performance-scaling

Overview

This skill provides performance scaling and optimization strategies for autonomous agents across different LLM models, ensuring optimal execution characteristics while maintaining quality standards.

Model Performance Profiles

Claude Sonnet 4.5 Performance Profile

{
  "model": "claude-sonnet-4.5",
  "base_performance": {
    "execution_speed": "fast",
    "reasoning_depth": "high",
    "context_switching": "excellent",
    "adaptability": "very_high"
  },
  "scaling_factors": {
    "time_multiplier": 1.0,
    "quality_target": 90,
    "complexity_handling": 0.9,
    "parallel_processing": 1.2
  },
  "optimization_strategies": [
    "context_merging",
    "predictive_delegation",
    "pattern_weighting",
    "adaptive_quality_thresholds"
  ]
}

Claude Haiku 4.5 Performance Profile

{
  "model": "claude-haiku-4.5",
  "base_performance": {
    "execution_speed": "very_fast",
    "reasoning_depth": "medium",
    "context_switching": "good",
    "adaptability": "high"
  },
  "scaling_factors": {
    "time_multiplier": 0.8,
    "quality_target": 88,
    "complexity_handling": 1.1,
    "parallel_processing": 1.0
  },
  "optimization_strategies": [
    "fast_execution",
    "selective_processing",
    "efficient_delegation",
    "streamlined_quality_checks"
  ]
}

Claude Opus 4.1 Performance Profile

{
  "model": "claude-opus-4.1",
  "base_performance": {
    "execution_speed": "very_fast",
    "reasoning_depth": "very_high",
    "context_switching": "excellent",
    "adaptability": "maximum"
  },
  "scaling_factors": {
    "time_multiplier": 0.9,
    "quality_target": 95,
    "complexity_handling": 0.8,
    "parallel_processing": 1.4
  },
  "optimization_strategies": [
    "anticipatory_execution",
    "enhanced_parallelization",
    "predictive_caching",
    "advanced_pattern_recognition"
  ]
}

GLM-4.6 Performance Profile

{
  "model": "glm-4.6",
  "base_performance": {
    "execution_speed": "moderate",
    "reasoning_depth": "medium",
    "context_switching": "good",
    "adaptability": "medium"
  },
  "scaling_factors": {
    "time_multiplier": 1.25,
    "quality_target": 88,
    "complexity_handling": 1.2,
    "parallel_processing": 0.8
  },
  "optimization_strategies": [
    "structured_sequencing",
    "explicit_instruction_optimization",
    "step_by_step_validation",
    "clear_handoff_protocols"
  ]
}

Performance Scaling Strategies

Time-Based Scaling

Execution Time Allocation:

function scaleExecutionTime(baseTime, model, complexity) {
  const profiles = {
    'claude-sonnet': { multiplier: 1.0, complexity_factor: 0.9 },
    'claude-4.5': { multiplier: 0.9, complexity_factor: 0.8 },
    'glm-4.6': { multiplier: 1.25, complexity_factor: 1.2 },
    'fallback': { multiplier: 1.5, complexity_factor: 1.4 }
  };

  const profile = profiles[model] || profiles.fallback;
  return baseTime * profile.multiplier * (1 + complexity * profile.complexity_factor);
}

Timeout Adjustments:

• Claude Sonnet: Standard timeouts with 10% buffer
• Claude 4.5: Reduced timeouts with 5% buffer
• GLM-4.6: Extended timeouts with 25% buffer
• Fallback: Conservative timeouts with 50% buffer

Quality Target Scaling

Model-Specific Quality Targets:

function getQualityTarget(model, taskType) {
  const baseTargets = {
    'claude-sonnet': { simple: 85, complex: 90, critical: 95 },
    'claude-4.5': { simple: 88, complex: 92, critical: 96 },
    'glm-4.6': { simple: 82, complex: 88, critical: 92 },
    'fallback': { simple: 80, complex: 85, critical: 90 }
  };

  return baseTargets[model]?.[taskType] || baseTargets.fallback.complex;
}

Quality Assessment Adaptation:

• Claude Models: Emphasize contextual understanding and pattern recognition
• GLM Models: Emphasize structured accuracy and procedural correctness

Resource Scaling

Memory Management:

function scaleMemoryUsage(model, taskSize) {
  const profiles = {
    'claude-sonnet': { base_memory: 'medium', scaling_factor: 1.1 },
    'claude-4.5': { base_memory: 'medium', scaling_factor: 1.0 },
    'glm-4.6': { base_memory: 'high', scaling_factor: 1.3 },
    'fallback': { base_memory: 'high', scaling_factor: 1.5 }
  };

  const profile = profiles[model] || profiles.fallback;
  return allocateMemory(profile.base_memory, taskSize * profile.scaling_factor);
}

Concurrent Task Limits:

• Claude Sonnet: 3-4 concurrent tasks
• Claude 4.5: 4-5 concurrent tasks
• GLM-4.6: 2-3 concurrent tasks
• Fallback: 1-2 concurrent tasks

