class CommunicationToolOrchestrator:
    """コミュニケーションツール統合管理"""
    
    def __init__(self):
        self.tools = {
            'slack': SlackAPI(),
            'github': GitHubAPI(),
            'jira': JiraAPI(),
            'confluence': ConfluenceAPI(),
            'calendar': CalendarAPI()
        }
        self.automation_rules = self._load_automation_rules()
    
    def setup_project_communication_environment(self, project_config):
        """プロジェクト用コミュニケーション環境の自動セットアップ"""
        
        project_name = project_config['name']
        team_members = project_config['team_members']
        project_type = project_config['type']  # 'development', 'research', 'maintenance'
        
        # Slackチャンネル作成・設定
        slack_channels = self._create_slack_channels(project_name, project_type)
        for channel in slack_channels:
            self.tools['slack'].create_channel(
                name=channel['name'],
                description=channel['description'],
                members=team_members,
                integrations=channel['integrations']
            )
        
        # GitHubリポジトリ・Issue設定
        github_setup = self._setup_github_project(project_name, project_config)
        
        # Jiraプロジェクト・ワークフロー設定
        jira_setup = self._setup_jira_project(project_name, project_config)
        
        # Confluence空間・テンプレート作成
        confluence_setup = self._setup_confluence_space(project_name, project_config)
        
        # 自動連携ルール設定
        self._setup_automation_rules(project_name, {
            'slack_channels': slack_channels,
            'github_repo': github_setup['repo_url'],
            'jira_project': jira_setup['project_key'],
            'confluence_space': confluence_setup['space_key']
        })
        
        return {
            'project_name': project_name,
            'communication_channels': slack_channels,
            'github_repository': github_setup,
            'project_management': jira_setup,
            'documentation_space': confluence_setup,
            'automation_rules': self.automation_rules[project_name]
        }
    
    def _create_slack_channels(self, project_name, project_type):
        """プロジェクトタイプに応じたSlackチャンネル設計"""
        
        base_channels = [
            {
                'name': f"{project_name}-general",
                'description': 'プロジェクト全般の議論・情報共有',
                'integrations': ['github', 'jira', 'calendar']
            },
            {
                'name': f"{project_name}-tech",
                'description': '技術的議論・アーキテクチャ・実装相談',
                'integrations': ['github', 'stackoverflow']
            },
            {
                'name': f"{project_name}-alerts",
                'description': 'CI/CD・監視・障害アラート専用',
                'integrations': ['github_actions', 'monitoring', 'oncall']
            }
        ]
        
        # プロジェクトタイプ別の追加チャンネル
        if project_type == 'development':
            base_channels.extend([
                {
                    'name': f"{project_name}-reviews",
                    'description': 'コードレビュー・PR通知',
                    'integrations': ['github_pr', 'sonarqube']
                },
                {
                    'name': f"{project_name}-releases",
                    'description': 'リリース計画・デプロイ調整',
                    'integrations': ['github_releases', 'deployment_tools']
                }
            ])
        
        elif project_type == 'research':
            base_channels.extend([
                {
                    'name': f"{project_name}-findings",
                    'description': '調査結果・実験データの共有',
                    'integrations': ['confluence', 'data_visualization']
                },
                {
                    'name': f"{project_name}-papers",
                    'description': '論文・参考資料の共有・議論',
                    'integrations': ['arxiv', 'google_scholar']
                }
            ])
        
        return base_channels
    
    def optimize_notification_strategy(self, team_preferences, work_patterns):
        """チーム特性に基づく通知最適化"""
        
        optimization_strategy = {}
        
        for member in team_preferences:
            member_id = member['id']
            timezone = member['timezone']
            focus_hours = member['focus_hours']  # 集中時間帯
            urgency_tolerance = member['urgency_tolerance']  # 緊急通知への許容度
            
