docs: Update documentation for v2.0 module reorganization

- Update feature_registry.json paths to new module locations (v0.3.0)
- Update cheatsheet with new import paths (v2.3)
- Mark migration plan as completed (v3.0)

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

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

.claude/skills/01_CHEATSHEET.md

@@ -1,11 +1,11 @@
 ---
 skill_id: SKILL_001
-version: 2.2
-last_updated: 2025-12-28
+version: 2.3
+last_updated: 2025-12-29
 type: reference
 code_dependencies:
   - optimization_engine/extractors/__init__.py
-  - optimization_engine/method_selector.py
+  - optimization_engine/core/method_selector.py
   - optimization_engine/utils/trial_manager.py
   - optimization_engine/utils/dashboard_db.py
 requires_skills:
@@ -14,8 +14,8 @@ requires_skills:
 
 # Atomizer Quick Reference Cheatsheet
 
-**Version**: 2.2
-**Updated**: 2025-12-28
+**Version**: 2.3
+**Updated**: 2025-12-29
 **Purpose**: Rapid lookup for common operations. "I want X → Use Y"
 
 ---
@@ -142,7 +142,7 @@ Question: Do you need >50 trials OR surrogate model?
 Exploits surrogate differentiability for **100-1000x faster** local refinement:
 
 ```python
-from optimization_engine.gradient_optimizer import GradientOptimizer, run_lbfgs_polish
+from optimization_engine.core.gradient_optimizer import GradientOptimizer, run_lbfgs_polish
 
 # Quick usage - polish from top FEA candidates
 results = run_lbfgs_polish(study_dir, n_starts=20, n_iterations=100)
@@ -154,7 +154,7 @@ result = optimizer.optimize(starting_points=top_candidates, method='lbfgs')
 
 **CLI usage**:
 ```bash
-python -m optimization_engine.gradient_optimizer studies/my_study --n-starts 20
+python -m optimization_engine.core.gradient_optimizer studies/my_study --n-starts 20
 
 # Or per-study script (if available)
 python run_lbfgs_polish.py --n-starts 20 --grid-then-grad

.claude/skills/modules/OPTIMIZATION_ENGINE_MIGRATION_PLAN.md

@@ -2,15 +2,42 @@
 
 ## Complete Guide for Safe Codebase Restructuring
 
-**Document Version**: 2.1 (EXHAUSTIVE + CONTEXT ENGINEERING ALIGNED)
+**Document Version**: 3.0 (COMPLETED)
 **Created**: 2025-12-23
-**Updated**: 2025-12-28
-**Status**: READY FOR EXECUTION
+**Updated**: 2025-12-29
+**Status**: ✅ COMPLETED - Merged to main
 **Risk Level**: HIGH - Affects 500+ files across entire codebase
 **Next Phase**: ATOMIZER_CONTEXT_ENGINEERING_PLAN.md (execute AFTER this migration)
 
 ---
 
+## ✅ Migration Completed: 2025-12-29
+
+### Final Statistics
+| Metric | Value |
+|--------|-------|
+| Files modified | 120 |
+| Import changes | ~200 |
+| Commits | 2 (pre-migration checkpoint + migration) |
+| Branch | `refactor/optimization-engine-v2` merged to `main` |
+| Version bump | `optimization_engine` v1.0 → v2.0.0 |
+
+### Key Changes Made
+1. Created new directory structure: `core/`, `processors/surrogates/`, `nx/`, `study/`, `reporting/`, `config/`
+2. Moved 60+ Python files to new locations
+3. Updated all imports across 125 files
+4. Added lazy-loading `__init__.py` files to avoid circular imports
+5. Implemented backwards compatibility layer with deprecation warnings
+6. Updated `feature_registry.json` paths
+
+### Post-Migration Notes
+- All existing `run_optimization.py` scripts continue to work
+- Old imports emit deprecation warnings but still function
+- Dashboard integration preserved
+- Study databases unchanged
+
+---
+
 ## Execution Order (IMPORTANT)
 
 This migration plan should be executed **BEFORE** the Context Engineering plan.

optimization_engine/feature_registry.json

@@ -1,7 +1,7 @@
 {
   "feature_registry": {
-    "version": "0.2.0",
-    "last_updated": "2025-01-16",
+    "version": "0.3.0",
+    "last_updated": "2025-12-29",
     "description": "Comprehensive catalog of Atomizer capabilities for LLM-driven optimization",
     "architecture_doc": "docs/FEATURE_REGISTRY_ARCHITECTURE.md",
     "categories": {
@@ -162,9 +162,9 @@
               "lifecycle_stage": "all",
               "abstraction_level": "workflow",
               "implementation": {
-                "file_path": "optimization_engine/runner.py",
+                "file_path": "optimization_engine/core/runner.py",
                 "function_name": "run_optimization",
-                "entry_point": "from optimization_engine.runner import run_optimization"
+                "entry_point": "from optimization_engine.core.runner import run_optimization"
               },
               "interface": {
                 "inputs": [
@@ -240,7 +240,7 @@
               "lifecycle_stage": "optimization",
               "abstraction_level": "primitive",
               "implementation": {
-                "file_path": "optimization_engine/runner.py",
+                "file_path": "optimization_engine/core/runner.py",
                 "function_name": "optuna.samplers.TPESampler",
                 "entry_point": "import optuna.samplers.TPESampler"
               },
@@ -295,9 +295,9 @@
               "lifecycle_stage": "solve",
               "abstraction_level": "primitive",
               "implementation": {
-                "file_path": "optimization_engine/nx_solver.py",
+                "file_path": "optimization_engine/nx/solver.py",
                 "function_name": "run_nx_simulation",
-                "entry_point": "from optimization_engine.nx_solver import run_nx_simulation"
+                "entry_point": "from optimization_engine.nx.solver import run_nx_simulation"
               },
               "interface": {
                 "inputs": [
@@ -370,9 +370,9 @@
               "lifecycle_stage": "pre_solve",
               "abstraction_level": "primitive",
               "implementation": {
-                "file_path": "optimization_engine/nx_updater.py",
+                "file_path": "optimization_engine/nx/updater.py",
                 "function_name": "update_nx_expressions",
-                "entry_point": "from optimization_engine.nx_updater import update_nx_expressions"
+                "entry_point": "from optimization_engine.nx.updater import update_nx_expressions"
               },
               "interface": {
                 "inputs": [
@@ -558,9 +558,9 @@
               "lifecycle_stage": "pre_optimization",
               "abstraction_level": "composite",
               "implementation": {
-                "file_path": "optimization_engine/runner.py",
+                "file_path": "optimization_engine/study/creator.py",
                 "function_name": "setup_study",
-                "entry_point": "from optimization_engine.runner import setup_study"
+                "entry_point": "from optimization_engine.study.creator import setup_study"
               },
               "interface": {
                 "inputs": [