Fetches vision-framework from dpearson2699/swift-ios-skills and configures it for Cursor.
3
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│ ── Universal (.agents/skills) ────────────────
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4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/vision-framework
Restart Cursor to activate vision-framework. Access via /vision-framework in your agent's command palette.
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Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Detect text, faces, barcodes, objects, and body poses in images and video using
on-device computer vision. Patterns target iOS 26+ with Swift 6.3,
backward-compatible where noted.
The modern API uses the ImageProcessingRequest protocol. Each request type
has a perform(on:orientation:) method that accepts CGImage, CIImage,
CVPixelBuffer, CMSampleBuffer, Data, or URL. Most requests are
structs; stateful requests for video tracking (e.g., TrackObjectRequest,
TrackRectangleRequest, DetectTrajectoriesRequest) are final classes.
Request Pattern (Modern API)
All modern Vision requests follow the same pattern: create a request struct,
call perform(on:), and handle the typed result.
var request =RecognizeTextRequest()request.recognitionLevel =.accurate // .fast for real-timerequest.recognitionLanguages =[Locale.Language(identifier:"en-US"),Locale.Language(identifier:"fr-FR"),]request.usesLanguageCorrection =truerequest.customWords =["SwiftUI","Xcode"]// domain-specific termslet observations =tryawait request.perform(on: cgImage)for observation in observations {guardlet candidate = observation.topCandidates(1).first else{continue}let text = candidate.string
let confidence = candidate.confidence // 0.0 ... 1.0let bounds = observation.boundingBox // normalized coordinates}
Legacy: VNRecognizeTextRequest
let request =VNRecognizeTextRequest()request.recognitionLevel =.accurate
request.recognitionLanguages =["en-US","fr-FR"]request.usesLanguageCorrection =true
Key differences: Modern API uses Locale.Language for languages; legacy
uses string identifiers. Both support .accurate (best quality) and .fast
(real-time suitable) recognition levels.
Face Detection
Detect face rectangles, landmarks (eyes, nose, mouth), and capture quality.
// Modern APIlet faceRequest =DetectFaceRectanglesRequest()let faces =tryawait faceRequest.perform(on: cgImage)for face in faces {let boundingBox = face.boundingBox // normalized CGRectlet roll = face.roll // Measurement<UnitAngle>let yaw = face.yaw // Measurement<UnitAngle>}// Landmarks (eyes, nose, mouth contours)var landmarkRequest =DetectFaceLandmarksRequest()let landmarkFaces =tryawait landmarkRequest.perform(on: cgImage)for face in landmarkFaces {let landmarks = face.landmarks
let leftEye = landmarks?.leftEye?.normalizedPoints
let nose = landmarks?.nose?.normalizedPoints
}
Coordinate System
Vision uses a normalized coordinate system with origin at the bottom-left.
Convert to UIKit (top-left origin) before display:
›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
