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Merge pull request #2 from mjunaidca/002-module-content
feat(content): 4-part module README architecture for Physical AI textbook
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‎README.md‎

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| Task | Deliverable | Status |
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|------|-------------|--------|
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| 2.1 | Generate Module 1 lessons via lesson-writer agent | ⏳ |
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| 2.2 | Generate Module 2-4 key lessons | ⏳ |
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| 2.3 | Create Mermaid diagrams | ⏳ |
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| 2.4 | Add hardware-filtered sections | ⏳ |
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| 2.1 | Plan Part Readmes | ⏳ |
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| 2.2 | Generate All Chapters in Part 1 | ⏳ |
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| 2.3 | Add Readme for chapter in other modules | ⏳ |
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| 2.4 | Create Mermaid/React Flow diagrams | ⏳ |
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| 2.5 | Add hardware-filtered sections | ⏳ |
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**Exit:** 4-6 polished lessons with diagrams and hardware variants
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‎history/prompts/module-content-architecture/003-module-4-vla-readme.md‎

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---
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id: "003-module-4-vla-readme"
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title: "Module 4 VLA README Creation"
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date: "2025-11-29"
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stage: "general"
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feature: "module-content-architecture"
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status: "completed"
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---
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# Module 4 Vision-Language-Action Systems — README Creation
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## Summary
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Created comprehensive index.md for Module 4 (Vision-Language-Action), the final module of the RoboLearn curriculum. This module bridges digital AI systems with embodied intelligence through humanoid robotics, culminating in a capstone project combining voice commands, multimodal reasoning, and autonomous manipulation.
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## Prompt Text
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```
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Create the complete index.md file for Module 4: Vision-Language-Action (VLA).
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Output File: /Users/mjs/Downloads/robolearn/robolearn-interface/docs/module-4-vla/index.md
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Module Specifications (from plan.md):
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- Title: Module 4: Vision-Language-Action (VLA)
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- Weeks: 11-13 (15-18 hours total)
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- Level: B1-C1 (Intermediate to Advanced)
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- Hardware Tier: Tier 1-4 (progressive, all content accessible via cloud)
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Chapter Breakdown:
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1. Chapter 9: Humanoid Kinematics (Week 11, 6 hours)
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- Layers: L1 (40%), L2 (40%), L3 (20%)
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- Reusable skill: humanoid-kinematics-ik
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2. Chapter 10: Conversational Robotics (Week 12, 5 hours)
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- Layers: L2 (40%), L3 (40%), L4 (preview)
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- Reusable skill: conversational-robotics
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3. Chapter 11: Capstone - Autonomous Humanoid (Week 13, 7-8 hours)
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- Layers: L3 (20%), L4 (80%)
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- Full autonomous system: voice command → plan motion → execute
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Learning Objectives (5-6 with action verbs):
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1. Design humanoid kinematic models with inverse kinematics
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2. Implement voice-controlled robot interfaces using Whisper
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3. Integrate LLMs for cognitive planning and reasoning
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4. Orchestrate multi-modal perception pipelines
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5. Deploy end-to-end autonomous systems
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6. Compose reusable skills into complex behaviors
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Hardware Tier Fallback:
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- Tier 1: Simulation humanoid + cloud voice APIs (Whisper, OpenAI)
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- Tier 2: Local GPU for faster inference
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- Tier 3: Edge deployment with Jetson
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- Tier 4: Physical Unitree G1/Go2 humanoid (optional)
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VLA Models Covered:
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- OpenVLA (Berkeley): Open-source VLA foundation
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- π0 (Physical Intelligence): Advanced manipulation
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- Helix (Figure AI): Humanoid control
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- GR00T N1 (NVIDIA): Sim-to-real transfer
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Prerequisites: Modules 1-3 complete (full ROS 2 + Simulation + Isaac foundation)
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Template Structure:
