` in the scaffold 3. Update the synthesis section with cross-lens convergence (read all cross-lens notes) 4. Write the final HTML to `docs/perspectives-vlm-primary-hybrid-nav-v2.html` 5. Generate `docs/perspectives-vlm-primary-hybrid-nav-v2-sections.json` from TTS text files ### Step 3: Version linking 1. Update v1 HTML to add forward link to v2 2. Ensure v2 has backward link to v1 ### Step 4: QA + Open 1. Open v2 in browser 2. Test click-to-reveal, theme toggle, TTS buttons, scroll animations 3. Fix any bugs ## Key Findings from Previous Session's 26 Agents (for Synthesis) These convergence points emerged across multiple lenses: 1. **WiFi is the Achilles' heel** (Lenses 04, 10, 13, 25) — cliff edge at 100ms, no graceful degradation 2. **Multi-query pipeline is highest-value lowest-risk** (Lenses 01, 17, 18, 23) — one-line code change, zero hardware 3. **Semantic map + voice = killer app** (Lenses 06, 16, 20, 21) — "Annie, what's in the kitchen?" 4. **Glass door problem unsolved** (Lenses 10, 11, 12, 13) — both sensors fail on transparent surfaces 5. **Transfer potential is massive** (Lenses 05, 17, 19) — NavCore open-source middleware opportunity 6. **Research describes Waymo then does opposite** (Lens 14) — VLM-primary vs lidar-primary inconsistency 7. **Last 40% accuracy costs 10x hardware** (Lens 15) — 3 constraints relaxable now for <$200 8. **Build the map to remember, not to navigate** (Lens 16) — Annie's value is persistent spatial witness 9. **Voice-to-ESTOP gap** (Lens 21) — Mom can't say "Stop!" with <5s latency 10. **Bypass text-language layer** (Lens 26) — 3 independent question-types converge on this 11. **3 neuroscience mechanisms untried** (Lens 08) — saccadic suppression, predictive coding, hippocampal replay 12. **18-gap Ghost Inventory** (Lens 24) — camera-lidar calibration is hidden prerequisite ## Agent Prompt Template (proven, use as-is) For each lens, the prompt is: ``` You are a deep-analysis specialist for Lens {N}: {LENS_NAME}. Read these files: 1. docs/research-perspectives-catalog.html — find lens {N}, extract core question, all driving questions, "Why this works" 2. docs/RESEARCH-VLM-PRIMARY-HYBRID-NAV.md — the research 3. docs/perspectives-vlm-primary-hybrid-nav-v2-scaffold.html — read the lens-{NN} section for context This lens uses the {PATTERN} visual pattern. YOUR TASK: [lens-specific instructions from skill file] After generating, WRITE outputs to: - docs/.perspectives-build/lens-{NN}.html (the complete

HTML) - docs/.perspectives-build/lens-{NN}-tts.txt (plain text for TTS) - docs/.perspectives-build/lens-{NN}-crosslens.txt (cross-lens notes) ``` ## Lens → Visual Pattern Mapping (for agent prompts) | Lens | Name | Pattern | |------|------|---------| | 01 | First Principles X-Ray | LAYERS | | 02 | Abstraction Elevator | LAYERS | | 03 | Dependency Telescope | TREE | | 04 | Sensitivity Surface | BARS | | 05 | Evolution Timeline | TIMELINE | | 06 | Second-Order Effects | TREE | | 07 | Landscape Map | SCATTER | | 08 | Analogy Bridge | COMPARE | | 09 | Tradeoff Radar | RADAR (SVG) | | 10 | Failure Pre-mortem | TIMELINE | | 11 | Red Team Brief | CARDS | | 12 | Anti-Pattern Gallery | COMPARE | | 13 | Constraint Analysis | MATRIX | | 14 | The Inversion | COMPARE | | 15 | Constraint Relaxation | COMPARE | | 16 | Composition Lab | MATRIX | | 17 | Transfer Matrix | CARDS | | 18 | Decision Tree | FLOW | | 19 | Scale Microscope | BARS | | 20 | Day-in-the-Life | TIMELINE | | 21 | Stakeholder Kaleidoscope | CARDS | | 22 | Learning Staircase | LAYERS | | 23 | Energy Landscape | BARS | | 24 | Gap Finder | CHECKLIST | | 25 | Blind Spot Scan | CARDS | | 26 | Question Horizon | TREE |