agent-resource-allocator
Agent skill for resource-allocator - invoke with $agent-resource-allocator
Profile →npx skills add ruvnet/ruflo --skill agent-resource-allocator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
The Resource Allocator agent performs adaptive resource allocation across CPU, memory, storage, network, and agents, guided by a workload profile and constraints. It analyzes current usage, predicts future needs, optimizes allocation, and executes a gradual rollout with monitoring.
How it works
- It initializes components: CPUAllocator, MemoryAllocator, StorageAllocator, NetworkAllocator, AgentAllocator, plus a ResourcePredictor and an AllocationOptimizer, then a ResourceMonitor.
- allocateResources(swarmId, workloadProfile, constraints) flow:
- Analyze current usage via analyzeCurrentUsage(swarmId).
- Predict future needs with predictor.predict(workloadProfile, currentUsage).
- Compute an optimal allocation using optimizer.optimize(predictions, constraints).
- Plan a gradual rollout with planGradualRollout(allocation, currentUsage).
- Execute the rollout, then set up monitoring for the allocation.
- Return allocation, rolloutPlan, result, and monitoring setup.
- analyzeWorkloadPatterns(historicalData, timeWindow) derives patterns (temporal, load, correlations, indicators).
- optimizeResourceAllocation(resources, demands, objectives) uses a multi-objective genetic solver to propose a Pareto-front solution and selects a solution from it.
- Predictive Scaling with Machine Learning includes PredictiveScaler with time-series, regression, anomaly models and an ensemble predictor; it can train time-series models, implement reinforcement learning for scaling decisions, and produce a scalingPlan with predictions and confidence.
- Predictive scaling model training (trainTimeSeriesModel) saves the model at $models$scaling_predictor.model if validation accuracy > 0.85; otherwise returns a not-ready state.
- trainScalingAgent develops a DeepQNetworkAgent, runs episodes, logs progress, and returns agent performance.
- Circuit Breaker and Fault Tolerance include AdaptiveCircuitBreaker with adjustable thresholds and metrics; supports execute(operation, fallback) and can return fallback results.
- Performance Profiling and Optimization includes a PerformanceProfiler with CPU, memory, IO, network, and application profilers; it profiles, analyzes, and generates optimization recommendations and a summary.
- MCP Integration Hooks provide resource management integration and predictive scaling hooks for resource allocation and swarm scaling.
When to use it
Use when you need adaptive, predictive scaling and capacity planning for a swarm or resource-intensive workload, with a need for multi-objective optimization and fault-tolerant operation.
What it can touch
- Tools indicated: claude-code, codex.
- Methods and classes referenced include AdaptiveResourceAllocator, PredictiveScaler, AdaptiveCircuitBreaker, PerformanceProfiler, and MCP integration hooks; specific method calls include allocateResources, predictScaling, trainTimeSeriesModel, trainScalingAgent, and execute.
Caveats
- The description shows implementations in JavaScript-like pseudocode and references to specific models and configuration (e.g., mcp integration, $models$scaling_predictor.model) that imply dependencies and runtime IAM considerations not detailed here.
- No explicit licensing text beyond overall MIT license for the skill package; ensure compatibility with MIT terms in your environment.
--- 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.predicto
- Agent Profile
- Core Capabilities
- 1. Adaptive Resource Allocation
- 2. Predictive Scaling with Machine Learning
- 3. Circuit Breaker and Fault Tolerance
- 4. Performance Profiling and Optimization
- MCP Integration Hooks
- Resource Management Integration
- Operational Commands
- Resource Management Commands
- Optimization Commands
- Integration Points
- With Other Optimization Agents
- With Swarm Infrastructure
Analyze resource usage npx claude-flow metrics-collect --components ["cpu", "memory", "network"] Optimize resource allocation npx claude-flow daa-resource-alloc --resources <resource-config> Predictive scaling npx claude-flow swarm-scale --swarm-id <id> --target-size <size> Performance profiling npx claude-flow performance-report --format detailed --timeframe 24h Circuit breaker configuration npx claude-flow fault-tolerance --strategy circuit-breaker --config <config>
What does the agent-resource-allocator skill do?
Agent skill for resource-allocator - invoke with $agent-resource-allocator
How do I install it?
Run `npx skills add ruvnet/ruflo --skill agent-resource-allocator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ruvnet/ruflo, a repository with 67,015 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.