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.
npx skills add henryalouf/ruflow --skill agent-topology-optimizergit clone --depth 1 https://github.com/henryalouf/ruflowWrote 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.
[](https://agentmods.dev/skills/henryalouf/ruflow/agent-topology-optimizer)<a href="https://agentmods.dev/skills/henryalouf/ruflow/agent-topology-optimizer"><img src="https://agentmods.dev/badge/skills/henryalouf/ruflow/agent-topology-optimizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/henryalouf/ruflow/agent-topology-optimizer"><img src="https://agentmods.dev/badge/skills/henryalouf/ruflow/agent-topology-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once 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 |
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 5d 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.
This is a copy
100% identical to agent-topology-optimizer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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)
};
}
}
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.
- 5d ago First seen · 813 lines · 21 tokens per session scan A e0606fe8a770
agent-topology-optimizer is a skill published in the GitHub repository henryalouf/ruflow (126 stars, last pushed 3mo 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. It is 100% identical to agent-topology-optimizer, differing in 0 lines, and is treated as a copy.
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