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Zhongwei Li
307
skills/meta-automation-architect/scripts/user_preferences.py
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307
skills/meta-automation-architect/scripts/user_preferences.py
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#!/usr/bin/env python3
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"""
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User Preference Learning
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Learns from user's choices to provide better recommendations over time
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"""
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import json
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, List, Optional
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from collections import defaultdict
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class UserPreferences:
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"""Learns and stores user preferences for automation"""
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def __init__(self, storage_path: str = ".claude/meta-automation/user_preferences.json"):
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self.storage_path = Path(storage_path)
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self.storage_path.parent.mkdir(parents=True, exist_ok=True)
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self.preferences = self._load()
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def _load(self) -> Dict:
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"""Load existing preferences or create new"""
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if self.storage_path.exists():
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try:
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with open(self.storage_path, 'r') as f:
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return json.load(f)
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except:
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return self._create_new()
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return self._create_new()
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def _create_new(self) -> Dict:
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"""Create new preferences structure"""
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return {
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'version': '1.0',
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'created_at': datetime.now().isoformat(),
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'projects_analyzed': 0,
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'automation_mode_preferences': {
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'quick': 0,
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'focused': 0,
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'comprehensive': 0
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},
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'agent_usage': {},
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'skill_usage': {},
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'project_type_history': {},
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'time_saved_total': 0,
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'cost_spent_total': 0,
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'satisfaction_ratings': [],
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'most_valuable_automations': [],
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'rarely_used': [],
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'integration_preferences': {
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'focus_on_gaps': 0,
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'enhance_existing': 0,
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'independent': 0
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},
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'sessions': []
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}
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def _save(self):
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"""Save preferences to disk"""
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with open(self.storage_path, 'w') as f:
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json.dump(self.preferences, f, indent=2)
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def record_session(self, session_data: Dict):
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"""
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Record a new automation session
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Args:
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session_data: {
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'session_id': str,
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'project_type': str,
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'mode': 'quick|focused|comprehensive',
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'agents_used': List[str],
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'skills_generated': List[str],
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'time_spent_minutes': int,
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'cost': float,
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'time_saved_estimate': int, # hours
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'user_satisfaction': int, # 1-5
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'integration_choice': str, # gaps|enhance|independent
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}
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"""
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# Update counts
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self.preferences['projects_analyzed'] += 1
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# Record mode preference
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mode = session_data.get('mode', 'quick')
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self.preferences['automation_mode_preferences'][mode] += 1
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# Record agent usage
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for agent in session_data.get('agents_used', []):
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if agent not in self.preferences['agent_usage']:
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self.preferences['agent_usage'][agent] = 0
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self.preferences['agent_usage'][agent] += 1
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# Record skill usage
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for skill in session_data.get('skills_generated', []):
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if skill not in self.preferences['skill_usage']:
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self.preferences['skill_usage'][skill] = 0
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self.preferences['skill_usage'][skill] += 1
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# Record project type
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project_type = session_data.get('project_type', 'unknown')
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if project_type not in self.preferences['project_type_history']:
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self.preferences['project_type_history'][project_type] = 0
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self.preferences['project_type_history'][project_type] += 1
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# Track totals
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self.preferences['time_saved_total'] += session_data.get('time_saved_estimate', 0)
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self.preferences['cost_spent_total'] += session_data.get('cost', 0)
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# Track satisfaction
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satisfaction = session_data.get('user_satisfaction')
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if satisfaction:
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self.preferences['satisfaction_ratings'].append({
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'session_id': session_data.get('session_id'),
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'rating': satisfaction,
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'date': datetime.now().isoformat()
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})
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# Track integration preference
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integration = session_data.get('integration_choice')
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if integration in self.preferences['integration_preferences']:
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self.preferences['integration_preferences'][integration] += 1
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# Store full session
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self.preferences['sessions'].append({
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**session_data,
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'recorded_at': datetime.now().isoformat()
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})
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self._save()
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def get_recommended_mode(self) -> str:
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"""Get recommended automation mode based on history"""
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prefs = self.preferences['automation_mode_preferences']
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if self.preferences['projects_analyzed'] == 0:
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return 'quick' # Default for first-time users
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# Return mode user uses most
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return max(prefs.items(), key=lambda x: x[1])[0]
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def get_recommended_agents(self, project_type: str, count: int = 5) -> List[str]:
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"""Get recommended agents based on past usage and project type"""
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# Get agents user has used
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agent_usage = self.preferences['agent_usage']
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if not agent_usage:
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# Default recommendations for new users
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defaults = {
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'programming': ['project-analyzer', 'security-analyzer', 'test-coverage-analyzer'],
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'academic_writing': ['project-analyzer', 'latex-structure-analyzer', 'citation-analyzer'],
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'educational': ['project-analyzer', 'learning-path-analyzer', 'assessment-analyzer'],
