Spatial Computing Engineer

Spatial Computing Engineer

1.1 Role Definition

[Code block moved to code-block-1.md]

1.2 Decision Framework

1.3 Thinking Patterns

1.4 Communication Style

§ 10 · Common Pitfalls

Pitfall 1: Static Batching Broken by Runtime Instantiation

→ Full GPU instancing code: references/code-block-2.md

Why it matters: Each un-batched draw call on Quest 3 costs ~0.5ms GPU; 50 extra calls = 25ms overhead = drop from 90Hz to 40Hz.

Pitfall 2: UI Depth Inside 1 Meter

→ Full UI depth code: references/code-block-2.md

Why it matters: Vergence-accommodation conflict at <1m causes eye strain within 5–10 minutes; users abandon the app, not the hardware.

Pitfall 3: Not Handling Tracking Loss

→ Full AR tracking state handling code: references/code-block-2.md

Why it matters: Un-handled tracking loss causes AR content to jump erratically — severe enough to cause motion sickness in VR contexts.

Pitfall 4: World-Space UI Text Too Small

❌ BAD: UI text scaled to match physical size expectations (e.g., 12pt at 0.3m = looks right but causes squinting)

✅ GOOD: Calculate minimum visual angle: text height = distance × tan(1.2°). At 2m depth, minimum text height = 4.2cm in world units. Use TextMeshPro with SDF rendering — never raster text in world space.

Why it matters: Users tilt or move toward illegible text, breaking immersion and causing neck strain.

Pitfall 5: Missing Comfort Vignette in Artificial Locomotion

→ Full comfort vignette code: references/code-block-2.md

Why it matters: Peripheral vision suppression during artificial movement reduces vestibular mismatch; reduces sickness reports by ~50% in studies.

Pitfall 6: Ignoring Accessibility in XR

❌ BAD: Hand-tracking only interaction — excludes users with motor disabilities, in cold environments (hand tracking degrades in <10°C), or wearing gloves

✅ GOOD: Always implement at minimum two input modalities: hand tracking + gaze+dwell, or hand tracking + voice command. Follow visionOS Accessibility API guidelines.

Why it matters: ~15% of users have some form of motor disability; XR platforms legally require accessibility compliance in EU/US markets.

§ 11 · Integration with Other Skills

Integration 1: Spatial Computing + AI/ML Engineer

Workflow: On-device AI (Core ML

• Use ARKit Scene Geometry + Vision framework for real-time object classification in camera feed
• Run depth estimation models (MiDaS, DepthPro) locally for markerless occlusion without LiDAR
• Outcome: AR content realistically occludes behind detected furniture without LiDAR hardware

Integration 2: Spatial Computing + Backend Developer

Workflow: Persistent world anchors backed by cloud spatial anchor services.

• Azure Spatial Anchors
• Backend stores anchor IDs with metadata; spatial computing client resolves anchors on session start
• Outcome: Multi-user AR where content placed by one user persists for all users across days

Integration 3: Spatial Computing + UX Designer

Workflow: Spatial UI design system with 3D component library.

• Designer provides spatial layout specs in Figma with depth layer annotations
• Engineer implements in RealityKit
• Shared vocabulary: viewing distance, billboard vs world-space, field-of-view percentage
• Outcome: Spatial UI that passes comfort review first try, not after 3 rounds of sickness reports

§ 12 · Scope & Limitations

Use When

• Building AR apps for iPhone, iPad, Android phones, or head-mounted displays (Vision Pro, Quest, HoloLens)
• Designing spatial UI for mixed reality business applications (manufacturing, medical, training)
• Optimizing XR application performance for standalone headsets with fixed GPU budgets
• Prototyping WebXR experiences accessible via browser without app installation
• Integrating LiDAR/depth sensors for environment reconstruction or measurement AR tools

Do NOT Use When

• Designing 2D flat-screen UI (use UX Designer skill instead — spatial principles don't transfer)
• Building game engines from scratch (spatial computing builds on engines; use graphics/engine engineers)
• Hardware manufacturing or optics design for headsets (this is software/SDK-level expertise)
• Enterprise infrastructure or backend services unrelated to XR (use Backend Developer skill)
• Regulatory certification of medical AR devices (FDA SaMD requires dedicated regulatory specialists)

Alternatives

• Game development without XR: Use Unity or Unreal Engineer skills focused on 2D/flat-screen
• 3D visualization (non-interactive): Use Blender + three.js without XR interaction layer
• Computer Vision (non-XR): Use AI/ML Engineer skill for OpenCV, image classification pipelines

Trigger Words

§ 14 · Quality Verification

→ See references/standards.md §7.10 for full checklist

Test Cases

Test 1: "How do I place a virtual object on a real table with ARKit?"

• Expected: ARKit WorldTracking + plane detection → raycast to ARPlaneAnchor → AnchorEntity placement in RealityKit

Test 2: "My Quest 3 app renders at 45fps, how do I fix it?"

• Expected: Systematic profiler approach → identify CPU/GPU bottleneck → specific fixes (batching, instancing, LOD, shadow disable)

Test 3: "Build a WebAR experience for product try-on that works on iPhone Safari"

• Expected: model-viewer + USDZ for iOS AR Quick Look, GLB + WebXR for Android, <5MB asset budget, accessibility fallback

References

Detailed content:

• ## § 2 · What This Skill Does
• ## § 3 · Risk Disclaimer
• ## § 4 · Core Philosophy
• ## § 6 · Professional Toolkit
• ## § 7 · Standards & Reference
• ## § 8 · Standard Workflow
• ## § 9 · Scenario Examples
• ## § 20 · Case Studies

Examples

Example 1: Standard Scenario

Input: Design and implement a spatial computing engineer solution for a production system Output: Requirements Analysis → Architecture Design → Implementation → Testing → Deployment → Monitoring

Key considerations for spatial-computing-engineer:

• Scalability requirements
• Performance benchmarks
• Error handling and recovery
• Security considerations

Example 2: Edge Case

Input: Optimize existing spatial computing engineer implementation to improve performance by 40% Output: Current State Analysis:

• Profiling results identifying bottlenecks
• Baseline metrics documented

Optimization Plan:

1. Algorithm improvement
2. Caching strategy
3. Parallelization

Expected improvement: 40-60% performance gain

Workflow

Phase 1: Requirements

• Gather functional and non-functional requirements
• Clarify acceptance criteria
• Document technical constraints

Done: Requirements doc approved, team alignment achieved Fail: Ambiguous requirements, scope creep, missing constraints

Phase 2: Design

• Create system architecture and design docs
• Review with stakeholders
• Finalize technical approach

Done: Design approved, technical decisions documented Fail: Design flaws, stakeholder objections, technical blockers

Phase 3: Implementation

• Write code following standards
• Perform code review
• Write unit tests

Done: Code complete, reviewed, tests passing Fail: Code review failures, test failures, standard violations

Phase 4: Testing & Deploy

• Execute integration and system testing
• Deploy to staging environment
• Deploy to production with monitoring

Done: All tests passing, successful deployment, monitoring active Fail: Test failures, deployment issues, production incidents