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Zhongwei Li
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commands/analyze/groups.md
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685
commands/analyze/groups.md
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---
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name: analyze:groups
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description: Deep analysis of four-tier group behavior, collaboration patterns, and optimization recommendations
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version: 7.0.0
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category: analysis
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---
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# Analyze Groups Command
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Perform comprehensive deep analysis of all four agent groups including collaboration patterns, bottlenecks, optimization opportunities, and actionable recommendations for improving group coordination and performance.
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## What This Command Does
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**Analyzes**:
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1. **Group Collaboration Patterns** - How groups work together, communication patterns, handoff quality
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2. **Performance Bottlenecks** - Where delays occur, which groups need optimization
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3. **Specialization Effectiveness** - Whether groups are specializing appropriately
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4. **Knowledge Flow Analysis** - How knowledge transfers between groups
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5. **Decision Quality Analysis** - Group 2 decision-making effectiveness
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6. **Validation Effectiveness** - Group 4 validation impact on quality
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**Delivers**:
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- Root cause analysis of performance issues
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- Specific optimization recommendations
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- Communication improvement strategies
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- Specialization guidance
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- Actionable next steps
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## Execution Steps
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### Step 1: Load Comprehensive Data
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```python
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from lib.group_collaboration_system import get_group_collaboration_stats, analyze_workflow_efficiency
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from lib.group_performance_tracker import get_group_performance, compare_groups, analyze_workflow_efficiency as group_workflow
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from lib.inter_group_knowledge_transfer import get_knowledge_transfer_stats, get_transfer_effectiveness
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from lib.group_specialization_learner import get_specialization_profile, get_recommended_group_for_task, get_learning_insights
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from lib.agent_feedback_system import get_feedback_stats
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from lib.decision_explainer import get_all_explanations
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from lib.proactive_suggester import get_statistics as get_suggestion_stats
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# Gather all data for last 100 tasks
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collaboration_stats = get_group_collaboration_stats()
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workflow_efficiency = analyze_workflow_efficiency()
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knowledge_effectiveness = get_transfer_effectiveness()
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learning_insights = get_learning_insights()
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suggestion_stats = get_suggestion_stats()
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```
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### Step 2: Analyze Group Collaboration Patterns
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```python
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def analyze_collaboration_patterns(collab_stats):
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"""Identify collaboration patterns and issues"""
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patterns_found = []
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issues_found = []
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# Pattern 1: Sequential Flow (Normal)
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if collab_stats['group_1_to_2']['success_rate'] > 0.9 and \
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collab_stats['group_2_to_3']['success_rate'] > 0.9 and \
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collab_stats['group_3_to_4']['success_rate'] > 0.9:
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patterns_found.append({
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"pattern": "healthy_sequential_flow",
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"description": "Groups collaborate sequentially with high success",
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"status": "excellent"
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})
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# Pattern 2: Feedback Loop Effectiveness
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feedback_loops = [
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collab_stats.get('group_4_to_1', {}),
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collab_stats.get('group_4_to_2', {}),
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collab_stats.get('group_4_to_3', {})
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]
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avg_feedback_effectiveness = sum(loop.get('effectiveness', 0) for loop in feedback_loops) / 3
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if avg_feedback_effectiveness < 0.7:
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issues_found.append({
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"issue": "weak_feedback_loops",
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"severity": "medium",
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"description": "Group 4 feedback not effectively improving other groups",
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"recommendation": "Review feedback quality and actionability"
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})
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# Pattern 3: Bottleneck Detection
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communication_times = {
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"G1->G2": collab_stats['group_1_to_2'].get('avg_time_seconds', 0),
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"G2->G3": collab_stats['group_2_to_3'].get('avg_time_seconds', 0),
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"G3->G4": collab_stats['group_3_to_4'].get('avg_time_seconds', 0)
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}
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max_time = max(communication_times.values())
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for flow, time in communication_times.items():
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if time > max_time * 0.7: # More than 70% of max
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issues_found.append({
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"issue": "communication_bottleneck",
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"severity": "high",
