
Agent Topology Optimizer
Reconfigure multi-agent swarm communication topologies—mesh, hierarchical, ring, star, hybrid, adaptive—to match workload and cut coordination overhead.
Overview
agent-topology-optimizer is an agent skill for the Build phase that optimizes multi-agent swarm topologies and communication patterns for workload-fit performance.
Install
npx skills add https://github.com/ruvnet/ruflo --skill agent-topology-optimizerWhat is this skill?
- Topology Optimizer agent profile focused on dynamic swarm reconfiguration
- Supports hierarchical, mesh, ring, star, hybrid, and adaptive topology implementations
- optimizeTopology analyzes current layout, generates candidates from workloadProfile, and respects constraints
- Bundles NetworkOptimizer, TopologyAnalyzer, and TopologyPredictor components
- Invoke via $agent-topology-optimizer in ruflo-style agent stacks
- Documents 6 topology types: hierarchical, mesh, ring, star, hybrid, adaptive
Adoption & trust: 647 installs on skills.sh; 58.5k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your agent swarm uses one static topology and coordination cost grows faster than useful work as agents scale.
Who is it for?
Indie builders orchestrating ruflo or similar multi-agent systems who need adaptive mesh, star, or hybrid layouts under real workload profiles.
Skip if: Solo developers shipping a single LLM chat with no swarm runtime or coordination graph to optimize.
When should I use this skill?
Invoke with $agent-topology-optimizer when profiling swarm workload and needing dynamic topology reconfiguration.
What do I get? / Deliverables
You get a workload-aligned topology recommendation and reconfiguration plan using analyzer-driven candidates and optimizer constraints before you deploy the swarm layout.
- Topology performance analysis of the current swarm graph
- Ranked topology candidates matched to workload constraints
- Reconfiguration plan via NetworkOptimizer workflow
Recommended Skills
Journey fit
Canonical shelf is build/agent-tooling because topology selection and network optimization happen while composing or hardening a multi-agent runtime, not during marketing or ops triage. Subphase agent-tooling is where swarm structures, optimizer classes, and communication patterns are designed and tuned before production ship gates.
How it compares
Skill package for swarm graph design, not a hosted MCP server that provisions infrastructure.
Common Questions / FAQ
Who is agent-topology-optimizer for?
It is for builders already running multi-agent swarms who understand orchestration code and want an optimizer-focused agent to reshape communication topology.
When should I use agent-topology-optimizer?
Use it in build/agent-tooling while designing swarm architectures, tuning parallel research workers, or refactoring a ring or star layout before load testing.
Is agent-topology-optimizer safe to install?
Use the Security Audits panel on this Prism page for verification; implementing its patterns may imply shell and network access in your own runtime, which you control separately.
SKILL.md
READMESKILL.md - Agent Topology Optimizer
--- name: Topology Optimizer type: agent category: optimization description: Dynamic swarm topology reconfiguration and communication pattern optimization --- # Topology Optimizer Agent ## Agent Profile - **Name**: Topology Optimizer - **Type**: Performance Optimization Agent - **Specialization**: Dynamic swarm topology reconfiguration and network optimization - **Performance Focus**: Communication pattern optimization and adaptive network structures ## Core Capabilities ### 1. Dynamic Topology Reconfiguration ```javascript // Advanced topology optimization system class TopologyOptimizer { constructor() { this.topologies = { hierarchical: new HierarchicalTopology(), mesh: new MeshTopology(), ring: new RingTopology(), star: new StarTopology(), hybrid: new HybridTopology(), adaptive: new AdaptiveTopology() }; this.optimizer = new NetworkOptimizer(); this.analyzer = new TopologyAnalyzer(); this.predictor = new TopologyPredictor(); } // Intelligent topology selection and optimization async optimizeTopology(swarm, workloadProfile, constraints = {}) { // Analyze current topology performance const currentAnalysis = await this.analyzer.analyze(swarm.topology); // Generate topology candidates based on workload const candidates = await this.generateCandidates(workloadProfile, constraints); // Evaluate each candidate topology const evaluations = await Promise.all( candidates.map(candidate => this.evaluateTopology(candidate, workloadProfile)) ); // Select optimal topology using multi-objective optimization const optimal = this.selectOptimalTopology(evaluations, constraints); // Plan migration strategy if topology change is beneficial if (optimal.improvement > constraints.minImprovement || 0.1) { const migrationPlan = await this.planMigration(swarm.topology, optimal.topology); return { recommended: optimal.topology, improvement: optimal.improvement, migrationPlan, estimatedDowntime: migrationPlan.estimatedDowntime, benefits: optimal.benefits }; } return { recommended: null, reason: 'No significant improvement found' }; } // Generate topology candidates async generateCandidates(workloadProfile, constraints) { const candidates = []; // Base topology variations for (const [type, topology] of Object.entries(this.topologies)) { if (this.isCompatible(type, workloadProfile, constraints)) { const variations = await topology.generateVariations(workloadProfile); candidates.push(...variations); } } // Hybrid topology generation const hybrids = await this.generateHybridTopologies(workloadProfile, constraints); candidates.push(...hybrids); // AI-generated novel topologies const aiGenerated = await this.generateAITopologies(workloadProfile); candidates.push(...aiGenerated); return candidates; } // Multi-objective topology evaluation async evaluateTopology(topology, workloadProfile) { const metrics = await this.calculateTopologyMetrics(topology, workloadProfile); return { topology, metrics, score: this.calculateOverallScore(metrics), strengths: this.identifyStrengths(metrics), weaknesses: this.identifyWeaknesses(metrics), suitability: this.calculateSuitability(metrics, workloadProfile) }; } } ``` ### 2. Network Latency Optimization ```javascript // Advanced network latency optimization class NetworkLatencyOptimizer { constructor() { this.latencyAnalyzer = new LatencyAnalyzer(); this.routingOptimizer = new RoutingOptimizer(); this.bandwidthManager = new BandwidthManager(); } // Comprehensive latency optimization async optimizeLaten