            # 個人別通知設定の最適化
            member_strategy = {
                'quiet_hours': self._calculate_quiet_hours(timezone, focus_hours),
                'notification_levels': self._design_notification_levels(urgency_tolerance),
                'channel_preferences': self._optimize_channel_notifications(member['preferences']),
                'mobile_vs_desktop': self._optimize_device_notifications(member['device_usage'])
            }
            
            optimization_strategy[member_id] = member_strategy
        
        # チーム全体の通知ポリシー設定
        team_policy = self._create_team_notification_policy(optimization_strategy)
        
        return {
            'individual_strategies': optimization_strategy,
            'team_policy': team_policy,
            'automation_rules': self._create_notification_automation(optimization_strategy)
        }

class IntelligentKnowledgeBase:
    """動的知識管理システム"""
    
    def __init__(self):
        self.content_graph = KnowledgeGraph()
        self.search_engine = SemanticSearch()
        self.auto_updater = ContentUpdater()
        self.quality_monitor = QualityAssurance()
    
    def add_knowledge_item(self, content, metadata, context):
        """知識アイテムの追加・関連付け"""
        
        # コンテンツの自動分析・分類
        content_analysis = self._analyze_content(content)
        
        # 既存知識との関連性分析
        relationships = self._find_relationships(content, content_analysis)
        
        # 知識グラフへの追加
        knowledge_node = self.content_graph.add_node(
            content=content,
            metadata=metadata,
            analysis=content_analysis,
            relationships=relationships,
            context=context
        )
        
        # 検索インデックスの更新
        self.search_engine.index_content(knowledge_node)
        
        # 品質監視の開始
        self.quality_monitor.start_monitoring(knowledge_node)
        
        # 関連する既存知識の更新提案
        update_suggestions = self._suggest_related_updates(knowledge_node)
        
        return {
            'knowledge_id': knowledge_node.id,
            'relationships_created': len(relationships),
            'update_suggestions': update_suggestions,
            'quality_score': self.quality_monitor.assess_quality(knowledge_node)
        }
    
    def intelligent_search(self, query, context, user_profile):
        """コンテキスト理解型検索"""
        
        # クエリの意図理解
        query_intent = self._analyze_query_intent(query, context)
        
        # ユーザープロファイルに基づく個人化
        personalized_query = self._personalize_query(
            query, query_intent, user_profile
        )
        
        # セマンティック検索の実行
        search_results = self.search_engine.semantic_search(
            personalized_query, 
            context_filters=context,
            user_preferences=user_profile
        )
        
        # 結果の再ランキング・関連情報の付加
        enriched_results = self._enrich_search_results(
            search_results, query_intent, context
        )
        
        # 検索学習・改善
        self._learn_from_search(query, enriched_results, user_profile)
        
        return {
            'results': enriched_results,
            'suggestions': self._generate_search_suggestions(query_intent),
            'related_topics': self._find_related_topics(enriched_results),
            'learning_path': self._suggest_learning_path(enriched_results, user_profile)
        }
    
    def maintain_knowledge_quality(self):
        """知識品質の継続的維持"""
        
        maintenance_report = {
            'outdated_content': [],
            'quality_issues': [],
            'improvement_opportunities': [],
            'consolidation_candidates': []
        }
        
        # 全知識アイテムの品質チェック
        for knowledge_item in self.content_graph.get_all_nodes():
            
            # 鮮度チェック
            freshness_score = self._assess_content_freshness(knowledge_item)
            if freshness_score < 0.7:
                maintenance_report['outdated_content'].append({
                    'item_id': knowledge_item.id,
                    'title': knowledge_item.metadata['title'],
                    'last_updated': knowledge_item.metadata['last_updated'],
                    'freshness_score': freshness_score,
                    'update_priority': self._calculate_update_priority(knowledge_item)
                })
            
            # 品質問題の検出
            quality_issues = self._detect_quality_issues(knowledge_item)
            if quality_issues:
                maintenance_report['quality_issues'].extend(quality_issues)
            
            # 統合候補の検出
            consolidation_candidates = self._find_consolidation_opportunities(knowledge_item)
            if consolidation_candidates:
                maintenance_report['consolidation_candidates'].extend(consolidation_candidates)
        
