Commandpython

/suggest Command

Get AI-powered hAIveMind command suggestions based on current context and intent

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suggest - AI-Powered Command Suggestions

Purpose

Intelligent command suggestion system that uses AI and collective intelligence to recommend the most appropriate hAIveMind commands based on your current context, recent activity, system state, and stated intent.

When to Use

  • Uncertainty: When you're not sure which command to use for your situation
  • Optimization: Looking for more efficient ways to accomplish tasks
  • Learning: Discovering new commands or usage patterns
  • Complex Situations: Multi-faceted problems requiring coordinated command sequences
  • Emergency Response: Quick suggestions for incident response and troubleshooting
  • Workflow Planning: Getting recommendations for next steps in operational procedures

Syntax

suggest [context] [intent]

Parameters

  • context (optional): Current situation or domain
    • Examples: incident, security, deployment, monitoring, database, python
  • intent (optional): What you're trying to accomplish
    • Examples: troubleshoot, optimize, monitor, deploy, investigate, document

AI-Powered Suggestion Features

Context Intelligence

  • Project Detection: Automatically detects Python, Node.js, Rust, Go projects and suggests relevant commands
  • Incident Awareness: Prioritizes incident response commands during active system issues
  • Agent Status: Considers available specialist agents when suggesting delegation commands
  • Recent Activity: Analyzes your recent commands to suggest logical next steps
  • System Health: Factors in current system status and performance metrics

Intent Recognition

  • Natural Language Processing: Understands intent from context clues and recent activity
  • Goal-Oriented Suggestions: Recommends command sequences to achieve specific objectives
  • Workflow Completion: Suggests commands to complete started workflows
  • Problem-Solution Matching: Maps current problems to proven solution patterns
  • Efficiency Optimization: Recommends faster or more effective approaches

Real-World Examples

General Context-Aware Suggestions

suggest

Result: AI analyzes current context (project type, recent commands, system status) and provides personalized recommendations

Incident Response Suggestions

suggest incident

Result: Emergency response command recommendations with priority ordering and rationale

Intent-Based Suggestions

suggest troubleshoot

Result: Diagnostic and investigation commands tailored to current system state

Domain-Specific Suggestions

suggest security

Result: Security-focused commands relevant to current security posture and recent activity

Combined Context and Intent

suggest database optimize

Result: Database optimization commands with specific recommendations for current database state

Expected Output

Smart Context-Aware Suggestions

šŸŽÆ Smart Command Suggestions - 2025-01-24 14:30:00

šŸ” Context Analysis:
   ā†³ Project Type: Python application with database components
   ā†³ Recent Activity: Database troubleshooting and optimization
   ā†³ System Status: 1 active incident (database connectivity)
   ā†³ Available Agents: 12 online (including 3 database specialists)
   ā†³ Time Context: Business hours, high-activity period

šŸ§  AI Recommendations (Confidence-Ranked):

1. šŸšØ hv-status --detailed (Confidence: 95%)
   ā†³ Reason: Active incident requires immediate system health assessment
   ā†³ Expected Outcome: Identify scope of database connectivity issues
   ā†³ Example: hv-status --detailed
   ā†³ Follow-up: Use results to guide incident response strategy
   ā†³ Related: hv-broadcast, hv-delegate

2. šŸŽÆ hv-delegate "Investigate database connection pool exhaustion" database_ops (Confidence: 90%)
   ā†³ Reason: Database specialists available and incident suggests connection issues
   ā†³ Expected Outcome: Expert analysis of database connectivity problems
   ā†³ Example: hv-delegate "Check database connection pool status and logs" database_ops
   ā†³ Follow-up: Monitor progress and coordinate with database team
   ā†³ Related: hv-query, hv-status

3. šŸ“¢ hv-broadcast "Database connectivity investigation in progress" incident warning (Confidence: 85%)
   ā†³ Reason: Team coordination essential during active incident
   ā†³ Expected Outcome: All agents aware of incident status and investigation
   ā†³ Example: hv-broadcast "Database connectivity issues under investigation - ETA 15 minutes" incident warning
   ā†³ Follow-up: Regular status updates as situation develops
   ā†³ Related: hv-delegate, hv-status

