feat: Implement Phase 1 - Plugin & Hook System

Core plugin architecture for LLM-driven optimization:

New Features:
- Hook system with 6 lifecycle points (pre_mesh, post_mesh, pre_solve, post_solve, post_extraction, custom_objectives)
- HookManager for centralized registration and execution
- Code validation with AST-based safety checks
- Feature registry (JSON) for LLM capability discovery
- Example plugin: log_trial_start
- 23 comprehensive tests (all passing)

Integration:
- OptimizationRunner now loads plugins automatically
- Hooks execute at 5 points in optimization loop
- Custom objectives can override total_objective via hooks

Safety:
- Module whitelist (numpy, scipy, pandas, optuna, pyNastran)
- Dangerous operation blocking (eval, exec, os.system, subprocess)
- Optional file operation permission flag

Files Added:
- optimization_engine/plugins/__init__.py
- optimization_engine/plugins/hooks.py
- optimization_engine/plugins/hook_manager.py
- optimization_engine/plugins/validators.py
- optimization_engine/feature_registry.json
- optimization_engine/plugins/pre_solve/log_trial_start.py
- tests/test_plugin_system.py (23 tests)

Files Modified:
- optimization_engine/runner.py (added hook integration)

Ready for Phase 2: LLM interface layer

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

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

optimization_engine/runner.py

@@ -23,6 +23,8 @@ import pandas as pd
 from datetime import datetime
 import pickle
 
+from optimization_engine.plugins import HookManager
+
 
 class OptimizationRunner:
     """
@@ -68,6 +70,15 @@ class OptimizationRunner:
         self.output_dir = self.config_path.parent / 'optimization_results'
         self.output_dir.mkdir(exist_ok=True)
 
+        # Initialize plugin/hook system
+        self.hook_manager = HookManager()
+        plugins_dir = Path(__file__).parent / 'plugins'
+        if plugins_dir.exists():
+            self.hook_manager.load_plugins_from_directory(plugins_dir)
+            summary = self.hook_manager.get_summary()
+            if summary['total_hooks'] > 0:
+                print(f"Loaded {summary['enabled_hooks']}/{summary['total_hooks']} plugins")
+
     def _load_config(self) -> Dict[str, Any]:
         """Load and validate optimization configuration."""
         with open(self.config_path, 'r') as f:
@@ -311,6 +322,16 @@ class OptimizationRunner:
                     int(dv['bounds'][1])
                 )
 
+        # Execute pre_solve hooks
+        pre_solve_context = {
+            'trial_number': trial.number,
+            'design_variables': design_vars,
+            'sim_file': self.config.get('sim_file', ''),
+            'working_dir': str(Path.cwd()),
+            'config': self.config
+        }
+        self.hook_manager.execute_hooks('pre_solve', pre_solve_context, fail_fast=False)
+
         # 2. Update NX model with new parameters
         try:
             self.model_updater(design_vars)
@@ -318,6 +339,15 @@ class OptimizationRunner:
             print(f"Error updating model: {e}")
             raise optuna.TrialPruned()
 
+        # Execute post_mesh hooks (after model update)
+        post_mesh_context = {
+            'trial_number': trial.number,
+            'design_variables': design_vars,
+            'sim_file': self.config.get('sim_file', ''),
+            'working_dir': str(Path.cwd())
+        }
+        self.hook_manager.execute_hooks('post_mesh', post_mesh_context, fail_fast=False)
+
         # 3. Run simulation
         try:
             result_path = self.simulation_runner()
@@ -325,6 +355,15 @@ class OptimizationRunner:
             print(f"Error running simulation: {e}")
             raise optuna.TrialPruned()
 
+        # Execute post_solve hooks
+        post_solve_context = {
+            'trial_number': trial.number,
+            'design_variables': design_vars,
+            'result_path': str(result_path) if result_path else '',
+            'working_dir': str(Path.cwd())
+        }
+        self.hook_manager.execute_hooks('post_solve', post_solve_context, fail_fast=False)
+
         # 4. Extract results with appropriate precision
         extracted_results = {}
         for obj in self.config['objectives']:
@@ -362,6 +401,16 @@ class OptimizationRunner:
                 print(f"Error extracting {const['name']}: {e}")
                 raise optuna.TrialPruned()
 
+        # Execute post_extraction hooks
+        post_extraction_context = {
+            'trial_number': trial.number,
+            'design_variables': design_vars,
+            'extracted_results': extracted_results,
+            'result_path': str(result_path) if result_path else '',
+            'working_dir': str(Path.cwd())
+        }
+        self.hook_manager.execute_hooks('post_extraction', post_extraction_context, fail_fast=False)
+
         # 5. Evaluate constraints
         for const in self.config.get('constraints', []):
             value = extracted_results[const['name']]
@@ -389,6 +438,23 @@ class OptimizationRunner:
             else:  # maximize
                 total_objective -= weight * value
 
+        # Execute custom_objective hooks (can modify total_objective)
+        custom_objective_context = {
+            'trial_number': trial.number,
+            'design_variables': design_vars,
+            'extracted_results': extracted_results,
+            'total_objective': total_objective,
+            'working_dir': str(Path.cwd())
+        }
+        custom_results = self.hook_manager.execute_hooks('custom_objective', custom_objective_context, fail_fast=False)
+
+        # Allow hooks to override objective value
+        for result in custom_results:
+            if result and 'total_objective' in result:
+                total_objective = result['total_objective']
+                print(f"Custom objective hook modified total_objective to {total_objective:.6f}")
+                break  # Use first hook that provides override
+
         # 7. Store results in history
         history_entry = {
             'trial_number': trial.number,