        # 改善機会の特定
        improvement_opportunities = self._identify_improvement_opportunities()
        maintenance_report['improvement_opportunities'] = improvement_opportunities
        
        return maintenance_report
    
    def _assess_content_freshness(self, knowledge_item):
        """コンテンツ鮮度の評価"""
        
        # 最終更新からの経過時間
        last_updated = knowledge_item.metadata['last_updated']
        time_since_update = datetime.now() - last_updated
        
        # コンテンツタイプによる鮮度要求の違い
        content_type = knowledge_item.metadata['type']
        freshness_requirements = {
            'api_documentation': 30,    # 30日
            'troubleshooting': 90,      # 90日
            'architecture_decision': 180, # 180日
            'learning_material': 365     # 365日
        }
        
        required_freshness = freshness_requirements.get(content_type, 90)
        freshness_score = max(0, 1 - time_since_update.days / required_freshness)
        
        # 関連技術の変更による影響
        technology_change_impact = self._assess_technology_change_impact(knowledge_item)
        
        # 利用頻度による重要度調整
        usage_frequency = knowledge_item.metadata.get('monthly_views', 0)
        importance_multiplier = min(2.0, 1 + usage_frequency / 100)
        
        final_score = freshness_score * (1 - technology_change_impact * 0.3) * importance_multiplier
        
        return min(1.0, final_score)

# ナレッジベース分析・最適化
class KnowledgeBaseAnalytics:
    """ナレッジベース分析・最適化"""
    
    def analyze_usage_patterns(self, access_logs, user_profiles):
        """利用パターンの分析"""
        
        analytics = {
            'popular_content': self._identify_popular_content(access_logs),
            'search_patterns': self._analyze_search_behavior(access_logs),
            'knowledge_gaps': self._identify_knowledge_gaps(access_logs, user_profiles),
            'user_journey_analysis': self._analyze_user_journeys(access_logs),
            'content_effectiveness': self._measure_content_effectiveness(access_logs)
        }
        
        return {
            'usage_analytics': analytics,
            'optimization_recommendations': self._generate_optimization_recommendations(analytics),
            'content_strategy': self._suggest_content_strategy(analytics),
            'user_experience_improvements': self._suggest_ux_improvements(analytics)
        }
    
    def _identify_knowledge_gaps(self, access_logs, user_profiles):
        """知識ギャップの特定"""
        
        # 検索されるが見つからない内容
        failed_searches = [log for log in access_logs if log['result_count'] == 0]
        
        # 検索クエリの分析・分類
        gap_categories = {}
        for search in failed_searches:
            query = search['query']
            category = self._categorize_query(query)
            
            if category not in gap_categories:
                gap_categories[category] = {
                    'queries': [],
                    'frequency': 0,
                    'user_types': set()
                }
            
            gap_categories[category]['queries'].append(query)
            gap_categories[category]['frequency'] += 1
            gap_categories[category]['user_types'].add(search['user_type'])
        
        # 優先度付けされた知識ギャップ
        prioritized_gaps = []
        for category, data in gap_categories.items():
            priority_score = (
                data['frequency'] * 0.4 +  # 検索頻度
                len(data['user_types']) * 0.3 +  # 影響するユーザータイプの多様性
                self._assess_business_impact(category) * 0.3  # ビジネスインパクト
            )
            
            prioritized_gaps.append({
                'category': category,
                'priority_score': priority_score,
                'search_frequency': data['frequency'],
                'affected_user_types': list(data['user_types']),
                'sample_queries': data['queries'][:5],
                'recommended_content_type': self._recommend_content_type(category)
            })
        
        return sorted(prioritized_gaps, key=lambda x: x['priority_score'], reverse=True)

class SlackChannelOptimizer:
    """Slackチャンネル最適化システム"""
    
    def __init__(self):
        self.slack_api = SlackAPI()
        self.analytics = SlackAnalytics()
        self.ml_models = ChannelOptimizationML()
    
