Agent Resource AllocatorCAUTION

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: agent-resource-allocator
description: Agent skill for resource-allocator - invoke with $agent-resource-allocator
---

---
name: Resource Allocator
type: agent
category: optimization
description: Adaptive resource allocation, predictive scaling and intelligent capacity planning
---

# Resource Allocator Agent

## Agent Profile
- **Name**: Resource Allocator
- **Type**: Performance Optimization Agent
- **Specialization**: Adaptive resource allocation and predictive scaling
- **Performance Focus**: Intelligent resource management and capacity planning

## Core Capabilities

### 1. Adaptive Resource Allocation
```javascript
// Advanced adaptive resource allocation system
class AdaptiveResourceAllocator {
  constructor() {
    this.allocators = {
      cpu: new CPUAllocator(),
      memory: new MemoryAllocator(),
      storage: new StorageAllocator(),
      network: new NetworkAllocator(),
      agents: new AgentAllocator()
    };
    
    this.predictor = new ResourcePredictor();
    this.optimizer = new AllocationOptimizer();
    this.monitor = new ResourceMonitor();
  }
  
  // Dynamic resource allocation based on workload patterns
  async allocateResources(swarmId, workloadProfile, constraints = {}) {
    // Analyze current resource usage
    const currentUsage = await this.analyzeCurrentUsage(swarmId);
    
    // Predict future resource needs
    const predictions = await this.predictor.predict(workloadProfile, currentUsage);
    
    // Calculate optimal allocation
    const allocation = await this.optimizer.optimize(predictions, constraints);
    
    // Apply allocation with gradual rollout
    const rolloutPlan = await this.planGradualRollout(allocation, currentUsage);
    
    // Execute allocation
    const result = await this.executeAllocation(rolloutPlan);
    
    return {
      allocation,
      rolloutPlan,
      result,
      monitoring: await this.setupMonitoring(allocation)
    };
  }
  
  // Workload pattern analysis
  async analyzeWorkloadPatterns(historicalData, timeWindow = '7d') {
    const patterns = {
      // Temporal patterns
      temporal: {
        hourly: this.analyzeHourlyPatterns(historicalData),
        daily: this.analyzeDailyPatterns(historicalData),
        weekly: this.analyzeWeeklyPatterns(historicalData),
        seasonal: this.analyzeSeasonalPatterns(historicalData)
      },
      
      // Load patterns
      load: {
        baseline: this.calculateBaselineLoad(historicalData),
        peaks: this.identifyPeakPatterns(historicalData),
        valleys: this.identifyValleyPatterns(historicalData),
        spikes: this.detectAnomalousSpikes(historicalData)
      },
      
      // Resource correlation patterns
      correlations: {
        cpu_memory: this.analyzeCPUMemoryCorrelation(historicalData),
        network_load: this.analyzeNetworkLoadCorrelation(historicalData),
        agent_resource: this.analyzeAgentResourceCorrelation(historicalData)
      },
      
      // Predictive indicators
      indicators: {
        growth_rate: this.calculateGrowthRate(historicalData),
        volatility: this.calculateVolatility(historicalData),
        predictability: this.calculatePredictability(historicalData)
      }
    };
    
    return patterns;
  }
  
  // Multi-objective resource optimization
  async optimizeResourceAllocation(resources, demands, objectives) {
    const optimizationProblem = {
      variables: this.defineOptimizationVariables(resources),
      constraints: this.defineConstraints(resources, demands),
      objectives: this.defineObjectives(objectives)
    };
    
    // Use multi-objective genetic algorithm
    const solver = new MultiObjectiveGeneticSolver({
      populationSize: 100,
      generations: 200,
      mutationRate: 0.1,
      crossoverRate: 0.8
    });
    
    const solutions = await solver.solve(optimizationProblem);
    
    // Select solution from Pareto front
    const selectedSolution = this.selectFromParetoFront(solutions, objectives);
    
    return {
      optimalAllocation: selectedSolution.allocation,
      paretoFront: solutions.paretoFront,
      tradeoffs: solutions.tradeoffs,
      confidence: selectedSolution.confidence
    };
  }
}
```

### 2. Predictive Scaling with Machine Learning
```javascript
// ML-powered predictive scaling system
class PredictiveScaler {
  constructor() {
    this.models = {
      time_series: new LSTMTimeSeriesModel(),
      regression: new RandomForestRegressor(),
      anomaly: new IsolationForestModel(),
      ensemble: new EnsemblePredictor()
    };
    
    this.featureEngineering = new FeatureEngineer();
    this.dataPreprocessor = new DataPreprocessor();
  }
  
  // Predict scaling requirements
  async predictScaling(swarmId, timeHorizon = 3600, confidence = 0.95) {
    // Collect training data
    const trainingData = await this.collectTrainingData(swarmId);
    
    // Engineer features
    const features = await this.featureEngineering.engineer(trainingData);
    
    // Train$update models
    await this.updateModels(features);
    
    // Generate predictions
    const predictions = await this.generatePredictions(timeHorizon, confidence);
    
    // Calculate scaling recommendations
    const scalingPlan = await this.calculateScalingPlan(predictions);
    
    return {
      predictions,
      scalingPlan,
      confidence: predictions.confidence,
      timeHorizon,
      features: features.summary
    };
  }
  
  // LSTM-based time series prediction
  async trainTimeSeriesModel(data, config = {}) {
    const model = await mcp.neural_train({
      pattern_type: 'prediction',
      training_data: JSON.stringify({
        sequences: data.sequences,
        targets: data.targets,
        features: data.features
      }),
      epochs: config.epochs || 100
    });
    
    // Validate model performance
    const validation = await this.validateModel(model, data.validation);
    
    if (validation.accuracy > 0.85) {
      await mcp.model_save({
        modelId:

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (4)

crates

plugin/agents

plugin/commands

plugin/skills

Gates applied: no_behavioural_pass.