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"location": flow,
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"description": f"Communication delay in {flow}: {time}s",
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"recommendation": f"Optimize {flow.split('->')[0]} output preparation"
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})
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return patterns_found, issues_found
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```
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### Step 3: Analyze Performance Bottlenecks
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```python
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def identify_bottlenecks():
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"""Identify which groups are performance bottlenecks"""
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bottlenecks = []
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for group_num in [1, 2, 3, 4]:
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perf = get_group_performance(group_num)
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# Check success rate
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if perf.get('success_rate', 1.0) < 0.8:
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bottlenecks.append({
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"group": group_num,
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"issue": "low_success_rate",
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"value": perf['success_rate'],
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"severity": "high",
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"recommendation": "Review group training and specialization"
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})
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# Check execution time
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if perf.get('avg_execution_time', 0) > 300: # 5 minutes
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bottlenecks.append({
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"group": group_num,
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"issue": "slow_execution",
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"value": perf['avg_execution_time'],
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"severity": "medium",
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"recommendation": "Profile and optimize slow operations"
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})
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# Check quality output
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if perf.get('avg_quality_score', 100) < 75:
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bottlenecks.append({
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"group": group_num,
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"issue": "low_quality_output",
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"value": perf['avg_quality_score'],
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"severity": "high",
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"recommendation": "Improve group capabilities or adjust expectations"
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})
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return bottlenecks
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```
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### Step 4: Analyze Specialization Effectiveness
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```python
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def analyze_specialization():
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"""Check if groups are developing appropriate specializations"""
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specialization_analysis = {}
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for group_num in [1, 2, 3, 4]:
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profile = get_specialization_profile(group_num)
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specializations = profile.get('specializations', [])
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task_count = profile.get('total_tasks', 0)
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# Ideal: 3-5 clear specializations after 100+ tasks
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if task_count < 50:
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status = "insufficient_data"
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recommendation = f"Need {50 - task_count} more tasks to identify specializations"
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elif len(specializations) == 0:
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status = "no_specialization"
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recommendation = "Group not developing specializations - may need more diverse tasks"
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elif len(specializations) < 3:
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status = "emerging"
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recommendation = "Specializations emerging - continue diverse task exposure"
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elif len(specializations) <= 5:
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status = "optimal"
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recommendation = "Good specialization balance - maintain current task distribution"
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else:
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status = "over_specialized"
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recommendation = "Too many specializations - may indicate lack of focus"
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specialization_analysis[f"Group {group_num}"] = {
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"status": status,
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"specializations": specializations,
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"task_count": task_count,
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"recommendation": recommendation
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}
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return specialization_analysis
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```
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### Step 5: Analyze Knowledge Flow
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```python
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def analyze_knowledge_flow(knowledge_stats):
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"""Analyze how knowledge flows between groups"""
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flow_analysis = {
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"total_knowledge": knowledge_stats.get('total_knowledge', 0),
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"successful_transfers": knowledge_stats.get('successful_transfers', 0),
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"transfer_success_rate": knowledge_stats.get('transfer_success_rate', 0),
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"flow_patterns": []
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}
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# Identify dominant knowledge sources
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sources = {}
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for transfer in knowledge_stats.get('transfers', []):
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source = transfer.get('source_group')
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sources[source] = sources.get(source, 0) + 1
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# Check if knowledge is distributed or concentrated
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if sources:
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max_source = max(sources.values())
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if max_source > sum(sources.values()) * 0.6:
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flow_analysis['flow_patterns'].append({
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"pattern": "concentrated_source",
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"description": f"Group {max(sources, key=sources.get)} is primary knowledge source ({max_source} items)",
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"recommendation": "Encourage knowledge sharing from other groups"
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})
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else:
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flow_analysis['flow_patterns'].append({
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"pattern": "distributed_sources",
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"description": "Knowledge well-distributed across groups",
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"status": "healthy"
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})
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# Check transfer effectiveness
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if flow_analysis['transfer_success_rate'] < 0.7:
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flow_analysis['flow_patterns'].append({
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"pattern": "low_transfer_effectiveness",
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"severity": "medium",
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"description": f"Knowledge transfer success rate: {flow_analysis['transfer_success_rate']:.1%}",
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"recommendation": "Improve knowledge quality, context, and applicability"
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})
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return flow_analysis
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```
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### Step 6: Decision Quality Analysis (Group 2)
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```python
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def analyze_decision_quality():
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"""Analyze Group 2 decision-making effectiveness"""
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group2_perf = get_group_performance(2)
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explanations = get_all_explanations() # Get recent decision explanations
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analysis = {
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"total_decisions": group2_perf.get('total_tasks', 0),
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"decision_accuracy": group2_perf.get('success_rate', 0),
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"avg_confidence": group2_perf.get('avg_confidence', 0),
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"user_alignment": 0, # From user_preference_learner
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"issues": [],
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"strengths": []
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}
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# Check decision accuracy
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if analysis['decision_accuracy'] < 0.85:
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analysis['issues'].append({
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"issue": "low_decision_accuracy",
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"value": analysis['decision_accuracy'],
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"severity": "high",
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"description": "Decisions not leading to successful outcomes",
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"recommendation": "Review decision criteria and incorporate more historical data"
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})
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else:
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analysis['strengths'].append("High decision accuracy")
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# Check confidence calibration
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if analysis['avg_confidence'] > 0.9 and analysis['decision_accuracy'] < 0.85:
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analysis['issues'].append({
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"issue": "overconfident_decisions",
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"severity": "medium",
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"description": "Confidence higher than actual success rate",
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"recommendation": "Calibrate confidence scoring - add uncertainty factors"
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})
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# Check explanation quality
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if len(explanations) > 0:
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avg_explanation_completeness = sum(
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len(e.get('why_chosen', [])) + len(e.get('why_not_alternatives', []))
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for e in explanations
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) / len(explanations)
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if avg_explanation_completeness < 5:
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analysis['issues'].append({
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"issue": "sparse_explanations",
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"severity": "low",
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"description": "Decision explanations lack detail",
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"recommendation": "Enhance decision_explainer to provide more comprehensive reasoning"
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})
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return analysis
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```
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### Step 7: Validation Effectiveness Analysis (Group 4)
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```python
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def analyze_validation_effectiveness():
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"""Analyze Group 4 validation impact"""
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group4_perf = get_group_performance(4)
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analysis = {
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"total_validations": group4_perf.get('total_tasks', 0),
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"go_rate": 0, # Percentage of GO decisions
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"nogo_rate": 0, # Percentage of NO-GO decisions
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"avg_quality_score": group4_perf.get('avg_quality_score', 0),
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"feedback_effectiveness": 0,
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"issues": [],
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"impact": []
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}
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# Ideal GO rate: 70-85% (too high = not catching issues, too low = too strict)
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# This data would come from validation results
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# For now, use placeholders
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if analysis['go_rate'] > 0.9:
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analysis['issues'].append({
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"issue": "validation_too_lenient",
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"severity": "medium",
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"description": f"GO rate too high ({analysis['go_rate']:.1%}) - may miss quality issues",
