aibuilder.sh
commandby lancejames221b

Recent

Show recently used hAIveMind commands with outcomes and pattern analysis

Installs: 0
Used in: 1 repos
Updated: 1d ago
$npx ai-builder add command lancejames221b/recent

Installs to .claude/commands/recent.md

# recent - Command History & Pattern Analysis

## Purpose
Intelligent command history system that shows your recently used hAIveMind commands with success rates, execution times, and pattern analysis to help optimize your workflow and identify improvement opportunities.

## When to Use
- **Performance Analysis**: Review command success rates and execution times
- **Workflow Optimization**: Identify patterns in your command usage for efficiency improvements
- **Troubleshooting**: Analyze recent failures to identify recurring issues
- **Learning**: Understand your usage patterns and discover optimization opportunities
- **Audit Trail**: Review recent actions for compliance or debugging purposes
- **Pattern Recognition**: Discover successful command sequences for future use

## Syntax
```
recent [limit]
```

## Parameters
- **limit** (optional): Number of recent commands to display (default: 10, max: 50)
  - Examples: `recent 5`, `recent 20`, `recent 50`

## Intelligent Analysis Features

### Usage Pattern Recognition
- **Command Sequences**: Identifies common command patterns and their success rates
- **Timing Analysis**: Analyzes time between commands to identify workflow efficiency
- **Success Correlation**: Shows which command combinations lead to best outcomes
- **Context Awareness**: Correlates command usage with project types and situations
- **Trend Analysis**: Identifies changes in usage patterns over time

### Performance Insights
- **Execution Time Tracking**: Shows how long each command took to complete
- **Success Rate Analysis**: Tracks which commands succeed most often
- **Error Pattern Detection**: Identifies recurring failure patterns
- **Efficiency Metrics**: Calculates workflow efficiency and suggests improvements
- **Comparative Analysis**: Compares your patterns with collective best practices

## Real-World Examples

### Basic Recent Command History
```
recent
```
**Result**: Last 10 commands with timestamps, success status, execution times, and context

### Extended History Analysis
```
recent 25
```
**Result**: Last 25 commands with detailed pattern analysis, success trends, and optimization suggestions

### Quick Recent Check
```
recent 5
```
**Result**: Last 5 commands with focus on immediate recent activity and quick insights

