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.
Borrowing it
Nothing to install: this file belongs to ruvnet/ruflo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ruvnet/ruflo/main/.agents/skills/agent-consensus-coordinator/SKILL.mdgit 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-consensus-coordinator)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-consensus-coordinator"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-consensus-coordinator/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/ruvnet/ruflo/agent-consensus-coordinator"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-consensus-coordinator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.02583 |
| Opus 5.5 | $0.00008 | $0.01033 |
| Sonnet 5.5 | $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 1mo 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
4 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
- 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.
- 1mo 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 (77,172 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 2,583 once invoked, about $0.0001 per session on Opus 5.5. 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
nexent-daily-test
A procedure for running Nexent daily, full-layer tests on a self-hosted Ubuntu machine. Nexent is tested across separate layers such as unit, service, AI runtime, and browser journeys, with results and evidence kept locally.
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.