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"recommendation": "Review validation thresholds and criteria"
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})
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elif analysis['go_rate'] < 0.6:
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analysis['issues'].append({
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"issue": "validation_too_strict",
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"severity": "low",
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"description": f"GO rate too low ({analysis['go_rate']:.1%}) - may cause unnecessary iterations",
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"recommendation": "Consider relaxing validation thresholds or improving Group 3 output quality"
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})
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# Check if validation is improving quality
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# Compare quality scores before/after validation feedback
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# This would require analysis of quality trends after Group 4 feedback
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return analysis
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```
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### Step 8: Generate Comprehensive Analysis Report
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**Report Structure**:
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```markdown
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# Four-Tier Group Analysis Report
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Generated: {timestamp}
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Analysis Period: Last {n} tasks
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## Executive Summary
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**Overall Health**: {score}/100 ({status})
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**Key Findings**:
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1. {finding_1}
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2. {finding_2}
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3. {finding_3}
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**Critical Issues**: {critical_count}
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**Optimization Opportunities**: {opportunity_count}
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---
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## 1. Collaboration Pattern Analysis
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### Identified Patterns
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#### Pattern: {pattern_name}
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**Status**: {excellent/good/needs_attention}
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**Description**: {description}
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**Impact**: {impact_description}
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### Collaboration Issues
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#### Issue: {issue_name}
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**Severity**: {high/medium/low}
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**Location**: {group_flow}
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**Description**: {detailed_description}
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**Root Cause Analysis**:
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- {cause_1}
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- {cause_2}
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**Recommendation**:
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- {recommendation_1}
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- {recommendation_2}
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**Expected Improvement**: {improvement_description}
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---
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## 2. Performance Bottleneck Analysis
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### Bottlenecks Identified
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#### Bottleneck: {bottleneck_name}
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**Group**: Group {group_num} ({group_name})
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**Type**: {slow_execution/low_success/poor_quality}
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**Severity**: {high/medium/low}
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**Metrics**:
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- Current Performance: {metric_value}
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- Expected Performance: {target_value}
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- Gap: {gap_value}
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**Impact on System**:
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{impact_description}
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**Root Cause**:
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{root_cause_analysis}
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**Optimization Strategy**:
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1. **Immediate Actions** (Next 1-5 tasks):
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- {action_1}
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- {action_2}
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2. **Short-term Improvements** (Next 10-20 tasks):
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- {improvement_1}
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- {improvement_2}
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3. **Long-term Optimization** (Next 50+ tasks):
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- {strategy_1}
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- {strategy_2}
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**Expected Results**:
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- Performance Improvement: {improvement}%
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- Time Savings: {time} per task
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- Quality Impact: +{points} points
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---
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## 3. Specialization Analysis
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### Group Specialization Status
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#### Group 1 (Strategic Analysis & Intelligence)
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**Status**: {optimal/emerging/no_specialization/over_specialized}
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**Task Count**: {count}
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**Current Specializations**:
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1. {specialization_1}: {success_rate}% success, {count} tasks
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2. {specialization_2}: {success_rate}% success, {count} tasks
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3. {specialization_3}: {success_rate}% success, {count} tasks
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**Analysis**:
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{analysis_description}
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**Recommendation**:
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{recommendation}
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---
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(Repeat for Groups 2, 3, 4)
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---
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## 4. Knowledge Flow Analysis
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### Knowledge Transfer Effectiveness
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**Total Knowledge Base**: {count} items
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**Successful Transfers**: {success_count} ({success_rate}%)
