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feat: Implement YAML-first declarative intelligence architecture
Revolutionary transformation from hardcoded Python intelligence to hot-reloadable YAML patterns, enabling dynamic configuration without code changes. ## Phase 1: Foundation Intelligence Complete ### YAML Intelligence Patterns (6 files) - intelligence_patterns.yaml: Multi-dimensional pattern recognition with adaptive learning - mcp_orchestration.yaml: Server selection decision trees with load balancing - hook_coordination.yaml: Parallel execution patterns with dependency resolution - performance_intelligence.yaml: Resource zones and auto-optimization triggers - validation_intelligence.yaml: Health scoring and proactive diagnostic patterns - user_experience.yaml: Project detection and smart UX adaptations ### Python Infrastructure Enhanced (4 components) - intelligence_engine.py: Generic YAML pattern interpreter with hot-reload - learning_engine.py: Enhanced with YAML intelligence integration - yaml_loader.py: Added intelligence configuration helper methods - validate_system.py: New YAML-driven validation with health scoring ### Key Features Implemented - Hot-reload intelligence: Update patterns without code changes or restarts - Declarative configuration: All intelligence logic expressed in YAML - Graceful fallbacks: System works correctly even with missing YAML files - Multi-pattern coordination: Intelligent recommendations from multiple sources - Health scoring: Component-weighted validation with predictive diagnostics - Generic architecture: Single engine consumes all intelligence pattern types ### Testing Results ✅ All components integrate correctly ✅ Hot-reload mechanism functional ✅ Graceful error handling verified ✅ YAML-driven validation operational ✅ Health scoring system working (detected real system issues) This enables users to modify intelligence behavior by editing YAML files, add new pattern types without coding, and hot-reload improvements in real-time. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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NomenAK
@@ -75,6 +75,9 @@ class StopHook:
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self.initialization_time = (time.time() - start_time) * 1000
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self.performance_target_ms = config_loader.get_hook_config('stop', 'performance_target_ms', 200)
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# Store cache directory reference
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self._cache_dir = cache_dir
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def process_session_stop(self, session_data: dict) -> dict:
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"""
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Process session stop with analytics and persistence.
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@@ -188,21 +191,108 @@ class StopHook:
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# Graceful fallback on error
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return self._create_fallback_report(session_data, str(e))
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def _get_current_session_id(self) -> str:
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"""Get current session ID from cache."""
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try:
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session_id_file = self._cache_dir / "session_id"
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if session_id_file.exists():
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return session_id_file.read_text().strip()
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except Exception:
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pass
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return ""
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def _parse_session_activity(self, session_id: str) -> dict:
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"""Parse session activity from log files."""
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tools_used = set()
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operations_count = 0
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mcp_tools_used = set()
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if not session_id:
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return {
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"operations_completed": 0,
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"tools_utilized": [],
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"unique_tools_count": 0,
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"mcp_tools_used": []
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}
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# Parse current log file
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log_file_path = self._cache_dir / "logs" / f"superclaude-lite-{time.strftime('%Y-%m-%d')}.log"
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try:
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if log_file_path.exists():
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with open(log_file_path, 'r') as f:
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for line in f:
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try:
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log_entry = json.loads(line.strip())
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# Only process entries for current session
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if log_entry.get('session') != session_id:
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continue
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# Count operations from pre_tool_use hook
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if (log_entry.get('hook') == 'pre_tool_use' and
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log_entry.get('event') == 'start'):
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operations_count += 1
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tool_name = log_entry.get('data', {}).get('tool_name', '')
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if tool_name:
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tools_used.add(tool_name)
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# Track MCP tools separately
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if tool_name.startswith('mcp__'):
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mcp_tools_used.add(tool_name)
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except (json.JSONDecodeError, KeyError):
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# Skip malformed log entries
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continue
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except Exception as e:
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# Log error but don't fail
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log_error("stop", f"Failed to parse session activity: {str(e)}", {"session_id": session_id})
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return {
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"operations_completed": operations_count,
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"tools_utilized": list(tools_used),
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"unique_tools_count": len(tools_used),
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"mcp_tools_used": list(mcp_tools_used)
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}
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def _extract_session_context(self, session_data: dict) -> dict:
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"""Extract and enrich session context."""
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# Get current session ID
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current_session_id = self._get_current_session_id()
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# Try to load session context from current session file
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session_context = {}
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if current_session_id:
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session_file_path = self._cache_dir / f"session_{current_session_id}.json"
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try:
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if session_file_path.exists():
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with open(session_file_path, 'r') as f:
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session_context = json.load(f)
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except Exception as e:
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# Log error but continue with fallback
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log_error("stop", f"Failed to load session context: {str(e)}", {"session_id": current_session_id})
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# Parse session activity from logs
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activity_data = self._parse_session_activity(current_session_id)
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# Create context with session file data (if available) or fallback to session_data
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context = {
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'session_id': session_data.get('session_id', ''),
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'session_duration_ms': session_data.get('duration_ms', 0),
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'session_start_time': session_data.get('start_time', 0),
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'session_id': session_context.get('session_id', current_session_id or session_data.get('session_id', '')),
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'session_duration_ms': session_data.get('duration_ms', 0), # This comes from hook system
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'session_start_time': session_context.get('initialization_timestamp', session_data.get('start_time', 0)),
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'session_end_time': time.time(),
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'operations_performed': session_data.get('operations', []),
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'tools_used': session_data.get('tools_used', []),
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'mcp_servers_activated': session_data.get('mcp_servers', []),
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'operations_performed': activity_data.get('tools_utilized', []), # Actual tools used from logs
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'tools_used': activity_data.get('tools_utilized', []), # Actual tools used from logs
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'mcp_servers_activated': session_context.get('mcp_servers', {}).get('enabled_servers', []),
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'errors_encountered': session_data.get('errors', []),
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'user_interactions': session_data.get('user_interactions', []),
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'resource_usage': session_data.get('resource_usage', {}),
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'quality_metrics': session_data.get('quality_metrics', {}),
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'superclaude_enabled': session_data.get('superclaude_enabled', False)
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'superclaude_enabled': session_context.get('superclaude_enabled', False),
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# Add parsed activity metrics
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'operation_count': activity_data.get('operations_completed', 0),
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'unique_tools_count': activity_data.get('unique_tools_count', 0),
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'mcp_tools_used': activity_data.get('mcp_tools_used', [])
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}
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# Calculate derived metrics
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