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