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**Knowledge Sources**:
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- Group 1: {count} items
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- Group 2: {count} items
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- Group 3: {count} items
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- Group 4: {count} items
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### Flow Patterns
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#### Pattern: {pattern_name}
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**Description**: {description}
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**Impact**: {positive/negative}
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**Recommendation**: {recommendation}
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### Knowledge Gaps
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**Identified Gaps**:
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1. {gap_description} - Missing knowledge in {area}
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2. {gap_description} - Underutilized knowledge from {source}
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**Impact**: {impact_description}
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**Actions**:
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- {action_1}
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- {action_2}
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---
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## 5. Decision Quality Analysis (Group 2)
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### Decision-Making Effectiveness
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**Total Decisions**: {count}
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**Decision Accuracy**: {accuracy}%
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**Average Confidence**: {confidence}
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**User Alignment**: {alignment}%
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### Strengths
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- {strength_1}
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- {strength_2}
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### Areas for Improvement
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#### Issue: {issue_name}
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**Severity**: {severity}
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||||
**Description**: {description}
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||||
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**Analysis**:
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||||
{detailed_analysis}
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**Recommendation**:
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{actionable_recommendation}
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**Expected Impact**:
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- Decision Accuracy: +{improvement}%
|
||||
- User Satisfaction: +{improvement}%
|
||||
|
||||
---
|
||||
|
||||
## 6. Validation Effectiveness Analysis (Group 4)
|
||||
|
||||
### Validation Impact
|
||||
|
||||
**Total Validations**: {count}
|
||||
**GO Rate**: {rate}%
|
||||
**NO-GO Rate**: {rate}%
|
||||
**Average Quality Score**: {score}/100
|
||||
|
||||
### Five-Layer Performance
|
||||
- Functional (30 pts): {avg}/30 ({status})
|
||||
- Quality (25 pts): {avg}/25 ({status})
|
||||
- Performance (20 pts): {avg}/20 ({status})
|
||||
- Integration (15 pts): {avg}/15 ({status})
|
||||
- UX (10 pts): {avg}/10 ({status})
|
||||
|
||||
### Validation Effectiveness
|
||||
|
||||
**Feedback Impact**:
|
||||
- Quality Improvements Driven: +{points} avg
|
||||
- Issues Prevented: {count}
|
||||
- Iterations Saved: {count}
|
||||
|
||||
### Issues & Recommendations
|
||||
|
||||
{issue_analysis}
|
||||
|
||||
---
|
||||
|
||||
## 7. Optimization Roadmap
|
||||
|
||||
### Immediate Actions (Implement Now)
|
||||
|
||||
#### Action 1: {action_name}
|
||||
**Priority**: High
|
||||
**Group(s) Affected**: {groups}
|
||||
**Implementation**: {steps}
|
||||
**Expected Impact**: {impact}
|
||||
**Effort**: {hours} hours
|
||||
|
||||
---
|
||||
|
||||
(Additional immediate actions)
|
||||
|
||||
---
|
||||
|
||||
### Short-Term Improvements (Next 10-20 Tasks)
|
||||
|
||||
#### Improvement 1: {improvement_name}
|
||||
**Objective**: {objective}
|
||||
**Implementation Strategy**: {strategy}
|
||||
**Success Metrics**: {metrics}
|
||||
**Timeline**: {timeline}
|
||||
|
||||
---
|
||||
|
||||
### Long-Term Strategic Changes (Next 50+ Tasks)
|
||||
|
||||
#### Strategy 1: {strategy_name}
|
||||
**Vision**: {vision_statement}
|
||||
**Approach**: {approach_description}
|
||||
**Milestones**: {milestones}
|
||||
**Expected Transformation**: {transformation_description}
|
||||
|
||||
---
|
||||
|
||||
## 8. Success Metrics & KPIs
|
||||
|
||||
### Target Metrics (30-day goals)
|
||||
|
||||
| Metric | Current | Target | Gap |
|
||||
|--------|---------|--------|-----|
|
||||
| Overall Quality Score | {current} | {target} | {gap} |
|
||||
| Average Iterations | {current} | {target} | {gap} |
|
||||
| Decision Accuracy | {current}% | {target}% | {gap}% |
|
||||
| Communication Success | {current}% | {target}% | {gap}% |
|
||||
| GO Rate | {current}% | {target}% | {gap}% |
|
||||
|
||||
### Tracking Plan
|
||||
|
||||
**Weekly Checkpoints**:
|
||||
- Run `/monitor:groups` weekly
|
||||
- Track KPI progress
|
||||
- Adjust strategies as needed
|
||||
|
||||
**Monthly Reviews**:
|
||||
- Run `/analyze:groups` monthly
|
||||
- Comprehensive performance review
|
||||
- Strategic adjustments
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
**System Status**: {status}
|
||||
|
||||
**Key Takeaways**:
|
||||
1. {takeaway_1}
|
||||
2. {takeaway_2}
|
||||
3. {takeaway_3}
|
||||
|
||||
**Next Steps**:
|
||||
1. {next_step_1}
|
||||
2. {next_step_2}
|
||||
3. {next_step_3}
|
||||
|
||||
**Confidence in Recommendations**: {confidence}%
|
||||
|
||||
---
|
||||
|
||||
Report Path: .claude/data/reports/group-analysis-{date}.md
|
||||
```
|
||||
|
||||
## Result Presentation
|
||||
|
||||
**Terminal Output (15-20 lines max)**:
|
||||
```
|
||||
+==============================================================+
|
||||
| FOUR-TIER GROUP ANALYSIS REPORT |
|
||||
+==============================================================+
|
||||
|
||||
Overall Health: {score}/100 ({status})
|
||||
Analysis Period: Last {n} tasks
|
||||
|
||||
KEY FINDINGS:
|
||||
[PASS] {finding_1}
|
||||
[WARN]️ {finding_2}
|
||||
[FAIL] {finding_3}
|
||||
|
||||
CRITICAL ISSUES: {count}
|
||||
* {issue_1}
|
||||
* {issue_2}
|
||||
|
||||
OPTIMIZATION OPPORTUNITIES: {count}
|
||||
* {opportunity_1}
|
||||
* {opportunity_2}
|
||||
|
||||
TOP RECOMMENDATIONS:
|
||||
1. [{priority}] {recommendation_1}
|
||||
2. [{priority}] {recommendation_2}
|
||||
|
||||
📄 Detailed Analysis: .claude/data/reports/group-analysis-{date}.md
|
||||
⏱️ Execution Time: {time}s
|
||||
```
|
||||
|
||||
**File Report**: Save complete analysis to `.claude/data/reports/group-analysis-YYYY-MM-DD.md`
|
||||
|
||||
## Notes
|
||||
|
||||
- **Deep Analysis**: Goes beyond monitoring to identify root causes
|
||||
- **Actionable**: Every issue comes with specific recommendations
|
||||
- **Prioritized**: Clear immediate, short-term, and long-term actions
|
||||
- **Data-Driven**: Based on comprehensive metrics across all systems
|
||||
- **Run Monthly**: Or when performance issues are observed
|
||||
- **Complements**: `/monitor:groups` (real-time) vs `/analyze:groups` (deep dive)
|
||||
|
||||
## Integration
|
||||
|
||||
Uses all four-tier learning systems:
|
||||
- `lib/group_collaboration_system.py`
|
||||
- `lib/group_performance_tracker.py`
|
||||
- `lib/inter_group_knowledge_transfer.py`
|
||||
- `lib/group_specialization_learner.py`
|
||||
- `lib/agent_performance_tracker.py`
|
||||
- `lib/agent_feedback_system.py`
|
||||
- `lib/decision_explainer.py`
|
||||
- `lib/proactive_suggester.py`
|
||||
Reference in New Issue
Block a user