agent-topology-optimizer

agent-topology-optimizer is a skill for Claude Code from ruvnet/ruflo. It costs 21 tokens per session (4,999 once invoked), scanned A, original, MIT.

An agent that analyses and changes the communication structure of an AI-agent swarm to improve how its members work together.

In plain words
What is it for?
Use it to compare or reconfigure hierarchical, mesh, ring, star, hybrid, and adaptive swarm topologies.
Why use it?
It helps choose a suitable network arrangement for the workload and identify inefficient communication patterns.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: installed under .agents/ (shared by several agents); $skill-name invocation.

Part of the claude-flow plugin — 134 skills, 52 commands, 11 agents, 4 hooks shipped together

not rated 71krepo +669 2d ago A scan Socket: passSnyk: passSkillSpector: warn 21 tokens original MIT

Good fit Use it to compare or reconfigure hierarchical, mesh, ring, star, hybrid, and adaptive swarm topologies.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ruvnet/ruflo/agent-topology-optimizer
About the project

Ruflo is an execution and coordination layer for Claude Code and Codex that equips AI coding agents with tools, memory, control loops, sandboxes, and collaboration mechanisms. Developers use it to organize specialized agents into swarms, coordinate workflows, retain knowledge across sessions, and communicate across machines. The catalogue entries are Ruflo’s skills, commands, agents, hooks, and plugin components.

ruvnet/ruflo · 71,074 stars · on GitHub · cognitum.one

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add ruvnet/ruflo --skill agent-topology-optimizer
Clone the repo
git clone --depth 1 https://github.com/ruvnet/ruflo

Made for: Claude Code.

Or install claude-flow, the plugin that ships this one along with the rest of its 134 skills, 52 commands, 11 agents, 4 hooks.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for agent-topology-optimizer

README.md
[![agentmods](https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-topology-optimizer.svg)](https://agentmods.dev/skills/ruvnet/ruflo/agent-topology-optimizer)
Your own site
<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-topology-optimizer"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-topology-optimizer.svg" alt="Measured on agentmods" height="20"></a>
Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,999 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 18 Mar 2026
  • Snyk pass 1 Mar 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 8 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium MCP Rug Pull · line 734
    npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
    Fix: Pin the version: npx @scope/[email protected]
  • medium MCP Rug Pull · line 737
    npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
    Fix: Pin the version: npx @scope/[email protected]
  • medium MCP Rug Pull · line 740
    npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
    Fix: Pin the version: npx @scope/[email protected]
  • medium MCP Rug Pull · line 743
    npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
    Fix: Pin the version: npx @scope/[email protected]
  • medium MCP Rug Pull · line 746
    npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
    Fix: Pin the version: npx @scope/[email protected]
  • medium MCP Rug Pull · line 752
    npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
    Fix: Pin the version: npx @scope/[email protected]
  • medium MCP Rug Pull · line 755
    npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
    Fix: Pin the version: npx @scope/[email protected]
  • medium MCP Rug Pull · line 758
    npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.
    Fix: Pin the version: npx @scope/[email protected]
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00021 $0.04999
Opus 5 $0.00010 $0.02499
Sonnet 5 $0.00004 $0.01000
Haiku 4.5 $0.00002 $0.00500

Measured 4d ago against content hash e0606fe8a770, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

agent-topology-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

3 near-identical copies found in the catalogue:

.agents/skills/agent-topology-optimizer/SKILL.md · 813 lines

How it starts

The opening of the file, as written. The whole thing — 813 lines — stays where its author put it; the contents beside it link to each section on GitHub.


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

// 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)
    };
  }
}

Read the full file on GitHub · 813 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 813 lines · 21 tokens per session scan A e0606fe8a770

Subscribe to this mod's changes

agent-topology-optimizer is a skill published in the GitHub repository ruvnet/ruflo (71,074 stars, last pushed 2d ago), licensed MIT. It adds 21 tokens to every session and 4,999 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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