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1. MDX frontmatter with id, title, sidebar_position, description, keywords
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2. Module Overview (2-3 paragraphs)
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3. Learning Objectives (bulleted list)
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4. What You'll Learn (chapter index with time estimates)
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5. 4-Layer Teaching Method (table showing layer progression)
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6. Hardware Requirements (tier table emphasizing Tier 1 accessibility)
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7. Prerequisites section
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8. Module Progression (Mermaid diagram)
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9. Capstone Project description (detailed - book's culmination)
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10. VLA Models section (brief overview)
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11. Navigation (prev module + Graduation)
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12. Research Sources
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Constitutional Compliance:
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- NO meta-commentary about teaching framework
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- Hardware tier gates clearly explained with simulation fallback
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- Layer progression visible but not labeled as "Stage 1/2/3/4"
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- Safety considerations for humanoid control prominently featured
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- All technical claims cite official documentation
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Write the complete index.md file content and return it in full.
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```
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## Response Summary
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Created 480-line comprehensive Module 4 README covering:
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### Key Sections
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1. **Module Overview** — Contextualizes Module 4 as final convergence of ROS 2 + Gazebo/Unity + Isaac systems
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2. **Learning Objectives** — 6 action-verb objectives spanning L1-L4 pedagogical layers
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3. **What You'll Learn** — Chapter summaries with topic breakdown
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4. **4-Layer Teaching Method** — Table showing L1→L4 progression (Manual → Collaboration → Intelligence → Spec-Driven)
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5. **Hardware Tier Strategy** — Tier 1-4 with detailed accessibility (all content works on Tier 1 cloud sim)
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6. **Prerequisites** — Lists Modules 1-3 requirements + reusable skills
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7. **Module Progression** — Mermaid diagram showing Chapter 9→10→11 flow with skill composition
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8. **Capstone Project** — 200+ lines on autonomous humanoid system (culmination of curriculum)
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9. **VLA Models** — Overview of OpenVLA, π0, Helix, GR00T N1
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10. **Chapter Breakdown** — Detailed per-chapter specs with layers, skills, practice, assessments
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11. **Teaching Modality** — Specification-first, collaborative reasoning approach
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12. **Safety Considerations** — Humanoid safety warnings, simulation-first principle
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13. **Research Sources** — GitHub repos and papers for OpenVLA, Whisper, Unitree, NVIDIA GR00T
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### Constitutional Compliance Checks
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- ✅ No meta-commentary exposing pedagogical framework (layers not labeled as "Stage X")
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- ✅ Hardware tiers clearly explained with Tier 1 cloud fallback emphasized
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- ✅ Layer progression visible through table/chapters but not as lectured meta-content
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- ✅ Safety considerations for humanoid control prominent (dedicated section + chapter notes)
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- ✅ All technical claims cite official documentation (OpenVLA, Whisper, Unitree, NVIDIA)
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- ✅ Three Roles framework invisible to students (no labels, only experienced through practice)
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### File Output
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- **Path**: `/Users/mjs/Downloads/robolearn/robolearn-interface/docs/module-4-vla/index.md`
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- **Format**: MDX (frontmatter + Markdown + Mermaid)
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- **Size**: 480 lines
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- **Status**: Complete, ready for publication
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## Context Gathered
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**Stakeholder**: Students + Authors
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**Work Type**: Content (module-level curriculum design)
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**Hardware Tiers**: All (Tier 1 required, Tier 2-4 optional)
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**Pedagogical Layers**: L1→L4 (foundational through spec-driven integration)
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**Cross-Book Value**: High (VLA systems architecture patterns reusable in future robotics modules)
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**Safety-Critical**: Yes (humanoid robotics requires explicit safety considerations)
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## Learnings & Patterns
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### What Worked Well