    def analyze_channel_health(self, timeframe='30d'):
        """チャンネル健全性の総合分析"""
        
        all_channels = self.slack_api.get_all_channels()
        
        channel_health_report = {
            'healthy_channels': [],
            'at_risk_channels': [],
            'inactive_channels': [],
            'optimization_opportunities': []
        }
        
        for channel in all_channels:
            health_metrics = self._calculate_channel_health(channel, timeframe)
            
            if health_metrics['overall_score'] >= 0.8:
                channel_health_report['healthy_channels'].append({
                    'channel_name': channel['name'],
                    'health_score': health_metrics['overall_score'],
                    'key_strengths': health_metrics['strengths']
                })
            
            elif health_metrics['overall_score'] >= 0.4:
                channel_health_report['at_risk_channels'].append({
                    'channel_name': channel['name'],
                    'health_score': health_metrics['overall_score'],
                    'issues': health_metrics['issues'],
                    'recommendations': health_metrics['recommendations']
                })
            
            else:
                channel_health_report['inactive_channels'].append({
                    'channel_name': channel['name'],
                    'health_score': health_metrics['overall_score'],
                    'inactivity_reason': health_metrics['inactivity_analysis'],
                    'action_recommendation': health_metrics['action_needed']
                })
        
        # 最適化機会の特定
        optimization_opportunities = self._identify_optimization_opportunities(all_channels)
        channel_health_report['optimization_opportunities'] = optimization_opportunities
        
        return channel_health_report
    
    def _calculate_channel_health(self, channel, timeframe):
        """個別チャンネルの健全性評価"""
        
        # メッセージ活動量
        message_stats = self.analytics.get_message_statistics(channel['id'], timeframe)
        activity_score = self._score_activity_level(message_stats)
        
        # 参加者エンゲージメント
        engagement_stats = self.analytics.get_engagement_statistics(channel['id'], timeframe)
        engagement_score = self._score_engagement_level(engagement_stats)
        
        # 情報価値・品質
        content_quality = self.analytics.analyze_content_quality(channel['id'], timeframe)
        quality_score = self._score_content_quality(content_quality)
        
        # 目的適合性
        purpose_alignment = self._assess_purpose_alignment(channel, message_stats)
        alignment_score = self._score_purpose_alignment(purpose_alignment)
        
        # 総合スコア計算
        overall_score = (
            activity_score * 0.25 +
            engagement_score * 0.3 +
            quality_score * 0.25 +
            alignment_score * 0.2
        )
        
        return {
            'overall_score': overall_score,
            'activity_score': activity_score,
            'engagement_score': engagement_score,
            'quality_score': quality_score,
            'alignment_score': alignment_score,
            'strengths': self._identify_strengths({
                'activity': activity_score,
                'engagement': engagement_score,
                'quality': quality_score,
                'alignment': alignment_score
            }),
            'issues': self._identify_issues({
                'activity': activity_score,
                'engagement': engagement_score,
                'quality': quality_score,
                'alignment': alignment_score
            }),
            'recommendations': self._generate_recommendations(channel, overall_score)
        }
    
    def optimize_notification_strategy(self, user_preferences, team_patterns):
        """通知戦略の最適化"""
        
        # 個人別通知プロファイルの作成
        personal_profiles = {}
        for user_id, preferences in user_preferences.items():
            personal_profiles[user_id] = self._create_notification_profile(
                user_id, preferences, team_patterns
            )
        
        # チーム全体の通知最適化
        team_optimization = self._optimize_team_notifications(personal_profiles, team_patterns)
        
        # 自動化ルールの設計
        automation_rules = self._design_notification_automation(personal_profiles, team_optimization)
        
        return {
            'personal_profiles': personal_profiles,
            'team_optimization': team_optimization,
            'automation_rules': automation_rules,
            'implementation_plan': self._create_implementation_plan(automation_rules)
        }
    
    def _create_notification_profile(self, user_id, preferences, team_patterns):
        """個人向け通知プロファイル作成"""
        
