refactor: Archive experimental LLM features for MVP stability (Phase 1.1)

Moved experimental LLM integration code to optimization_engine/future/:
- llm_optimization_runner.py - Runtime LLM API runner
- llm_workflow_analyzer.py - Workflow analysis
- inline_code_generator.py - Auto-generate calculations
- hook_generator.py - Auto-generate hooks
- report_generator.py - LLM report generation
- extractor_orchestrator.py - Extractor orchestration

Added comprehensive optimization_engine/future/README.md explaining:
- MVP LLM strategy (Claude Code skills, not runtime LLM)
- Why files were archived
- When to revisit post-MVP
- Production architecture reference

Production runner confirmed: optimization_engine/runner.py is sole active runner.

This establishes clear separation between:
- Production code (stable, no runtime LLM dependencies)
- Experimental code (archived for post-MVP exploration)

Part of Phase 1: Core Stabilization & Organization for MVP

Generated with Claude Code

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

optimization_engine/future/report_generator.py

@@ -0,0 +1,134 @@
+"""
+Report Generator Utility
+Generates Markdown/HTML/PDF reports for optimization studies
+"""
+
+import json
+from pathlib import Path
+from typing import Optional
+import markdown
+from datetime import datetime
+
+def generate_study_report(
+    study_dir: Path,
+    output_format: str = "markdown",
+    include_llm_summary: bool = False
+) -> Optional[Path]:
+    """
+    Generate a report for the study.
+    
+    Args:
+        study_dir: Path to the study directory
+        output_format: 'markdown', 'html', or 'pdf'
+        include_llm_summary: Whether to include AI-generated summary
+        
+    Returns:
+        Path to the generated report file
+    """
+    try:
+        # Load data
+        config_path = study_dir / "1_setup" / "optimization_config.json"
+        history_path = study_dir / "2_results" / "optimization_history_incremental.json"
+        
+        if not config_path.exists() or not history_path.exists():
+            return None
+            
+        with open(config_path) as f:
+            config = json.load(f)
+            
+        with open(history_path) as f:
+            history = json.load(f)
+            
+        # Find best trial
+        best_trial = None
+        if history:
+            best_trial = min(history, key=lambda x: x['objective'])
+            
+        # Generate Markdown content
+        md_content = f"""# Optimization Report: {config.get('study_name', study_dir.name)}
+
+**Date**: {datetime.now().strftime('%Y-%m-%d %H:%M')}
+**Status**: {'Completed' if len(history) >= config.get('optimization_settings', {}).get('n_trials', 50) else 'In Progress'}
+
+## Executive Summary
+{_generate_summary(history, best_trial, include_llm_summary)}
+
+## Study Configuration
+- **Objectives**: {', '.join([o['name'] for o in config.get('objectives', [])])}
+- **Design Variables**: {len(config.get('design_variables', []))} variables
+- **Total Trials**: {len(history)}
+
+## Best Result (Trial #{best_trial['trial_number'] if best_trial else 'N/A'})
+- **Objective Value**: {best_trial['objective'] if best_trial else 'N/A'}
+- **Parameters**:
+"""
+        
+        if best_trial:
+            for k, v in best_trial['design_variables'].items():
+                md_content += f"  - **{k}**: {v:.4f}\n"
+                
+        md_content += "\n## Optimization Progress\n"
+        md_content += "The optimization process showed convergence towards the optimal solution.\n"
+        
+        # Save report based on format
+        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+        output_dir = study_dir / "2_results"
+        
+        if output_format in ['markdown', 'md']:
+            output_path = output_dir / f"optimization_report_{timestamp}.md"
+            with open(output_path, 'w') as f:
+                f.write(md_content)
+                
+        elif output_format == 'html':
+            output_path = output_dir / f"optimization_report_{timestamp}.html"
+            html_content = markdown.markdown(md_content)
+            # Add basic styling
+            styled_html = f"""
+            <html>
+            <head>
+                <style>
+                    body {{ font-family: sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; }}
+                    h1 {{ color: #2563eb; }}
+                    h2 {{ border-bottom: 1px solid #e5e7eb; padding-bottom: 10px; margin-top: 30px; }}
+                    code {{ background: #f3f4f6; padding: 2px 4px; rounded: 4px; }}
+                </style>
+            </head>
+            <body>
+                {html_content}
+            </body>
+            </html>
+            """
+            with open(output_path, 'w') as f:
+                f.write(styled_html)
+                
+        elif output_format == 'pdf':
+            # Requires weasyprint
+            try:
+                from weasyprint import HTML
+                output_path = output_dir / f"optimization_report_{timestamp}.pdf"
+                html_content = markdown.markdown(md_content)
+                HTML(string=html_content).write_pdf(str(output_path))
+            except ImportError:
+                print("WeasyPrint not installed, falling back to HTML")
+                return generate_study_report(study_dir, 'html', include_llm_summary)
+                
+        return output_path
+        
+    except Exception as e:
+        print(f"Report generation error: {e}")
+        return None
+
+def _generate_summary(history, best_trial, use_llm):
+    if use_llm:
+        return "[AI Summary Placeholder] The optimization successfully identified a design that minimizes mass while satisfying all constraints."
+    
+    if not history:
+        return "No trials completed yet."
+        
+    improvement = 0
+    if len(history) > 1:
+        first = history[0]['objective']
+        best = best_trial['objective']
+        improvement = ((first - best) / first) * 100
+        
+    return f"The optimization run completed {len(history)} trials. The best design found (Trial #{best_trial['trial_number']}) achieved an objective value of {best_trial['objective']:.4f}, representing a {improvement:.1f}% improvement over the initial design."