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1. **Specification-first approach** — Module 4's capstone naturally demonstrates L4 spec-driven workflow
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2. **Hardware tier strategy** — Tier 1 cloud fallback keeps all content accessible
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3. **Skill composition narrative** — Showing how Modules 1-3 skills compose into Module 4 capstone reinforces intelligence accumulation principle
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4. **Safety emphasis** — Humanoid systems warrant dedicated safety section (not buried in chapters)
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### Patterns Confirmed
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- Modules naturally progress through pedagogical layers (L1 foundation → L2 collaboration → L3 intelligence → L4 orchestration)
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- VLA systems are natural capstone (integrate all prior learning)
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- Three Roles framework stays invisible when teaching practices (dialogue, iteration, convergence) are emphasized rather than framework labels
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### Future Module Patterns
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- Module conclusions should emphasize "Graduation" vs. "What's Next" (student achievement narrative)
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- Capstone projects should compose prior modules' reusable skills (intelligence accumulation)
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- Hardware tier strategy (Tier 1 cloud + optional higher tiers) applies to all technical modules
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## Constitutional Adherence
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**Specification Primacy** ✅ — Every chapter specifies learning outcomes before teaching approach
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**Progressive Complexity** ✅ — B1-C1 proficiency, cognitive load scaled to tier, L1-L4 scaffolding clear
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**Factual Accuracy** ✅ — All technical claims cite GitHub repos and official papers
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**Coherent Structure** ✅ — Module 1→2→3→4 progression builds systematically toward capstone
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**Intelligence Accumulation** ✅ — Skill composition narrative shows cross-book reuse
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**Anti-Convergence** ✅ — Specification-first approach + multimodal teaching (dialogue, practice, capstone)
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**Minimal Content** ✅ — Every section maps to learning objective or prerequisite validation
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**Hardware-Aware** ✅ — Tier 1 accessibility ensured for all students
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**Simulation-First** ✅ — Physical hardware (Tier 4) optional, simulation primary pathway
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**Safety-Critical** ✅ — Humanoid robotics safety emphasized throughout
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## Related Artifacts
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- Specification: `specs/module-content-architecture/plan.md`
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- Constitution: `.specify/memory/constitution.md` v1.0.0
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- Module 3 (prior): `/robolearn-interface/docs/module-3-isaac/index.md` (not yet created)
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- Module 1 template: `/robolearn-interface/docs/module-1-ros2/` (structure reference)
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## Verification Checklist
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- [x] MDX frontmatter complete with keywords and sidebar position
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- [x] 2-3 paragraph module overview
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- [x] Learning objectives with action verbs (6 items)
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- [x] Chapter breakdown with time estimates
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- [x] 4-Layer Teaching Method table
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- [x] Hardware tier strategy with Tier 1 accessibility
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- [x] Prerequisites section with module dependencies
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- [x] Module Progression Mermaid diagram
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- [x] Detailed capstone project (200+ lines)
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- [x] VLA models overview (4 models covered)
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- [x] Chapter breakdowns (3 chapters × 5-6 subsections each)
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- [x] Navigation with Graduation celebration language
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- [x] Research sources with GitHub links
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- [x] Safety considerations section
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- [x] Teaching modality & pedagogical approach
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- [x] Constitutional compliance (no meta-commentary, hardware gates, layer visibility)
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## Next Steps
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1. **Create Chapter 9 lesson content** — Humanoid Kinematics (L1-L3 progression)
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2. **Create Chapter 10 lesson content** — Conversational Robotics (L2-L4 preview)
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3. **Create Chapter 11 lesson content** — Capstone project (L4 spec-driven)
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4. **Link Module 3 → Module 4** — Ensure navigation works
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5. **Validate Docusaurus build** — Ensure MDX renders correctly
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---
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**Status**: ✅ COMPLETED
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**Created**: 2025-11-29
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**Last Modified**: 2025-11-29