## Expected Output

### Standard Recent Commands View
```
ā±ļø Recent Command History - 2025-01-24 14:30:00

šŸ“Š Usage Summary (Last 10 commands):
   ā†³ Total Commands: 10
   ā†³ Success Rate: 90% (9 successful, 1 failed)
   ā†³ Average Execution Time: 1.8 seconds
   ā†³ Most Used: hv-status (3x), remember (2x), hv-query (2x)

šŸ”„ Recent Commands:

1. āœ“ hv-status --detailed                    14:28:45  1.2s  general
   ā†³ Success: Checked collective health, found 2 agents with high response times
   ā†³ Context: Routine health monitoring
   ā†³ Follow-up: Led to investigation of performance issues

2. āœ“ hv-broadcast "Database optimization completed" infrastructure info  14:25:30  0.8s  infrastructure
   ā†³ Success: Notified 12 agents about database improvements
   ā†³ Context: Completing database optimization project
   ā†³ Impact: Team aware of performance improvements

3. āœ“ remember "Database query optimization reduced response time by 60%" infrastructure  14:23:15  0.5s  documentation
   ā†³ Success: Documented optimization results for future reference
   ā†³ Context: Preserving successful optimization techniques
   ā†³ Tags: database, optimization, performance

4. āœ“ hv-delegate "Monitor database performance metrics" monitoring  14:20:45  2.1s  task_assignment
   ā†³ Success: Assigned monitoring task to monitoring specialists
   ā†³ Context: Ensuring optimization results are tracked
   ā†³ Assigned to: monitoring-agent, grafana-agent

5. āœ“ hv-query "database performance optimization techniques"  14:18:30  3.2s  research
   ā†³ Success: Found 8 relevant memories with optimization strategies
   ā†³ Context: Researching before implementing database changes
   ā†³ Results: Query indexing, connection pooling, cache strategies

6. āœ— hv-sync --force                         14:15:20  timeout  system_maintenance
   ā†³ Failed: Timeout during configuration synchronization
   ā†³ Context: Attempting to update agent configurations
   ā†³ Error: Network connectivity issues with 3 agents
   ā†³ Resolution: Retry succeeded after network stabilized

7. āœ“ recall "database issues last 7 days" incidents  14:12:10  1.9s  investigation
   ā†³ Success: Retrieved 4 recent database-related incidents
   ā†³ Context: Understanding recent database problems before optimization
   ā†³ Findings: Connection pool issues, slow query patterns

8. āœ“ hv-status                               14:10:05  0.9s  general
   ā†³ Success: Quick health check before starting database work
   ā†³ Context: Verifying system stability before changes
   ā†³ Status: All systems operational, ready for optimization

9. āœ“ help hv-delegate                        14:08:30  0.3s  learning
   ā†³ Success: Reviewed delegation syntax and best practices
   ā†³ Context: Learning proper task delegation techniques
   ā†³ Outcome: Better understanding of specialist assignment

10. āœ“ remember "Starting database optimization project" infrastructure  14:05:15  0.4s  documentation
    ā†³ Success: Documented project initiation for tracking
    ā†³ Context: Beginning systematic database performance improvement
    ā†³ Project: Database optimization initiative

šŸ“ˆ Pattern Analysis:

šŸŽÆ Successful Command Sequences (Last 24 hours):
   1. help ā†’ hv-query ā†’ hv-delegate ā†’ hv-broadcast (Success Rate: 100%, Used 3 times)
      ā†³ Pattern: Learn ā†’ Research ā†’ Assign ā†’ Communicate
      ā†³ Average Duration: 8.2 minutes
      ā†³ Effectiveness: High - leads to well-informed actions

   2. hv-status ā†’ recall ā†’ remember (Success Rate: 95%, Used 4 times)
      ā†³ Pattern: Check ā†’ Research ā†’ Document
      ā†³ Average Duration: 4.1 minutes
      ā†³ Effectiveness: High - good for investigation workflows

   3. remember ā†’ hv-broadcast ā†’ hv-delegate (Success Rate: 90%, Used 2 times)
      ā†³ Pattern: Document ā†’ Communicate ā†’ Assign
      ā†³ Average Duration: 3.8 minutes
      ā†³ Effectiveness: Good - effective for sharing and follow-up

ā±ļø Timing Patterns:
   ā†³ Average time between commands: 3.2 minutes
   ā†³ Fastest sequence: help ā†’ hv-query (30 seconds)
   ā†³ Longest gap: 15 minutes (between investigation and action)
   ā†³ Most efficient hour: 14:00-15:00 (current session)

šŸš€ Performance Insights:
   ā†³ Commands with <1s execution time: 60% (very efficient)
   ā†³ Commands with >3s execution time: 20% (investigate optimization)
   ā†³ Network-dependent commands: 30% (consider local caching)
   ā†³ Documentation commands: 30% (good knowledge preservation)

šŸ’” Optimization Recommendations:

1. šŸŽÆ Workflow Efficiency
   ā†³ Your "help ā†’ research ā†’ act" pattern is highly effective (100% success)
   ā†³ Consider standardizing this approach for complex tasks
   ā†³ Time savings: ~15% by following proven patterns

2. āš” Command Performance
   ā†³ hv-query commands taking >3s - consider more specific search terms
   ā†³ hv-sync timeout suggests network optimization needed
   ā†³ Batch similar commands to reduce context switching

3. šŸ“š Documentation Habits
   ā†³ Good documentation frequency (30% of commands)
   ā†³ Consider adding more context tags to remember commands
   ā†³ Document failed attempts for collective learning

4. šŸ”„ Pattern Optimization
   ā†³ Your command sequences show good planning and follow-through
   ā†³ Consider using workflows command for complex multi-step procedures
   ā†³ Share successful patterns with team via hv-broadcast

šŸ“Š Comparative Analysis:
   ā†³ Your success rate (90%) is above collective average (87%)
   ā†³ Your documentation rate (30%) is excellent (collective: 18%)
   ā†³ Your command diversity shows good tool utilization
   ā†³ Time between commands suggests thoughtful execution

šŸŽÆ Recommended Next Steps:
   1. Continue current documentation practices - they're excellent
   2. Investigate hv-sync timeout issues for better reliability
   3. Consider using workflows for your successful command patterns
   4. Share your effective patterns with team via hv-broadcast
```

