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 agentmods add skills/ruvnet/ruflo/agent-load-balancernpx skills add ruvnet/ruflo --skill agent-load-balancergit 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-load-balancer)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-load-balancer"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-load-balancer.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.02810 |
| Opus 5 | $0.00010 | $0.01405 |
| Sonnet 5 | $0.00004 | $0.00562 |
| Haiku 4.5 | $0.00002 | $0.00281 |
Grade A, and why
agent-load-balancer 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- agent-load-balancer — 100% identical, 0 lines differ
- agent-load-balancer — 100% identical, 0 lines differ
- agent-load-balancer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 436 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: Load Balancing Coordinator type: agent category: optimization description: Dynamic task distribution, work-stealing algorithms and adaptive load balancing
Load Balancing Coordinator Agent
Agent Profile
- Name: Load Balancing Coordinator
- Type: Performance Optimization Agent
- Specialization: Dynamic task distribution and resource allocation
- Performance Focus: Work-stealing algorithms and adaptive load balancing
Core Capabilities
1. Work-Stealing Algorithms
// Advanced work-stealing implementation
const workStealingScheduler = {
// Distributed queue system
globalQueue: new PriorityQueue(),
localQueues: new Map(), // agent-id -> local queue
// Work-stealing algorithm
async stealWork(requestingAgentId) {
const victims = this.getVictimCandidates(requestingAgentId);
for (const victim of victims) {
const stolenTasks = await this.attemptSteal(victim, requestingAgentId);
if (stolenTasks.length > 0) {
return stolenTasks;
}
}
// Fallback to global queue
return await this.getFromGlobalQueue(requestingAgentId);
},
// Victim selection strategy
getVictimCandidates(requestingAgent) {
return Array.from(this.localQueues.entries())
.filter(([agentId, queue]) =>
agentId !== requestingAgent &&
queue.size() > this.stealThreshold
)
.sort((a, b) => b[1].size() - a[1].size()) // Heaviest first
.map(([agentId]) => agentId);
}
};
2. Dynamic Load Balancing
// Real-time load balancing system
const loadBalancer = {
// Agent capacity tracking
agentCapacities: new Map(),
currentLoads: new Map(),
performanceMetrics: new Map(),
// Dynamic load balancing
async balanceLoad() {
const agents = await this.getActiveAgents();
const loadDistribution = this.calculateLoadDistribution(agents);
// Identify overloaded and underloaded agents
const { overloaded, underloaded } = this.categorizeAgents(loadDistribution);
// Migrate tasks from overloaded to underloaded agents
for (const overloadedAgent of overloaded) {
const candidateTasks = await this.getMovableTasks(overloadedAgent.id);
const targetAgent = this.selectTargetAgent(underloaded, candidateTasks);
if (targetAgent) {
await this.migrateTasks(candidateTasks, overloadedAgent.id, targetAgent.id);
}
}
},
// Weighted Fair Queuing implementation
async scheduleWithWFQ(tasks) {
const weights = await this.calculateAgentWeights();
const virtualTimes = new Map();
return tasks.sort((a, b) => {
const aFinishTime = this.calculateFinishTime(a, weights, virtualTimes);
const bFinishTime = this.calculateFinishTime(b, weights, virtualTimes);
return aFinishTime - bFinishTime;
});
}
};
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 · 436 lines · 19 tokens per session scan A 23b2aa668ddf
agent-load-balancer is a skill published in the GitHub repository ruvnet/ruflo (70,498 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 2,810 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 2026-08-30.
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compare-harnesses
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diag-harness
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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.
nw-jtbd-bdd-integration
Translating JTBD analysis to BDD scenarios - job story to Given-When-Then patterns, forces-based test discovery, job-map-based test discovery, and property-shaped criteria.
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.