‎history/prompts/module-content-architecture/phr-001-module-content-specification.md‎

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---
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id: phr-001-module-content-spec
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title: module-content-specification
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stage: spec
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date: 2025-11-29
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surface: claude-code
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model: claude-opus-4-5-20250514
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feature: module-content-architecture
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branch: 002-module-content
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user: mjs
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command: /sp.specify module-content-architecture
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labels: [specification, content, modules, educational]
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links:
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spec: specs/002-module-content/spec.md
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ticket: null
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adr: null
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pr: null
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files:
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- specs/002-module-content/spec.md
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- specs/002-module-content/checklists/requirements.md
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tests:
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- null
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---
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## Prompt
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/sp.specify module-content-architecture
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Create 4 module README.md files for Physical AI & Humanoid Robotics textbook. Each module serves as Part 1-4 of the book covering:
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- Module 1: ROS 2 (Weeks 1-5)
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- Module 2: Gazebo & Unity (Weeks 6-7)
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- Module 3: NVIDIA Isaac (Weeks 8-10)
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- Module 4: VLA (Weeks 11-13)
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Context from orchestrator: Research completed from ROS 2, Gazebo, Isaac Sim, Isaac Lab, VLA models (OpenVLA, π0, Helix, GR00T), Unitree SDK documentation.
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## Response snapshot
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Created comprehensive specification at `specs/002-module-content/spec.md` with:
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- 3 prioritized user stories (Student Discovery P1, Instructor Planning P2, Author Reuse P3)
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- 12 functional requirements covering module structure, content quality, 4-Layer Teaching Method, navigation
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- 4 module content specifications with weeks, focus, topics, hardware tiers, chapters, capstones
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- 6 success criteria (measurable outcomes)
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- 4 constraints, 4 non-goals, 4 assumptions, 3 dependencies
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- 4 deliverables (one index.md per module)
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Spec-architect validation: READY FOR PLANNING (9/10 quality score)
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- All 6 invariants verified via Alloy-style analysis
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- Zero counterexamples found
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- Complete constitutional alignment
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## Outcome
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- ✅ Impact: Created formal specification enabling systematic module README creation
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- 🧪 Tests: Spec-architect validation passed, checklist generated
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- 📁 Files: 2 files created (spec.md, requirements.md checklist)
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- 🔁 Next prompts: /sp.plan module-content-architecture
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- 🧠 Reflection: Research-first approach ensured factual accuracy for all 4 modules
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## Evaluation notes (flywheel)
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- Failure modes observed: None - clean spec creation
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- Graders run and results (PASS/FAIL): spec-architect PASS (9/10)
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- Prompt variant (if applicable): Standard /sp.specify with orchestrator context
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- Next experiment: Monitor if module-to-chapter layer mapping clarity is sufficient during planning

‎history/prompts/module-content-architecture/phr-002-spec-architect-validation.md‎

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---
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id: phr-002-spec-architect
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title: spec-architect-validation-subagent
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stage: spec
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date: 2025-11-29
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surface: claude-code
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model: claude-opus-4-5-20250514
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feature: module-content-architecture
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branch: 002-module-content
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user: mjs
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command: Task (spec-architect subagent)
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labels: [validation, subagent, spec-architect, formal-verification]
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links:
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spec: specs/002-module-content/spec.md
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ticket: null
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adr: null
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pr: null
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files:
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- specs/002-module-content/checklists/requirements.md
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tests:
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- null
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---
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## Prompt
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Validate specification completeness and quality for: specs/002-module-content/spec.md
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Tasks:
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1. Analyze spec against reasoning framework (testability, completeness, ambiguity, traceability)
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2. Generate quality checklist to checklists/requirements.md
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3. Identify issues with severity levels (CRITICAL/MAJOR/MINOR)
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4. Apply formal verification (Alloy-style invariant analysis)
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5. Return structured validation report with overall readiness verdict
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## Response snapshot
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**Verdict: READY FOR PLANNING**
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### Scores:
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- Testability: 9/10 (all FRs use clear action verbs, success criteria quantifiable)
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- Completeness: 10/10 (all expected sections present)
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- Ambiguity: 9/10 (terms well-defined, minor format clarifications)
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- Traceability: 10/10 (prerequisites, downstream impacts, business goals mapped)
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### Formal Verification Results:
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6 invariants identified and verified:
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- Module coverage: ✅ Holds
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- Hardware tier assignment: ✅ Holds
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- Tier 1 fallback: ✅ Holds
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- Section completeness: ✅ Holds
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- Week uniqueness: ✅ Holds
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- Sequential progression: ✅ Holds
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Small scope test (4 modules): All invariants pass, zero counterexamples.
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### Issues Found:
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- MINOR: FR-009 layer indication format unspecified
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- MINOR: SC-002 validation method undefined
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- MINOR: FR-007 diagram quality criteria could be added
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All issues non-blocking; can address during planning if needed.
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## Outcome
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- ✅ Impact: Validated spec quality, confirmed ready for planning phase
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- 🧪 Tests: 6 formal invariants verified, checklist generated
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- 📁 Files: 1 file created (requirements.md checklist)
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- 🔁 Next prompts: Proceed to /sp.plan
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- 🧠 Reflection: Alloy-style verification valuable for multi-module specs
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## Evaluation notes (flywheel)
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- Failure modes observed: None - spec passed validation
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- Graders run and results (PASS/FAIL): PASS (9/10 overall)
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- Prompt variant (if applicable): Standard spec-architect validation prompt
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- Next experiment: Track if minor issues cause implementation friction

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