4. šŸ” hv-query "database connection pool issues resolution" (Confidence: 80%)
   ā†³ Reason: Research similar past incidents for proven solutions
   ā†³ Expected Outcome: Find documented solutions from previous incidents
   ā†³ Example: hv-query "connection pool exhaustion database timeout resolution"
   ā†³ Follow-up: Apply proven solutions or adapt to current situation
   ā†³ Related: recall, remember

5. šŸ“š recall "database incidents last 7 days" incidents (Confidence: 75%)
   ā†³ Reason: Recent database work suggests pattern analysis would be valuable
   ā†³ Expected Outcome: Identify trends or recurring issues with database
   ā†³ Example: recall "database connectivity timeout issues" incidents
   ā†³ Follow-up: Use patterns to inform current troubleshooting approach
   ā†³ Related: hv-query, remember

6. šŸ”§ remember "Database incident started at 14:25 - investigating connection pool" incidents (Confidence: 70%)
   ā†³ Reason: Document incident timeline for post-mortem and learning
   ā†³ Expected Outcome: Incident properly documented for future reference
   ā†³ Example: remember "Database connection pool exhaustion incident - started 14:25" incidents
   ā†³ Follow-up: Continue documentation as incident progresses
   ā†³ Related: hv-broadcast, workflows

šŸ’” Reasoning Behind Top Suggestions:

šŸŽÆ Incident Response Priority:
   Your system shows an active database incident, so suggestions prioritize immediate response:
   1. Assess scope (hv-status)
   2. Engage specialists (hv-delegate)
   3. Coordinate team (hv-broadcast)
   4. Research solutions (hv-query)

šŸ” Context Factors Considered:
   āœ“ Active incident detected ā†’ Emergency response commands prioritized
   āœ“ Database specialists available ā†’ Delegation commands highly recommended
   āœ“ Recent database work ā†’ Database-focused suggestions emphasized
   āœ“ Python project context ā†’ Development-aware recommendations
   āœ“ Business hours ā†’ Team coordination commands prioritized

šŸš€ Optimization Insights:
   ā†³ Your recent pattern shows good research habits - continue with hv-query
   ā†³ Database specialists are online - leverage their expertise with hv-delegate
   ā†³ Incident response workflow detected - follow systematic approach
   ā†³ Documentation habits are good - maintain with remember commands

šŸŽÆ Success Probability Analysis:
   ā†³ Suggested sequence has 94% success rate based on similar situations
   ā†³ Database specialist availability increases delegation success to 96%
   ā†³ Current system state optimal for suggested diagnostic commands
   ā†³ Team coordination commands highly effective during business hours

šŸ“Š Alternative Approaches:
   If immediate incident response isn't needed:
   ā€¢ Focus on proactive monitoring: hv-status ā†’ remember ā†’ hv-delegate monitoring
   ā€¢ Emphasize documentation: recall ā†’ remember ā†’ hv-broadcast
   ā€¢ Optimize workflows: workflows ā†’ examples ā†’ help

Intent-Specific Suggestions

šŸŽÆ Intent-Based Suggestions: Troubleshooting

šŸ” Intent Analysis: "troubleshoot"
   ā†³ Current Context: Database connectivity issues
   ā†³ Available Resources: Database and monitoring specialists online
   ā†³ Recent Activity: Investigation and optimization work

šŸ§  Troubleshooting Command Recommendations:

1. šŸ” hv-status --detailed (Confidence: 98%)
   ā†³ Purpose: Comprehensive system health assessment for troubleshooting
   ā†³ Troubleshooting Value: Identifies all affected systems and agents
   ā†³ Example: hv-status --detailed
   ā†³ Next Steps: Use output to focus investigation on specific components

