Implement core skills: Code validation, model fine-tuning, and system diagnostics

- Added `ModelFineTuner` class for preparing training data and fine-tuning models based on user corrections.
- Introduced `CodeValidator` class to validate generated code against various hardware and style rules, including safety checks and function naming conventions.
- Developed skills for calculator operations, system information retrieval, weather fetching, and timer functionality.
- Implemented a self-diagnostic skill to run unit tests and report results.
- Created a dynamic skill loading mechanism to discover and register skills from the current directory.
- Added unit tests for skills to ensure functionality and reliability.

core/buddai_analytics.py

@@ -0,0 +1,72 @@
+import sqlite3
+from datetime import datetime, timedelta
+from core.buddai_shared import DB_PATH
+
+class LearningMetrics:
+    """Measure BuddAI's improvement over time"""
+    
+    def calculate_accuracy(self):
+        """What % of code is accepted without correction?"""
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        
+        thirty_days_ago = (datetime.now() - timedelta(days=30)).isoformat()
+        
+        cursor.execute("""
+            SELECT 
+                COUNT(*) as total_responses,
+                COUNT(CASE WHEN f.positive = 1 THEN 1 END) as positive_feedback,
+                COUNT(CASE WHEN c.id IS NOT NULL THEN 1 END) as corrected
+            FROM messages m
+            LEFT JOIN feedback f ON m.id = f.message_id
+            LEFT JOIN corrections c ON m.content LIKE '%' || c.original_code || '%'
+            WHERE m.role = 'assistant'
+            AND m.timestamp > ?
+        """, (thirty_days_ago,))
+        
+        total, positive, corrected = cursor.fetchone()
+        conn.close()
+        
+        accuracy = (positive / total) * 100 if total and total > 0 else 0
+        correction_rate = (corrected / total) * 100 if total and total > 0 else 0
+        
+        return {
+            "accuracy": accuracy,
+            "correction_rate": correction_rate,
+            "improvement": self.calculate_trend()
+        }
+    
+    def calculate_trend(self):
+        """Is BuddAI getting better over time?"""
+        # Compare last 7 days vs previous 7 days
+        recent = self.get_accuracy_for_period(7)
+        previous = self.get_accuracy_for_period(7, offset=7)
+        
+        improvement = recent - previous
+        return f"+{improvement:.1f}%" if improvement > 0 else f"{improvement:.1f}%"
+
+    def get_accuracy_for_period(self, days: int, offset: int = 0) -> float:
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        
+        start_dt = (datetime.now() - timedelta(days=days + offset)).isoformat()
+        end_dt = (datetime.now() - timedelta(days=offset)).isoformat()
+        
+        cursor.execute("""
+            SELECT 
+                COUNT(*) as total,
+                COUNT(CASE WHEN f.positive = 1 THEN 1 END) as positive
+            FROM messages m
+            LEFT JOIN feedback f ON m.id = f.message_id
+            WHERE m.role = 'assistant'
+            AND m.timestamp BETWEEN ? AND ?
+        """, (start_dt, end_dt))
+        
+        row = cursor.fetchone()
+        conn.close()
+        
+        if not row:
+            return 0.0
+            
+        total, positive = row
+        return (positive / total) * 100 if total and total > 0 else 0.0