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# Analysis Commands Compliance Report
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## Overview
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Reviewed all command files in `.claude/commands/analysis/` directory to ensure proper usage of:
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- `mcp__claude-flow__*` tools (preferred)
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- `npx claude-flow` commands (as fallback)
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- No direct implementation calls
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## Files Reviewed
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### 1. token-efficiency.md
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**Status**: ✅ Updated
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**Changes Made**:
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- Replaced `npx ruv-swarm hook session-end --export-metrics` with proper MCP tool call
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- Updated to: `Tool: mcp__claude-flow__token_usage` with appropriate parameters
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- Maintained result format and context
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**Before**:
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```bash
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npx ruv-swarm hook session-end --export-metrics
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```
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**After**:
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```
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Tool: mcp__claude-flow__token_usage
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Parameters: {"operation": "session", "timeframe": "24h"}
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```
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### 2. performance-bottlenecks.md
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**Status**: ✅ Compliant (No changes needed)
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**Reason**: Already uses proper `mcp__claude-flow__task_results` tool format
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## Summary
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- **Total files reviewed**: 2
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- **Files updated**: 1
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- **Files already compliant**: 1
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- **Compliance rate after updates**: 100%
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## Compliance Patterns Enforced
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1. **MCP Tool Usage**: All direct tool calls now use `mcp__claude-flow__*` format
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2. **Parameter Format**: JSON parameters properly structured
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3. **Command Context**: Preserved original functionality and expected results
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4. **Documentation**: Maintained clarity and examples
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## Recommendations
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1. All analysis commands now follow the proper pattern
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2. No direct bash commands or implementation calls remain
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3. Token usage analysis properly integrated with MCP tools
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4. Performance analysis already using correct tool format
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The analysis directory is now fully compliant with the Claude Flow command standards.
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# Performance Bottleneck Analysis
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## Purpose
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Identify and resolve performance bottlenecks in your development workflow.
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## Automated Analysis
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### 1. Real-time Detection
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The post-task hook automatically analyzes:
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- Execution time vs. complexity
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- Agent utilization rates
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- Resource constraints
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- Operation patterns
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### 2. Common Bottlenecks
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**Time Bottlenecks:**
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- Tasks taking > 5 minutes
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- Sequential operations that could parallelize
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- Redundant file operations
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**Coordination Bottlenecks:**
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- Single agent for complex tasks
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- Unbalanced agent workloads
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- Poor topology selection
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**Resource Bottlenecks:**
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- High operation count (> 100)
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- Memory constraints
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- I/O limitations
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### 3. Improvement Suggestions
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```
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Tool: mcp__claude-flow__task_results
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Parameters: {"taskId": "task-123", "format": "detailed"}
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Result includes:
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{
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"bottlenecks": [
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{
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"type": "coordination",
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"severity": "high",
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"description": "Single agent used for complex task",
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"recommendation": "Spawn specialized agents for parallel work"
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}
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],
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"improvements": [
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{
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"area": "execution_time",
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"suggestion": "Use parallel task execution",
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"expectedImprovement": "30-50% time reduction"
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}
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]
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}
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```
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## Continuous Optimization
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The system learns from each task to prevent future bottlenecks!
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# Token Usage Optimization
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## Purpose
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Reduce token consumption while maintaining quality through intelligent coordination.
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## Optimization Strategies
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### 1. Smart Caching
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- Search results cached for 5 minutes
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- File content cached during session
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- Pattern recognition reduces redundant searches
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### 2. Efficient Coordination
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- Agents share context automatically
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- Avoid duplicate file reads
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- Batch related operations
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### 3. Measurement & Tracking
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```bash
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# Check token savings after session
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Tool: mcp__claude-flow__token_usage
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Parameters: {"operation": "session", "timeframe": "24h"}
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# Result shows:
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{
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"metrics": {
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"tokensSaved": 15420,
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"operations": 45,
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"efficiency": "343 tokens/operation"
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}
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}
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```
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## Best Practices
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1. **Use Task tool** for complex searches
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2. **Enable caching** in pre-search hooks
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3. **Batch operations** when possible
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4. **Review session summaries** for insights
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## Token Reduction Results
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- 📉 32.3% average token reduction
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- 🎯 More focused operations
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- 🔄 Intelligent result reuse
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- 📊 Cumulative improvements
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