feat: Add Study Insights module (SYS_16) for physics visualizations

Introduces a new plugin architecture for study-specific physics
visualizations, separating "optimizer perspective" (Analysis) from
"engineer perspective" (Insights).

New module: optimization_engine/insights/
- base.py: StudyInsight base class, InsightConfig, InsightResult, registry
- zernike_wfe.py: Mirror WFE with 3D surface and Zernike decomposition
- stress_field.py: Von Mises stress contours with safety factors
- modal_analysis.py: Natural frequencies and mode shapes
- thermal_field.py: Temperature distribution visualization
- design_space.py: Parameter-objective landscape exploration

Features:
- 5 insight types: zernike_wfe, stress_field, modal, thermal, design_space
- CLI: python -m optimization_engine.insights generate <study>
- Standalone HTML generation with Plotly
- Enhanced Zernike viz: Turbo colorscale, smooth shading, 0.5x AMP
- Dashboard API fix: Added include_coefficients param to extract_relative()

Documentation:
- docs/protocols/system/SYS_16_STUDY_INSIGHTS.md
- Updated ATOMIZER_CONTEXT.md (v1.7)
- Updated 01_CHEATSHEET.md with insights section

Tools:
- tools/zernike_html_generator.py: Standalone WFE HTML generator
- tools/analyze_wfe.bat: Double-click to analyze OP2 files

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.claude/skills/01_CHEATSHEET.md

@@ -22,7 +22,7 @@ requires_skills:
 
 | I want to... | Use Protocol | Key Command/Action |
 |--------------|--------------|-------------------|
-| Create a new optimization study | OP_01 | Generate `optimization_config.json` + `run_optimization.py` |
+| Create a new optimization study | OP_01 | Place in `studies/{geometry_type}/`, generate config + runner + **README.md** |
 | Run an optimization | OP_02 | `conda activate atomizer && python run_optimization.py` |
 | Check optimization progress | OP_03 | Query `study.db` or check dashboard at `localhost:3000` |
 | See best results | OP_04 | `optuna-dashboard sqlite:///study.db` or dashboard |
@@ -30,6 +30,7 @@ requires_skills:
 | Fix an error | OP_06 | Read error log → follow diagnostic tree |
 | Add custom physics extractor | EXT_01 | Create in `optimization_engine/extractors/` |
 | Add lifecycle hook | EXT_02 | Create in `optimization_engine/plugins/` |
+| Generate physics insight | SYS_16 | `python -m optimization_engine.insights generate <study>` |
 
 ---
 
@@ -291,6 +292,53 @@ Without it, `UpdateFemodel()` runs but the mesh doesn't change!
 | 13 | Dashboard | Real-time tracking and visualization |
 | 14 | Neural | Surrogate model acceleration |
 | 15 | Method Selector | Recommends optimization strategy |
+| 16 | Study Insights | Physics visualizations (Zernike, stress, modal) |
+
+---
+
+## Study Insights Quick Reference (SYS_16)
+
+Generate physics-focused visualizations from FEA results.
+
+### Available Insight Types
+| Type | Purpose | Data Required |
+|------|---------|---------------|
+| `zernike_wfe` | Mirror wavefront error | OP2 with displacements |
+| `stress_field` | Von Mises stress contours | OP2 with stresses |
+| `modal` | Mode shapes + frequencies | OP2 with eigenvectors |
+| `thermal` | Temperature distribution | OP2 with temperatures |
+| `design_space` | Parameter-objective landscape | study.db with 5+ trials |
+
+### Commands
+```bash
+# List available insights for a study
+python -m optimization_engine.insights list
+
+# Generate all insights
+python -m optimization_engine.insights generate studies/my_study
+
+# Generate specific insight
+python -m optimization_engine.insights generate studies/my_study --type zernike_wfe
+```
+
+### Python API
+```python
+from optimization_engine.insights import get_insight, list_available_insights
+from pathlib import Path
+
+study_path = Path("studies/my_study")
+
+# Check what's available
+available = list_available_insights(study_path)
+
+# Generate Zernike WFE insight
+insight = get_insight('zernike_wfe', study_path)
+result = insight.generate()
+print(result.html_path)  # Path to generated HTML
+print(result.summary)    # Key metrics dict
+```
+
+**Output**: HTMLs saved to `{study}/3_insights/`
 
 ---