feat: add Atomizer HQ multi-agent cluster infrastructure
- 8-agent OpenClaw cluster (Manager, Tech-Lead, Secretary, Auditor, Optimizer, Study-Builder, NX-Expert, Webster) - Orchestration engine: orchestrate.py (sync delegation + handoffs) - Workflow engine: YAML-defined multi-step pipelines - Agent workspaces: SOUL.md, AGENTS.md, MEMORY.md per agent - Shared skills: delegate, orchestrate, atomizer-protocols - Capability registry (AGENTS_REGISTRY.json) - Cluster management: cluster.sh, systemd template - All secrets replaced with env var references
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hq/workspaces/nx-expert/INTROSPECTION_EXECUTIVE_SUMMARY.md
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# Model Introspection — Executive Summary
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**Author:** NX Expert 🖥️
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**Date:** 2026-02-14
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**Model:** Codex (Claude 3.7 Sonnet)
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---
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## What You Asked For
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> "A deep and powerful report on what should contain a full introspection run when doing an optimization setup — the full plan and coarse idea on how to extract with MCP deep knowledge."
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---
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## What You're Getting
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**Three deliverables:**
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1. **MODEL_INTROSPECTION_RESEARCH.md** (23 KB)
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- Comprehensive framework design
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- JSON schema for full data capture
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- 6-phase implementation roadmap (10-13 days)
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- Example outputs for bracket study
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2. **INTROSPECTION_API_GUIDE.md** (25 KB)
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- Copy-paste ready NXOpen Python patterns
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- pyNastran BDF/OP2 extraction code
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- All 5 introspection layers covered
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- Production-ready code examples
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3. **This summary** (you are here)
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---
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## The Big Picture
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### Current State
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Atomizer has **basic introspection** (expressions, mass, materials) but **lacks deep knowledge**:
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- ❌ No mesh quality metrics
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- ❌ No BC/load details (magnitudes, DOFs, targets)
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- ❌ No solver config (solution sequences, output requests)
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- ❌ No parametric dependencies (what drives what)
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- ❌ No baseline results context
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### Proposed Framework
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**Five-layer introspection** that captures the **full data picture**:
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```
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Layer 1: GEOMETRIC PARAMETERS
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→ Expressions, features, sketches, mass, materials
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→ What can be optimized?
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Layer 2: FEA MODEL STRUCTURE
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→ Mesh (quality, elements, nodes), materials, properties
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→ What's the baseline mesh health?
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Layer 3: SOLVER CONFIGURATION
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→ Solutions, subcases, BCs, loads, output requests
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→ What physics governs the problem?
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Layer 4: DEPENDENCIES & RELATIONSHIPS
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→ Expression graph, feature tree, BC-mesh links
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→ What affects what? Sensitivities?
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Layer 5: BASELINE RESULTS
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→ Pre-opt stress, displacement, frequency
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→ Where are we starting from?
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```
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---
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## JSON Schema Preview
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```json
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{
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"introspection_version": "1.0.0",
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"model_id": "bracket_v2",
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"geometric_parameters": {
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"expressions": [
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{
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"name": "thickness",
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"value": 3.0,
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"units": "mm",
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"driven_features": ["Extrude(2)", "Shell(1)"],
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"dependencies": ["p47"]
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}
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],
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"mass_properties": {"mass_kg": 0.234}
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},
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"fea_model": {
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"mesh": {
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"total_elements": 8234,
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"element_types": {"CTETRA": 8234},
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"quality_metrics": {
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"aspect_ratio": {"average": 2.45, "max": 8.34}
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}
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}
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},
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"solver_configuration": {
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"solutions": [
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{
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"name": "Solution 1",
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"solution_sequence": "SOL 101",
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"boundary_conditions": {
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"constraints": [...],
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"loads": [...]
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}
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}
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]
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},
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"dependencies": {
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"expression_graph": {
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"nodes": [...],
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"edges": [{"from": "thickness", "to": "p47"}]
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}
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},
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"baseline_results": {
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"displacement": {"max_mm": 2.34},
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"stress": {"max_MPa": 145.6}
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},
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"optimization_context": {
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"potential_design_variables": [
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{"name": "thickness", "suggested_bounds": [1.5, 6.0]}
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],
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"potential_objectives": [
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{"type": "minimize", "metric": "mass"}
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],
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"recommended_study_type": "single_objective_mass_min"
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}
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}
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```
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---
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## What This Unlocks
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### For Atomizer HQ
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1. **Study Builder**
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- Auto-suggest design variables from expression analysis
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- Provide sensible parameter bounds
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- Validate study setup before expensive FEA runs
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2. **Optimizer**
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- Understand what constraints/objectives make sense
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- Estimate optimization difficulty (mesh size, parameter count)
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- Know expected FEA runtime
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3. **Reporter**
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- Baseline state for comparison (before/after)
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- Sensitivity context (which parameters matter most)
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4. **Manager**
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- Study complexity assessment
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- Resource allocation decisions
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### For You (Antoine)
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**Conversational optimization setup:**
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```
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You: "What can I optimize in this bracket?"
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Atomizer: "I see 3 user-defined expressions:
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- thickness (3.0 mm) → drives mass and stiffness
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- width (50.0 mm) → affects load path
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- height (100.0 mm) → affects bending stiffness
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Baseline: 0.234 kg, max stress 145.6 MPa (MoS 0.89)
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I recommend:
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- Objective: Minimize mass
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- Variables: thickness [1.5-6.0], width [30-70], height [80-120]
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- Constraint: Max stress < 200 MPa (SF 1.5)
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Estimated runtime: ~45 sec/trial
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Ready to proceed?"