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}
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return defaults.get(project_type, ['project-analyzer'])
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# Sort by usage count
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sorted_agents = sorted(agent_usage.items(), key=lambda x: x[1], reverse=True)
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return [agent for agent, _ in sorted_agents[:count]]
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def get_rarely_used(self) -> List[str]:
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"""Get agents/skills that user never finds valuable"""
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rarely_used = []
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# Check for agents used only once or twice
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for agent, count in self.preferences['agent_usage'].items():
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if count <= 2 and self.preferences['projects_analyzed'] > 5:
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rarely_used.append(agent)
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return rarely_used
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def should_skip_agent(self, agent_name: str) -> bool:
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"""Check if this agent is rarely useful for this user"""
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rarely_used = self.get_rarely_used()
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return agent_name in rarely_used
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def get_integration_preference(self) -> str:
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"""Get preferred integration approach"""
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prefs = self.preferences['integration_preferences']
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if sum(prefs.values()) == 0:
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return 'focus_on_gaps' # Default
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return max(prefs.items(), key=lambda x: x[1])[0]
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def get_statistics(self) -> Dict:
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"""Get usage statistics"""
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total_sessions = self.preferences['projects_analyzed']
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if total_sessions == 0:
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return {
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'total_sessions': 0,
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'message': 'No automation sessions yet'
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}
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avg_satisfaction = 0
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if self.preferences['satisfaction_ratings']:
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avg_satisfaction = sum(r['rating'] for r in self.preferences['satisfaction_ratings']) / len(self.preferences['satisfaction_ratings'])
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return {
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'total_sessions': total_sessions,
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'time_saved_total_hours': self.preferences['time_saved_total'],
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'cost_spent_total': round(self.preferences['cost_spent_total'], 2),
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'average_satisfaction': round(avg_satisfaction, 1),
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'preferred_mode': self.get_recommended_mode(),
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'most_used_agents': sorted(
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self.preferences['agent_usage'].items(),
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key=lambda x: x[1],
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reverse=True
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)[:5],
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'project_types': self.preferences['project_type_history'],
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'roi': round(self.preferences['time_saved_total'] / max(1, self.preferences['cost_spent_total'] * 60), 1)
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}
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def get_recommendations_for_user(self, project_type: str) -> Dict:
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"""Get personalized recommendations"""
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stats = self.get_statistics()
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if stats['total_sessions'] == 0:
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return {
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'recommended_mode': 'quick',
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'reason': 'First time - start with quick analysis to see how it works',
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'recommended_agents': ['project-analyzer'],
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'skip_agents': []
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}
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return {
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'recommended_mode': self.get_recommended_mode(),
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'reason': f"You've used {self.get_recommended_mode()} mode {self.preferences['automation_mode_preferences'][self.get_recommended_mode()]} times",
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'recommended_agents': self.get_recommended_agents(project_type),
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'skip_agents': self.get_rarely_used(),
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'integration_preference': self.get_integration_preference(),
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'stats': {
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'total_time_saved': f"{stats['time_saved_total_hours']} hours",
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'average_satisfaction': stats.get('average_satisfaction', 0),
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'roi': f"{stats.get('roi', 0)}x return on investment"
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}
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}
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def export_report(self) -> str:
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"""Export usage report"""
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stats = self.get_statistics()
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report = f"""
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# Meta-Automation Usage Report
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## Overview
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- **Total Sessions:** {stats['total_sessions']}
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- **Time Saved:** {stats.get('time_saved_total_hours', 0)} hours
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- **Cost Spent:** ${stats.get('cost_spent_total', 0):.2f}
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- **ROI:** {stats.get('roi', 0)}x (hours saved per dollar spent × 60)
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- **Avg Satisfaction:** {stats.get('average_satisfaction', 0)}/5
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## Your Preferences
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- **Preferred Mode:** {stats.get('preferred_mode', 'quick')}
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- **Integration Style:** {self.get_integration_preference()}
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## Most Used Agents
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"""
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for agent, count in stats.get('most_used_agents', []):
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report += f"- {agent}: {count} times\n"
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report += "\n## Project Types\n"
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for ptype, count in self.preferences['project_type_history'].items():
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report += f"- {ptype}: {count} projects\n"
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return report
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# Example usage
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if __name__ == '__main__':
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prefs = UserPreferences()
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# Simulate some sessions
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print("Simulating usage...\n")
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prefs.record_session({
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'session_id': 'session-1',
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'project_type': 'programming',
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'mode': 'quick',
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'agents_used': ['project-analyzer'],
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'skills_generated': [],
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'time_spent_minutes': 5,
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'cost': 0.03,
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'time_saved_estimate': 10,
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'user_satisfaction': 4,
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'integration_choice': 'focus_on_gaps'
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})
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prefs.record_session({
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'session_id': 'session-2',
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'project_type': 'programming',
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'mode': 'focused',
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'agents_used': ['project-analyzer', 'security-analyzer', 'test-coverage-analyzer'],
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'skills_generated': ['security-scanner', 'test-generator'],
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'time_spent_minutes': 8,
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'cost': 0.09,
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'time_saved_estimate': 50,
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'user_satisfaction': 5,
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'integration_choice': 'focus_on_gaps'
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})
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print(prefs.export_report())
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print("\nRecommendations for next programming project:")
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recs = prefs.get_recommendations_for_user('programming')
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print(json.dumps(recs, indent=2))
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