Adaptive Optimization Algorithms

Dynamic Performance Adjustment

Real-Time Performance Monitoring:

function monitorPerformance(model, currentMetrics) {
  const baseline = getPerformanceBaseline(model);
  const variance = calculateVariance(currentMetrics, baseline);

  if (variance > 0.2) {
    // Performance deviating significantly from baseline
    return adjustPerformanceParameters(model, currentMetrics);
  }

  return currentMetrics;
}

Automatic Parameter Tuning:

function tuneParameters(model, taskHistory) {
  const performance = analyzeTaskPerformance(taskHistory);
  const adjustments = calculateOptimalAdjustments(model, performance);

  return {
    timeout_adjustments: adjustments.timeouts,
    quality_thresholds: adjustments.quality,
    resource_allocation: adjustments.resources,
    delegation_strategy: adjustments.delegation
  };
}

Learning-Based Optimization

Pattern Recognition for Performance:

function learnPerformancePatterns(executionHistory) {
  const patterns = {
    successful_executions: extractSuccessPatterns(executionHistory),
    failed_executions: extractFailurePatterns(executionHistory),
    optimization_opportunities: identifyOptimizations(executionHistory)
  };

  return generatePerformanceRecommendations(patterns);
}

Model-Specific Learning:

• Claude Models: Learn from nuanced patterns and contextual factors
• GLM Models: Learn from structured procedures and clear success/failure patterns

Performance Metrics and KPIs

Core Performance Indicators

Execution Metrics:

• Task Completion Time: Time from task start to completion
• Quality Achievement: Final quality score vs target
• Resource Efficiency: Memory and CPU usage efficiency
• Error Rate: Frequency of errors requiring recovery

Model-Specific KPIs:

const modelKPIs = {
  'claude-sonnet': {
    'context_switching_efficiency': '>= 90%',
    'pattern_recognition_accuracy': '>= 85%',
    'adaptive_decision_quality': '>= 88%'
  },
  'claude-4.5': {
    'predictive_accuracy': '>= 80%',
    'anticipatory_optimization': '>= 75%',
    'enhanced_reasoning_utilization': '>= 90%'
  },
  'glm-4.6': {
    'procedural_accuracy': '>= 95%',
    'structured_execution_compliance': '>= 98%',
    'explicit_instruction_success': '>= 92%'
  }
};

Performance Benchmarking

Comparative Analysis:

function benchmarkPerformance(model, testSuite) {
  const results = runPerformanceTests(model, testSuite);
  const baseline = getIndustryBaseline(model);

  return {
    relative_performance: results.score / baseline.score,
    improvement_opportunities: identifyImprovements(results, baseline),
    model_strengths: analyzeModelStrengths(results),
    optimization_recommendations: generateRecommendations(results)
  };
}

Performance Optimization Techniques

Model-Specific Optimizations

Claude Sonnet Optimizations:

1. Context Merging: Combine related contexts to reduce switching overhead
2. Weight-Based Decision Making: Use historical success patterns for decisions
3. Progressive Loading: Load skills progressively based on immediate needs
4. Adaptive Quality Thresholds: Adjust quality targets based on task complexity

Claude 4.5 Optimizations:

1. Anticipatory Execution: Start likely tasks before explicit request
2. Enhanced Parallelization: Maximize concurrent task execution
3. Predictive Caching: Cache likely-needed resources proactively
4. Advanced Pattern Matching: Use complex pattern recognition for optimization

GLM-4.6 Optimizations:

1. Structured Sequencing: Optimize task order for efficiency
2. Explicit Instruction Optimization: Minimize ambiguity in instructions
3. Step-by-Step Validation: Validate each step before proceeding
4. Clear Handoff Protocols: Ensure clean transitions between tasks

Universal Optimizations

Cross-Model Techniques:

1. Resource Pooling: Share resources across compatible tasks
2. Intelligent Caching: Cache results based on usage patterns
3. Batch Processing: Group similar operations for efficiency
4. Lazy Loading: Load resources only when needed

Implementation Guidelines

Performance Configuration Loading

function loadPerformanceConfiguration(model) {
  const baseConfig = getBasePerformanceProfile(model);
  const historicalData = getHistoricalPerformanceData(model);
  const currentContext = assessCurrentContext();

  return mergeAndOptimizeConfiguration(baseConfig, historicalData, currentContext);
}

Runtime Performance Adjustment

function adjustRuntimePerformance(currentMetrics, targetProfile) {
  const adjustments = calculateNeededAdjustments(currentMetrics, targetProfile);

  return {
    timeout_adjustments: adjustments.timeouts,
    quality_modifications: adjustments.quality,
    resource_reallocation: adjustments.resources,
    strategy_changes: adjustments.strategy
  };
}

Performance Monitoring and Alerting

function monitorPerformanceHealth(model, metrics) {
  const healthScore = calculatePerformanceHealth(model, metrics);

  if (healthScore < 0.8) {
    return {
      status: 'degraded',
      recommendations: generateImprovementActions(model, metrics),
      automatic_adjustments: applyAutomaticOptimizations(model, metrics)
    };
  }

  return { status: 'healthy', score: healthScore };
}

Usage Guidelines

When to Apply Performance Scaling

1. Task Initialization: Set performance targets based on model and task type
2. Mid-Execution Adjustment: Adapt parameters based on current performance
3. Resource Optimization: Scale resource allocation based on availability
4. Quality-Performance Tradeoffs: Balance speed vs accuracy based on requirements

Integration Points

• Orchestrator Agent: Use for task planning and resource allocation
• All Specialized Agents: Use for model-specific execution optimization
• Quality Controller: Use for adaptive quality target setting
• Background Task Manager: Use for concurrent task optimization

This skill ensures optimal performance across all supported models while maintaining high quality standards and adapting to varying task requirements.