
Agent Resource Allocator
Plan adaptive CPU, memory, storage, network, and agent capacity for swarms with predictive scaling patterns.
Overview
Agent Resource Allocator is an agent skill most often used in Operate (also Build) that guides adaptive resource allocation and predictive scaling for agent swarms.
Install
npx skills add https://github.com/ruvnet/ruflo --skill agent-resource-allocatorWhat is this skill?
- Adaptive resource allocation across CPU, memory, storage, network, and agents
- Workload-profile-driven prediction before allocation decisions
- AllocationOptimizer and ResourceMonitor integration patterns in JavaScript
- Dynamic allocateResources flow with constraints and current-usage analysis
- Performance-focused capacity planning for multi-agent swarms
- Five resource allocator domains: CPU, memory, storage, network, and agents
Adoption & trust: 645 installs on skills.sh; 58.5k GitHub stars; 2/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your agent swarm or service burns budget on idle capacity or stalls when workloads surge because allocation is static.
Who is it for?
Indie builders operating multi-agent systems who need explicit allocation patterns and prediction steps in the agent loop.
Skip if: Simple single-process apps with flat hosting where manual instance sizing is enough.
When should I use this skill?
Invoke with $agent-resource-allocator when adaptive resource allocation, predictive scaling, or intelligent capacity planning is required for a swarm or workload profile.
What do I get? / Deliverables
You get a constraint-aware allocation plan grounded in current usage and predicted workload so you can right-size infra before the next deploy or scale event.
- Optimal allocation recommendation per resource type
- Prediction summary tied to workload profile
- Constraint-aware allocation plan ready to implement in infra
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Operate is the canonical shelf because allocation and capacity planning are production concerns after something is running. Infra subphase covers predictive scaling, constraints, and intelligent capacity planning for agent swarms.
Where it fits
Right-size agent worker pools after analyzing peak concurrent tasks.
Design allocateResources APIs before shipping a swarm orchestrator.
Pre-launch capacity review using predicted traffic profiles.
Feed live usage metrics back into the predictor for the next allocation cycle.
How it compares
Agent optimization skill with code patterns—not a managed autoscaling product or one-click cloud dashboard.
Common Questions / FAQ
Who is agent-resource-allocator for?
Developers shipping agent swarms or performance-sensitive APIs who want the coding agent to follow structured allocate-and-predict workflows.
When should I use agent-resource-allocator?
In Operate when tuning infra and capacity after launch, and in Build when designing agent-tooling backends—whenever workload profiles and constraints must drive allocation.
Is agent-resource-allocator safe to install?
It describes allocation logic only; review the Security Audits panel on this page before granting shell or cloud API permissions in your environment.
SKILL.md
READMESKILL.md - 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);