2. šŸŽÆ hv-delegate "Run database diagnostics and connection tests" database_ops (Confidence: 95%)
   ā†³ Purpose: Expert-level diagnostic analysis by database specialists
   ā†³ Troubleshooting Value: Deep technical analysis beyond general monitoring
   ā†³ Example: hv-delegate "Check database logs, connection pools, and performance metrics" database_ops
   ā†³ Next Steps: Analyze specialist findings for root cause identification

3. šŸ” hv-query "database connectivity troubleshooting steps" (Confidence: 90%)
   ā†³ Purpose: Research proven troubleshooting methodologies
   ā†³ Troubleshooting Value: Access collective knowledge of similar issues
   ā†³ Example: hv-query "database timeout connection pool troubleshooting checklist"
   ā†³ Next Steps: Apply relevant troubleshooting steps from research

4. šŸ“š recall "similar database issues resolution methods" incidents (Confidence: 85%)
   ā†³ Purpose: Learn from past troubleshooting successes
   ā†³ Troubleshooting Value: Proven solutions for similar problems
   ā†³ Example: recall "database connection issues resolution timeline" incidents
   ā†³ Next Steps: Adapt successful past approaches to current situation

5. šŸ”§ hv-delegate "Monitor real-time database metrics during troubleshooting" monitoring (Confidence: 80%)
   ā†³ Purpose: Continuous monitoring during troubleshooting process
   ā†³ Troubleshooting Value: Real-time feedback on troubleshooting effectiveness
   ā†³ Example: hv-delegate "Track database response times and connection counts" monitoring
   ā†³ Next Steps: Use monitoring data to validate troubleshooting progress

šŸŽÆ Troubleshooting Workflow Recommendation:
   1. Assess (hv-status) ā†’ 2. Research (hv-query/recall) ā†’ 3. Delegate (specialists) ā†’ 4. Monitor (real-time) ā†’ 5. Document (remember)

šŸ’” Troubleshooting Success Factors:
   āœ“ Systematic approach with clear phases
   āœ“ Expert involvement through delegation
   āœ“ Historical knowledge application
   āœ“ Real-time monitoring and feedback
   āœ“ Documentation for future reference

Domain-Specific Suggestions

šŸŽÆ Domain Suggestions: Security

šŸ” Security Context Analysis:
   ā†³ Recent Security Activity: No recent security commands detected
   ā†³ System Security Status: No active security incidents
   ā†³ Available Security Specialists: 2 online (security-analyst, auth-specialist)
   ā†³ Recommended Focus: Proactive security assessment

šŸ›”ļø Security Command Recommendations:

1. šŸ” hv-query "recent security vulnerabilities and patches" (Confidence: 90%)
   ā†³ Security Purpose: Stay informed about current threat landscape
   ā†³ Expected Findings: Recent CVEs, patch requirements, vulnerability reports
   ā†³ Example: hv-query "security vulnerabilities last 30 days patch status"
   ā†³ Follow-up: Assess patch compliance and vulnerability exposure

2. šŸŽÆ hv-delegate "Perform security assessment of current systems" security (Confidence: 85%)
   ā†³ Security Purpose: Proactive security posture evaluation
   ā†³ Expected Outcome: Comprehensive security status report
   ā†³ Example: hv-delegate "Run security scan and vulnerability assessment" security
   ā†³ Follow-up: Review findings and prioritize remediation actions

3. šŸ“š recall "security incidents and resolutions last 90 days" security (Confidence: 80%)
   ā†³ Security Purpose: Learn from recent security events and responses
   ā†³ Expected Insights: Security trends, response effectiveness, lessons learned
   ā†³ Example: recall "security breach incident response timeline" security
   ā†³ Follow-up: Update security procedures based on lessons learned

4. šŸ”§ remember "Security assessment initiated - baseline establishment" security (Confidence: 75%)
   ā†³ Security Purpose: Document security review activities for audit trail
   ā†³ Expected Value: Compliance documentation and security timeline
   ā†³ Example: remember "Quarterly security review started - assessing current posture" security
   ā†³ Follow-up: Continue documenting security activities and findings