### Extended History with Deep Analysis
```
ā±ļø Extended Command History Analysis - Last 25 Commands

šŸ“Š Comprehensive Usage Statistics:
   ā†³ Total Commands: 25 (last 4 hours)
   ā†³ Success Rate: 88% (22 successful, 3 failed)
   ā†³ Average Execution Time: 2.1 seconds
   ā†³ Command Diversity: 7 different commands used
   ā†³ Most Active Period: 14:00-15:00 (12 commands)

šŸ” Command Frequency Analysis:
   1. hv-status: 8 uses (32%) - Avg: 1.1s, Success: 100%
   2. remember: 5 uses (20%) - Avg: 0.6s, Success: 100%
   3. hv-query: 4 uses (16%) - Avg: 2.8s, Success: 75%
   4. hv-broadcast: 3 uses (12%) - Avg: 0.9s, Success: 100%
   5. hv-delegate: 2 uses (8%) - Avg: 2.3s, Success: 100%
   6. recall: 2 uses (8%) - Avg: 1.7s, Success: 100%
   7. hv-sync: 1 use (4%) - Avg: timeout, Success: 0%

šŸ“ˆ Trend Analysis (4-hour window):
   ā†³ Command frequency increasing (2 ā†’ 5 ā†’ 8 ā†’ 10 per hour)
   ā†³ Success rate stable around 88-92%
   ā†³ Documentation rate increasing (good trend)
   ā†³ Research-heavy period (multiple hv-query commands)

šŸŽÆ Advanced Pattern Recognition:

Workflow Pattern: "Database Optimization Project"
   Timeline: 14:05 - 14:30 (25 minutes)
   Commands: remember ā†’ help ā†’ hv-query ā†’ recall ā†’ hv-delegate ā†’ remember ā†’ hv-broadcast ā†’ hv-status
   Success Rate: 87.5% (7/8 successful)
   Outcome: Successful database optimization with team coordination
   
   Pattern Breakdown:
   1. Project initiation (remember) āœ“
   2. Learning phase (help) āœ“
   3. Research phase (hv-query) āœ“
   4. Historical analysis (recall) āœ“
   5. Task assignment (hv-delegate) āœ“
   6. Documentation (remember) āœ“
   7. Team communication (hv-broadcast) āœ“
   8. Verification (hv-status) āœ“

Success Factors:
   āœ“ Systematic approach with clear phases
   āœ“ Good balance of research and action
   āœ“ Proper documentation at key points
   āœ“ Team communication and coordination
   āœ“ Verification of outcomes

šŸšØ Failure Analysis:

Failed Command: hv-sync --force (14:15:20)
   ā†³ Error Type: Network timeout
   ā†³ Context: System maintenance during active work
   ā†³ Impact: Delayed workflow by 5 minutes
   ā†³ Resolution: Retry after network stabilization
   ā†³ Prevention: Check network status before sync operations

Failed Commands Pattern:
   ā†³ 3 failures out of 25 commands (12% failure rate)
   ā†³ All failures were network-related (hv-sync, hv-query timeouts)
   ā†³ Failures clustered around 14:15-14:20 (network issue period)
   ā†³ No command logic or parameter errors

šŸŽÆ Optimization Opportunities:

1. Network Reliability (Priority: High)
   ā†³ 100% of failures were network-related
   ā†³ Consider network diagnostics before critical operations
   ā†³ Implement retry logic for network-dependent commands
   ā†³ Monitor network status: hv-status --network

2. Query Optimization (Priority: Medium)
   ā†³ hv-query commands averaging 2.8s (above optimal)
   ā†³ Use more specific search terms to reduce search time
   ā†³ Consider caching frequently accessed information
   ā†³ Break complex queries into smaller, focused searches

3. Workflow Standardization (Priority: Low)
   ā†³ Your successful patterns could be formalized into workflows
   ā†³ Database optimization pattern highly successful (87.5%)
   ā†³ Consider creating custom workflow templates
   ā†³ Share successful patterns with collective via documentation

šŸ“Š Performance Benchmarking:

Your Performance vs. Collective Averages:
   ā†³ Success Rate: 88% (You) vs 87% (Collective) - Above Average āœ“
   ā†³ Execution Time: 2.1s (You) vs 1.8s (Collective) - Slightly Slower
   ā†³ Documentation Rate: 20% (You) vs 18% (Collective) - Above Average āœ“
   ā†³ Command Diversity: 7 types (You) vs 5.2 (Collective) - Above Average āœ“

Strengths:
   āœ“ Excellent documentation habits
   āœ“ Good command diversity and tool utilization
   āœ“ Systematic approach to complex tasks
   āœ“ Strong success rate despite network challenges

Improvement Areas:
   ā†³ Network connectivity optimization
   ā†³ Query efficiency improvement
   ā†³ Command execution speed optimization
```

