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.
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 ruvnet/ruflo --skill agent-topology-optimizergit clone --depth 1 https://github.com/ruvnet/rufloWrote 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/ruvnet/ruflo/agent-topology-optimizer)<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>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
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]
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 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- agent-topology-optimizer — 100% identical, 0 lines differ
- agent-topology-optimizer — 100% identical, 0 lines differ
- agent-topology-optimizer — 100% identical, 0 lines differ
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.
- 4d ago First seen · 813 lines · 21 tokens per session scan A e0606fe8a770
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.
Other skills, from other repositories
compare-harnesses
Diff two scaffolded harnesses (ADR-031). Reports manifest meta drift + host list + per-file fingerprint changes (added/removed/changed). Exits 0 IDENTICAL, 1 DRIFT, 2 missing manifest. Use --bundle for the ADR-031 schema-1 JSON envelope.
diag-harness
Kernel-version skew check (ADR-027). Reports manifest surface + manifest kernel + installed kernel + verdict (match/patch-diff/minor-diff/major-diff). Exits 1 on minor/major skew with a copy-pasteable npm install @metaharness/[email protected] next step. Exits 2 if no .harness/manifest.json at path.
repo-genome
7-section readiness scorecard for a LOCAL repo. Reports repo type + agent topology + MCP risk + test confidence + release readiness + recommended harness plan + scorecard. Exit 0 ready, 1 needs-work, 2 blocked. --json for the 6-field scorecard, --bundle for the ADR-031 schema-1 envelope.
score-harness
5-dimension scorecard (0-100, grade A/B/C/F) for a scaffolded harness. Dimensions: Repo understanding (25%), Agent usefulness (25%), MCP safety (20%), Test coverage (15%), Publish readiness (15%). Emits a 6-field badges block (score + mcpRisk + 4 booleans) ready for the harness README. Exit 0 A/B, 1 C, 2 F.
upgrade-harness
Drift detection + apply for a scaffolded harness. Re-renders the template with the same vars, computes added/removed/changed file plan, and applies with Git-style conflict markers or .rej files. Default is dry-run.
verify-witness
Verify the Ed25519 witness manifest of a scaffolded harness. Fast yes/no signature check that proves the publisher signed this exact file set — separate from the full release-readiness umbrella in validate-harness.