        # ユーザーの作業パターン分析
        work_patterns = self.analytics.analyze_user_work_patterns(user_id)
        
        # 注意深い時間帯の特定
        focus_hours = self._identify_focus_hours(work_patterns, preferences)
        
        # 緊急度レベルの設定
        urgency_thresholds = {
            'critical': {  # 即座に通知
                'keywords': ['障害', '緊急', 'production', 'down', '停止'],
                'channels': preferences.get('critical_channels', []),
                'time_restrictions': None  # 24/7通知
            },
            'important': {  # 作業時間内に通知
                'keywords': ['レビュー依頼', 'ブロッカー', 'urgent', 'deadline'],
                'channels': preferences.get('important_channels', []),
                'time_restrictions': work_patterns['active_hours']
            },
            'normal': {  # まとめて通知
                'keywords': [],  # その他全て
                'channels': 'all_other_channels',
                'time_restrictions': work_patterns['active_hours'],
                'batch_delivery': True,
                'batch_interval': preferences.get('batch_interval', '2h')
            }
        }
        
        # デバイス別通知設定
        device_settings = {
            'desktop': {
                'during_focus_hours': {
                    'critical_only': True,
                    'popup_notifications': False,
                    'sound_notifications': False
                },
                'outside_focus_hours': {
                    'all_important_and_critical': True,
                    'popup_notifications': True,
                    'sound_notifications': preferences.get('desktop_sounds', True)
                }
            },
            'mobile': {
                'working_hours': {
                    'critical_and_important': True,
                    'push_notifications': True,
                    'quiet_hours': focus_hours
                },
                'off_hours': {
                    'critical_only': True,
                    'push_notifications': preferences.get('off_hours_mobile', False)
                }
            }
        }
        
        return {
            'user_id': user_id,
            'work_patterns': work_patterns,
            'focus_hours': focus_hours,
            'urgency_thresholds': urgency_thresholds,
            'device_settings': device_settings,
            'personalization_score': self._calculate_personalization_effectiveness(preferences)
        }

class IntelligentSlackBot:
    """エンジニア向けインテリジェントSlackBot"""
    
    def __init__(self):
        self.slack_client = SlackClient()
        self.nlp_processor = NLPProcessor()
        self.knowledge_base = KnowledgeBase()
        self.workflow_engine = WorkflowEngine()
        self.learning_system = BotLearningSystem()
    
    def handle_message(self, message, channel, user):
        """メッセージの理解・処理"""
        
        # 意図理解・分類
        intent_analysis = self.nlp_processor.analyze_intent(message)
        
        # ユーザーコンテキストの取得
        user_context = self._get_user_context(user, channel)
        
        # 適切なハンドラーの選択・実行
        if intent_analysis['category'] == 'question':
            response = self._handle_question(message, intent_analysis, user_context)
        
        elif intent_analysis['category'] == 'task_request':
            response = self._handle_task_request(message, intent_analysis, user_context)
        
        elif intent_analysis['category'] == 'information_sharing':
            response = self._handle_information_sharing(message, intent_analysis, user_context)
        
        else:
            response = self._handle_general_interaction(message, intent_analysis, user_context)
        
        # 学習・改善
        self.learning_system.learn_from_interaction(message, response, user_context)
        
        return response
    
    def _handle_question(self, message, intent_analysis, user_context):
        """質問への回答処理"""
        
        question_type = intent_analysis['question_type']
        
        if question_type == 'technical_troubleshooting':
            return self._handle_technical_question(message, intent_analysis, user_context)
        
        elif question_type == 'process_procedure':
            return self._handle_process_question(message, intent_analysis, user_context)
        
        elif question_type == 'knowledge_lookup':
            return self._handle_knowledge_question(message, intent_analysis, user_context)
        
        else:
            return self._handle_general_question(message, intent_analysis, user_context)
    
    def _handle_technical_question(self, message, intent_analysis, user_context):
        """技術的質問への対応"""
        