## Advanced Analysis Features

### Pattern Learning and Prediction
- **Success Prediction**: Predicts likelihood of command success based on context and history
- **Workflow Recognition**: Automatically identifies recurring workflow patterns
- **Optimization Suggestions**: Recommends command sequence improvements
- **Timing Optimization**: Suggests optimal timing for command execution
- **Context Correlation**: Links command success to environmental factors

### Collective Intelligence Integration
- **Benchmark Comparison**: Compares your patterns with collective best practices
- **Success Rate Analysis**: Shows how your success rates compare to other agents
- **Pattern Sharing**: Identifies successful patterns worth sharing with collective
- **Learning Opportunities**: Suggests areas for improvement based on collective data
- **Best Practice Adoption**: Recommends proven patterns from high-performing agents

## Performance Considerations
- **History Storage**: Command history stored locally and in collective memory
- **Analysis Speed**: Pattern analysis completed in <500ms for typical history sizes
- **Memory Usage**: Optimized storage of command metadata and outcomes
- **Privacy**: Personal command history separate from collective analytics
- **Retention**: Command history retained for 30 days, patterns preserved longer

## Related Commands
- **For workflow optimization**: Use `workflows` to see formalized patterns
- **For command help**: Use `help <command>` to understand failed commands
- **For examples**: Use `examples` to see successful usage patterns
- **For suggestions**: Use `suggest` to get recommendations based on recent patterns
- **For analytics**: Use `help analytics` to see broader usage statistics

## Troubleshooting Recent Command Analysis

### Missing Command History
```
ā“ No recent commands shown or incomplete history
šŸ’” Possible Causes:
   1. New installation - history builds over time
   2. Command tracking not enabled
   3. History storage issues
   4. Use commands to build history for analysis
```

### Inaccurate Success Rates
```
āš ļø Success rates don't match actual experience
šŸ’” Troubleshooting:
   1. Success tracking depends on command completion detection
   2. Network timeouts may be misclassified
   3. Manual verification of recent command outcomes
   4. Report discrepancies for system improvement
```

### Performance Analysis Issues
```
šŸŒ Pattern analysis taking too long or failing
šŸ’” Resolution Steps:
   1. Reduce history limit for faster analysis
   2. Check system resources during analysis
   3. Clear analysis cache if available
   4. Report performance issues for optimization
```

## Best Practices for Command History Analysis
- **Regular Review**: Check recent commands daily to identify patterns and issues
- **Learn from Failures**: Analyze failed commands to prevent future issues
- **Optimize Patterns**: Use successful patterns as templates for future work
- **Share Insights**: Document and share successful patterns with team
- **Track Improvements**: Monitor how changes affect success rates and efficiency
- **Use for Planning**: Let recent patterns inform future workflow planning

---

**Continuous Improvement**: The recent command analysis system learns from your usage patterns to provide increasingly accurate insights and optimization recommendations, helping you become more effective over time.

Quick Install

$npx ai-builder add command lancejames221b/recent

Details

Type
command
Author
lancejames221b
Slug
lancejames221b/recent
Created
4d ago

Used In