognizeTextRequest exists
❌ Using AVFoundation + Vision when DataScannerViewController suffices
❌ Processing every camera frame for scanning (skip frames, use region of interest)
❌ Enabling all barcode symbologies when you only need one (performance hit)
❌ Ignoring RecognizeDocumentsRequest when you need table/list structure (iOS 26+)
Mandatory First Steps
Before implementing any Vision feature:
1. Choose the Right API (Decision Tree)
What do you need to do?
┌─ Isolate subject(s) from background?
│ ├─ Need system UI + out-of-process → VisionKit
│ │ └─ ImageAnalysisInteraction (iOS/iPadOS)
│ │ └─ ImageAnalysisOverlayView (macOS)
│ ├─ Need custom pipeline / HDR / large images → Vision
│ │ └─ VNGenerateForegroundInstanceMaskRequest
│ └─ Need to EXCLUDE hands from object → Combine APIs
│ └─ Subject mask + Hand pose + custom masking (see Pattern 1)
│
├─ Segment people?
│ ├─ All people in one mask → VNGeneratePersonSegmentationRequest
│ └─ Separate mask per person (up to 4) → VNGeneratePersonInstanceMaskRequest
│
├─ Detect hand pose/gestures?
│ ├─ Just hand location → VNDetectHumanRectanglesRequest
│ └─ 21 hand landmarks → VNDetectHumanHandPoseRequest
│ └─ Gesture recognition → Hand pose + distance checks
│
├─ Detect body pose?
│ ├─ 2D normalized landmarks → VNDetectHumanBodyPoseRequest
│ ├─ 3D real-world coordinates → VNDetectHumanBodyPose3DRequest
│ └─ Action classification → Body pose + CreateML model
│
├─ Face detection?
│ ├─ Just bounding boxes → VNDetectFaceRectanglesRequest
│ └─ Detailed landmarks → VNDetectFaceLandmarksRequest
│
├─ Person detection (location only)?
│ └─ VNDetectHumanRectanglesRequest
│
├─ Recognize text in images?
│ ├─ Real-time from camera + need UI → DataScannerViewController (iOS 16+)
│ ├─ Processing captured image → VNRecognizeTextRequest
│ │ ├─ Need speed (real-time camera) → recognitionLevel = .fast
│ │ └─ Need accuracy (documents) → recognitionLevel = .accurate
│ └─ Need structured documents (iOS 26+) → RecognizeDocumentsRequest
│
├─ Detect barcodes/QR codes?
│ ├─ Real-time camera + need UI → DataScannerViewController (iOS 16+)
│ └─ Processing image → VNDetectBarcodesRequest
│
└─ Scan documents?
├─ Need built-in UI + perspective correction → VNDocumentCameraViewController
├─ Need structured data (tables, lists) → RecognizeDocumentsRequest (iOS 26+)
└─ Custom pipeline → VNDetectDocumentSegmentationRequest + perspective correction
2. Set Up Background Processing
NEVER run Vision on main thread:
let processingQueue =DispatchQueue(label:"com.yourapp.vision", qos:.userInitiated)processingQueue.async{do{let request =VNGenerateForegroundInstanceMaskRequest()let handler =VNImageRequestHandler(cgImage: image)try handler.perform([request])// Process observations...DispatchQueue.main.async{// Update UI}}catch{// Handle error}}
3. Choose the Right Request Handler
Processing video frames? Use VNSequenceRequestHandler (maintains inter-frame state for temporal smoothing). For single images, use VNImageRequestHandler. Creating a new VNImageRequestHandler per frame discards temporal context and causes jittery results. See axiom-vision-ref for full comparison and code examples.
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