        # 技術領域・複雑度の分析
        technical_analysis = self._analyze_technical_complexity(message)
        
        if technical_analysis['complexity'] == 'simple':
            # 直接回答可能
            answer = self.knowledge_base.search_answer(
                query=message,
                context=user_context,
                confidence_threshold=0.8
            )
            
            if answer and answer['confidence'] > 0.8:
                return {
                    'type': 'direct_answer',
                    'content': answer['content'],
                    'sources': answer['sources'],
                    'confidence': answer['confidence']
                }
        
        # 複雑な質問 - 専門家への転送
        expert_suggestions = self._find_subject_matter_experts(
            technical_analysis['domain'], user_context
        )
        
        return {
            'type': 'expert_referral',
            'message': f"この質問は{technical_analysis['domain']}の専門的な内容ですね。",
            'suggested_experts': expert_suggestions,
            'related_channels': self._find_relevant_channels(technical_analysis['domain']),
            'search_suggestions': self._generate_search_suggestions(message)
        }
    
    def implement_workflow_automation(self, workflow_definitions):
        """ワークフロー自動化の実装"""
        
        implemented_workflows = []
        
        for workflow in workflow_definitions:
            workflow_handler = self._create_workflow_handler(workflow)
            
            # トリガーの設定
            triggers = self._setup_workflow_triggers(workflow['triggers'])
            
            # アクションチェーンの構築
            action_chain = self._build_action_chain(workflow['actions'])
            
            # エラーハンドリング・監視の設定
            error_handler = self._setup_error_handling(workflow)
            monitoring = self._setup_workflow_monitoring(workflow)
            
            implemented_workflow = {
                'workflow_id': workflow['id'],
                'handler': workflow_handler,
                'triggers': triggers,
                'action_chain': action_chain,
                'error_handler': error_handler,
                'monitoring': monitoring,
                'status': 'active'
            }
            
            implemented_workflows.append(implemented_workflow)
        
        return {
            'implemented_workflows': implemented_workflows,
            'monitoring_dashboard': self._create_workflow_dashboard(implemented_workflows),
            'management_interface': self._create_workflow_management_ui(implemented_workflows)
        }
    
    def optimize_bot_performance(self, performance_data, user_feedback):
        """Bot性能の継続的最適化"""
        
        optimization_areas = {
            'response_accuracy': self._optimize_response_accuracy(performance_data),
            'response_time': self._optimize_response_time(performance_data),
            'user_satisfaction': self._optimize_user_experience(user_feedback),
            'automation_effectiveness': self._optimize_automation(performance_data)
        }
        
        # 機械学習モデルの再訓練
        model_improvements = self.learning_system.retrain_models(
            performance_data, user_feedback
        )
        
        # 新機能・改善点の提案
        improvement_suggestions = self._generate_improvement_suggestions(
            optimization_areas, model_improvements
        )
        
        return {
            'optimization_results': optimization_areas,
            'model_improvements': model_improvements,
            'improvement_suggestions': improvement_suggestions,
            'implementation_roadmap': self._create_improvement_roadmap(improvement_suggestions)
        }

# Bot分析・改善システム
class BotAnalyticsSystem:
    """Bot分析・改善システム"""
    
    def analyze_bot_effectiveness(self, interaction_logs, user_feedback):
        """Bot効果の総合分析"""
        
        effectiveness_metrics = {
            'response_quality': self._measure_response_quality(interaction_logs),
            'user_engagement': self._measure_user_engagement(interaction_logs),
            'automation_impact': self._measure_automation_impact(interaction_logs),
            'knowledge_discovery': self._measure_knowledge_discovery_impact(interaction_logs),
            'team_productivity': self._measure_productivity_impact(interaction_logs)
        }
        
        return {
            'effectiveness_metrics': effectiveness_metrics,
            'improvement_areas': self._identify_improvement_areas(effectiveness_metrics),
            'success_stories': self._identify_success_cases(interaction_logs, user_feedback),
            'roi_analysis': self._calculate_bot_roi(effectiveness_metrics)
        }