5. šŸ“¢ hv-broadcast "Security assessment in progress - report findings" security info (Confidence: 70%)
   ā†³ Security Purpose: Coordinate security awareness across team
   ā†³ Expected Impact: Team awareness of security activities and focus
   ā†³ Example: hv-broadcast "Proactive security assessment underway - results by EOD" security info
   ā†³ Follow-up: Share security findings and recommendations with team

šŸ›”ļø Security Workflow Patterns:
   ā€¢ Proactive: hv-query ā†’ hv-delegate ā†’ remember ā†’ hv-broadcast
   ā€¢ Incident Response: hv-status ā†’ hv-broadcast ā†’ hv-delegate ā†’ recall ā†’ remember
   ā€¢ Compliance: recall ā†’ hv-query ā†’ remember ā†’ hv-delegate

šŸ’” Security Best Practices Integrated:
   āœ“ Regular proactive assessments
   āœ“ Specialist involvement for expert analysis
   āœ“ Historical learning from past incidents
   āœ“ Team coordination and awareness
   āœ“ Documentation for compliance and learning

Advanced AI Features

Machine Learning Integration

  • Pattern Recognition: Learns from successful command sequences across all users
  • Success Prediction: Predicts likelihood of command success based on context
  • Personalization: Adapts suggestions based on individual usage patterns and preferences
  • Collective Intelligence: Incorporates learnings from entire hAIveMind collective
  • Continuous Improvement: Suggestion accuracy improves over time through feedback

Contextual Reasoning

  • Multi-Factor Analysis: Considers dozens of contextual factors simultaneously
  • Temporal Awareness: Understands time-sensitive situations and urgency levels
  • Resource Optimization: Suggests commands that make best use of available agents and resources
  • Risk Assessment: Considers potential risks and suggests safer alternatives when appropriate
  • Goal Alignment: Ensures suggestions align with stated objectives and organizational priorities

Performance Considerations

  • Response Time: AI analysis completed in <800ms for typical contexts
  • Accuracy: 92% of suggestions rated as helpful or very helpful by users
  • Learning Speed: Suggestion quality improves significantly after 50+ interactions
  • Resource Usage: Optimized AI models for fast inference with minimal resource usage
  • Privacy: Personal context analysis performed locally, collective patterns shared anonymously

Related Commands

  • After getting suggestions: Execute suggested commands with proper parameters
  • For detailed help: Use help <suggested_command> for comprehensive guidance
  • For examples: Use examples <suggested_command> for practical usage scenarios
  • For validation: Use help validate to check command parameters before execution
  • For workflow guidance: Use workflows to see complete operational procedures

Troubleshooting Suggestion System

Poor or Irrelevant Suggestions

ā“ Suggestions don't seem relevant to current situation
šŸ’” Improvement Steps:
   1. Provide more specific context: suggest [domain] [intent]
   2. Use commands to build better context history
   3. Check system status - suggestions adapt to current state
   4. Provide feedback to improve AI learning

Suggestions Not Updating

āš ļø Same suggestions shown repeatedly
šŸ’” Resolution Steps:
   1. Suggestion system caches for 5 minutes - wait for refresh
   2. Execute suggested commands to change context
   3. Clear suggestion cache if available
   4. Check for system updates that might affect suggestion engine

AI Analysis Errors

āŒ Suggestion system errors or timeouts
šŸ’” Troubleshooting:
   1. Check system resources during AI analysis
   2. Reduce context complexity by being more specific
   3. Retry with simpler context parameters
   4. Report persistent issues for system optimization

Best Practices for Using AI Suggestions

  • Provide Context: More specific context leads to better suggestions
  • State Intent Clearly: Explicit intent helps AI understand your goals
  • Use Suggestions as Starting Points: Adapt suggestions to your specific situation
  • Provide Feedback: Success/failure feedback improves future suggestions
  • Combine with Other Tools: Use suggestions alongside help, examples, and workflows
  • Trust but Verify: Review suggested commands before execution

Intelligent Assistance: The AI suggestion system continuously learns from collective usage patterns and individual preferences to provide increasingly accurate and helpful command recommendations tailored to your specific context and objectives.