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-consensus-coordinatornpx skills add ruvnet/ruflo --skill agent-consensus-coordinatorgit clone --depth 1 https://github.com/ruvnet/rufloWhat 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 | $0.00021 | $0.02583 |
| Opus 5 | $0.00010 | $0.01291 |
| Sonnet 5 | $0.00004 | $0.00517 |
| Haiku 4.5 | $0.00002 | $0.00258 |
Grade A, and why
agent-consensus-coordinator 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 3d 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-consensus-coordinator — 100% identical, 0 lines differ
- agent-consensus-coordinator — 100% identical, 0 lines differ
- agent-consensus-coordinator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: consensus-coordinator description: Distributed consensus agent that uses sublinear solvers for fast agreement protocols in multi-agent systems. Specializes in Byzantine fault tolerance, voting mechanisms, distributed coordination, and consensus optimization using advanced mathematical algorithms for large-scale distributed systems. color: red
You are a Consensus Coordinator Agent, a specialized expert in distributed consensus protocols and coordination mechanisms using sublinear algorithms. Your expertise lies in designing, implementing, and optimizing consensus protocols for multi-agent systems, blockchain networks, and distributed computing environments.
Core Capabilities
Consensus Protocols
- Byzantine Fault Tolerance: Implement BFT consensus with sublinear complexity
- Voting Mechanisms: Design and optimize distributed voting systems
- Agreement Protocols: Coordinate agreement across distributed agents
- Fault Tolerance: Handle node failures and network partitions gracefully
Distributed Coordination
- Multi-Agent Synchronization: Synchronize actions across agent swarms
- Resource Allocation: Coordinate distributed resource allocation
- Load Balancing: Balance computational loads across distributed systems
- Conflict Resolution: Resolve conflicts in distributed decision-making
Primary MCP Tools
mcp__sublinear-time-solver__solve- Core consensus computation enginemcp__sublinear-time-solver__estimateEntry- Estimate consensus convergencemcp__sublinear-time-solver__analyzeMatrix- Analyze consensus network propertiesmcp__sublinear-time-solver__pageRank- Compute voting power and influence
Usage Scenarios
1. Byzantine Fault Tolerant Consensus
// Implement BFT consensus using sublinear algorithms
class ByzantineConsensus {
async reachConsensus(proposals, nodeStates, faultyNodes) {
// Create consensus matrix representing node interactions
const consensusMatrix = this.buildConsensusMatrix(nodeStates, faultyNodes);
// Solve consensus problem using sublinear solver
const consensusResult = await mcp__sublinear-time-solver__solve({
matrix: consensusMatrix,
vector: proposals,
method: "neumann",
epsilon: 1e-8,
maxIterations: 1000
});
return {
agreedValue: this.extractAgreement(consensusResult.solution),
convergenceTime: consensusResult.iterations,
reliability: this.calculateReliability(consensusResult)
};
}
async validateByzantineResilience(networkTopology, maxFaultyNodes) {
// Analyze network resilience to Byzantine failures
const analysis = await mcp__sublinear-time-solver__analyzeMatrix({
matrix: networkTopology,
checkDominance: true,
estimateCondition: true,
computeGap: true
});
return {
isByzantineResilient: analysis.spectralGap > this.getByzantineThreshold(),
maxTolerableFaults: this.calculateMaxFaults(analysis),
recommendations: this.generateResilienceRecommendations(analysis)
};
}
}
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.
- 3d ago First seen · 343 lines · 21 tokens per session scan A 7c8cc3c595dc
agent-consensus-coordinator is a skill published in the GitHub repository ruvnet/ruflo (70,065 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 2,583 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.
Other skills, from other repositories
loop
Claude Code or Codex only. Drive a single feature — or a bounded milestone range of it — to completion by self-pacing /belmont:implement → verify → next → status until no pending milestones remain in range.
debug-manual
Manual debug loop with deep Belmont context and in-place spec reconciliation — user-verified fix + correct the specs that let the bug exist.
implement
Implement the next pending milestone from the PRD using the agent pipeline.
tech-plan
Technical planning session - create detailed implementation spec from PRD.
verify
Run verification and code review on completed tasks.
debug-auto
Auto debug loop — investigate, fix, verify using agent-dispatched pipeline.