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```
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---
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## Implementation Roadmap
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### Phase 1: Enhanced Part Introspection (1-2 days)
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- ✅ Expression dependency parsing
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- ✅ Feature parameter extraction
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- ✅ Parametric relationship graph
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### Phase 2: FEM Model Deep Dive (2-3 days)
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- ✅ pyNastran BDF parsing (elements, materials, properties)
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- ✅ Mesh quality audit
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- ✅ Element type distribution
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### Phase 3: Solver Configuration (2-3 days)
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- ✅ BDF subcase extraction
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- ✅ BC/load detail parsing (magnitudes, DOFs)
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- ✅ Output request cataloging
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### Phase 4: Dependency Mapping (2 days)
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- ✅ Expression graph construction
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- ✅ Feature tree traversal
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- ✅ Mesh-geometry linking
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### Phase 5: Baseline Results (1 day)
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- ✅ Aggregate existing Atomizer extractors
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- ✅ Compute margins of safety
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### Phase 6: Master Orchestrator (2 days)
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- ✅ Single-command full introspection
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- ✅ JSON schema validation
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- ✅ Human-readable summary report
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**Total:** 10-13 days
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---
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## Extraction Methods Summary
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| Layer | Primary Tool | API/Library |
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|-------|-------------|-------------|
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| Geometric | `introspect_part.py` (enhanced) | NXOpen Python |
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| FEA Model | `introspect_fem.py` (new) | pyNastran BDF |
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| Solver Config | `introspect_sim.py` (enhanced) + BDF | NXOpen + pyNastran |
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| Dependencies | `build_dependency_graph.py` (new) | NXOpen + graph algorithms |
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| Baseline | Existing Atomizer extractors | pyNastran OP2 |
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**Orchestrator:** `run_full_introspection.py` (new)
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---
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## Example Output
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**Input:**
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```bash
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python run_full_introspection.py bracket.prt bracket_sim1.sim
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```
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**Output:**
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- `model_introspection_FULL.json` — Complete data (all 5 layers)
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- `introspection_summary.md` — Human-readable report
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**Summary snippet:**
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```
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INTROSPECTION SUMMARY — bracket_v2
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===================================
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DESIGN SPACE
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- 3 user-defined expressions detected
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- Recommended DVs: thickness, width, height
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- Suggested bounds: thickness [1.5-6.0] mm
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FEA MODEL
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- Mesh: 8,234 CTETRA, avg aspect ratio 2.45 (good)
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- Material: Al 6061-T6, E=68.9 GPa
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PHYSICS
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- Analysis: SOL 101 (Static)
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- BCs: 1 fixed face, 1000 N force
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- Baseline: Max disp 2.34 mm, max stress 145.6 MPa
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OPTIMIZATION CONTEXT
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- Recommended: Minimize mass
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- Constraint: Max stress < 200 MPa
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- Runtime: ~45 sec/trial
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```
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---
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## Next Steps
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### Option A: Full Implementation (10-13 days)
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Implement all 6 phases. You get the complete framework.
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### Option B: Phased Rollout
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1. **Phase 1-2 first** (3-5 days) → Enhanced part + FEM introspection
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2. Test on existing studies (M1 mirror, bracket, beam)
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3. Iterate based on real usage
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4. Add Phases 3-6 as needed
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### Option C: Pilot Study
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1. Pick one study (e.g., bracket)
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2. Implement just enough to generate full introspection JSON
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3. Validate that Atomizer HQ can consume it
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4. Expand coverage
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**My Recommendation:** **Option B** — Start with enhanced part + FEM introspection. These give you 80% of the value (design variables, mesh health, baseline mass/stress) with 40% of the effort.
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---
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## Questions for You
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1. **Priority?** Which layers matter most right now?
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- Geometric parameters? (Design variables, bounds)
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- FEA model? (Mesh quality, materials)
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- Solver config? (BCs, loads, subcases)
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- Dependencies? (What affects what)
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- Baseline results? (Pre-opt stress/displacement)
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2. **Timeline?** When do you need this?
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- ASAP (start with phased rollout)
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- Can wait (full implementation in 2 weeks)
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3. **Use case?** What's the first study you want to introspect?
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- M1 mirror? (complex optics optimization)
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- Bracket? (simple structural)
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- Hydrotech beam? (recent project)
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4. **Integration?** How should Atomizer HQ consume this JSON?
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- Study setup validation tool
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- Auto-documentation generator
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- Knowledge base population
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- All of the above
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---
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## What to Read Next
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### If you want the **big picture:**
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→ Read `MODEL_INTROSPECTION_RESEARCH.md`
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- Section 2: Five-layer framework
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- Section 3: JSON schema design
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- Section 7: Example bracket output
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### If you want **implementation details:**
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→ Read `INTROSPECTION_API_GUIDE.md`
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- Section 1: Geometric parameter extraction (NXOpen patterns)
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- Section 3: BDF parsing (pyNastran code)
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- Section 6: Master orchestrator (full runner)
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### If you're ready to start:
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→ Approve Phase 1-2 and I'll begin implementation tomorrow.
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---
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## Closing Thoughts
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This isn't just about extracting data — it's about **giving Atomizer a brain**.
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Right now, Atomizer executes studies you configure. With full introspection, Atomizer **understands** what it's optimizing:
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- What can change (design variables)
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- What physics matters (BCs, loads, solver)
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- What baseline looks like (pre-opt stress, displacement)
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- What relationships exist (expression dependencies)
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That understanding unlocks:
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- **Smarter suggestions** ("Based on your mesh, I recommend...")
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- **Better validation** ("Warning: This BC is invalid")
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- **Automated documentation** (Every study gets a full data sheet)
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- **Knowledge accumulation** (Every introspection feeds the HQ knowledge base)
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**You asked for introspection on another level. This is it.**
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---
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**Ready when you are.** 🖥️
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— NX Expert | Atomizer Engineering Co.
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