Agente de consenso distribuido que usa solucionadores sublineales para protocolos de acuerdo rápido en sistemas multiagente. Se especializa en tolerancia a fallos bizantinos, votación, coordinación distribuida y optimización de consenso.
en todo el repo
0–100, la ruta de este skill
último commit aquí
últimos 90 días
20 tok en reposo
12 KB
Funciona con cualquier agente que lea SKILL.md
npx -y skills add ruvnet/ruflo --skill agent-consensus-coordinator --agent claude-codeSe instala solo en este repositorio.
Este skill reads environment config.
Di cualquiera de estas frases y el agente debería cargar este skill.
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.
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// 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)
};
}
}
// Implement weighted voting with PageRank-based influence
async function distributedVoting(votes, voterNetwork, votingPower) {
// Calculate voter influence using PageRank
const influence = await mcp__sublinear-time-solver__pageRank({
adjacency: voterNetwork,
damping: 0.85,
epsilon: 1e-6,
personalized: votingPower
});
// Weight votes by influence scores
const weightedVotes = votes.map((vote, i) => vote * influence.scores[i]);
// Compute consensus using weighted voting
const consensus = await mcp__sublinear-time-solver__solve({
matrix: {
rows: votes.length,
cols: votes.length,
format: "dense",
data: this.createVotingMatrix(influence.scores)
},
vector: weightedVotes,
method: "neumann",
epsilon: 1e-8
});
return {
decision: this.extractDecision(consensus.solution),
confidence: this.calculateConfidence(consensus),
participationRate: this.calculateParticipation(votes)
};
}
// Coordinate actions across agent swarm
class SwarmCoordinator {
async coordinateActions(agents, objectives, constraints) {
// Create coordination matrix
const coordinationMatrix = this.buildCoordinationMatrix(agents, constraints);
// Solve coordination problem
const coordination = await mcp__sublinear-time-solver__solve({
matrix: coordinationMatrix,
vector: objectives,
method: "random-walk",
epsilon: 1e-6,
maxIterations: 500
});
return {
assignments: this.extractAssignments(coordination.solution),
efficiency: this.calculateEfficiency(coordination),
conflicts: this.identifyConflicts(coordination)
};
}
async optimizeSwarmTopology(currentTopology, performanceMetrics) {
// Analyze current topology effectiveness
const analysis = await mcp__sublinear-time-solver__analyzeMatrix({
matrix: currentTopology,
checkDominance: true,
checkSymmetry: false,
estimateCondition: true
});
// Generate optimized topology
return this.generateOptimizedTopology(analysis, performanceMetrics);
}
}
// Deploy consensus cluster in Flow Nexus
const consensusCluster = await mcp__flow-nexus__sandbox_create({
template: "node",
name: "consensus-cluster",
env_vars: {
CLUSTER_SIZE: "10",
CONSENSUS_PROTOCOL: "byzantine",
FAULT_TOLERANCE: "33"
}
});
// Initialize consensus network
const networkSetup = await mcp__flow-nexus__sandbox_execute({
sandbox_id: consensusCluster.id,
code: `
const ConsensusNetwork = require('.$consensus-network');
class DistributedConsensus {
constructor(nodeCount, faultTolerance) {
this.nodes = Array.from({length: nodeCount}, (_, i) =>
new ConsensusNode(i, faultTolerance));
this.network = new ConsensusNetwork(this.nodes);
}
async startConsensus(proposal) {
console.log('Starting consensus for proposal:', proposal);
// Initialize consensus round
const round = this.network.initializeRound(proposal);
// Execute consensus protocol
while (!round.hasReachedConsensus()) {
await round.executePhase();
// Check for Byzantine behaviors
const suspiciousNodes = round.detectByzantineNodes();
if (suspiciousNodes.length > 0) {
console.log('Byzantine nodes detected:', suspiciousNodes);
}
}
return round.getConsensusResult();
}
}
// Start consensus cluster
const consensus = new DistributedConsensus(
parseInt(process.env.CLUSTER_SIZE),
parseInt(process.env.FAULT_TOLERANCE)
);
console.log('Consensus cluster initialized');
`,
language: "javascript"
});
// Implement blockchain consensus using sublinear algorithms
const blockchainConsensus = await mcp__flow-nexus__neural_train({
config: {
architecture: {
type: "transformer",
layers: [
{ type: "attention", heads: 8, units: 256 },
{ type: "feedforward", units: 512, activation: "relu" },
{ type: "attention", heads: 4, units: 128 },
{ type: "dense", units: 1, activation: "sigmoid" }
]
},
training: {
epochs: 100,
batch_size: 64,
learning_rate: 0.001,
optimizer: "adam"
}
},
tier: "large"
});
The Consensus Coordinator Agent serves as the backbone for all distributed coordination and agreement protocols, ensuring reliable and efficient consensus across various distributed computing environments and multi-agent systems.
Reproducido de ruvnet/ruflo bajo licencia MIT. Leer esta página en markdown.
1 archivo en el paquete. Solo se lee SKILL.md al activarse — las referencias se cargan si el skill decide que las necesita.
Requiere las herramientas MCP mcp__sublinear-time-solver__* (solve, pageRank, analyzeMatrix).
Variables de entorno:CLUSTER_SIZEFAULT_TOLERANCE
Este repo incluye 275 skills. Si instalas uno, normalmente ya tienes los demás.
Inspecciona y audita genomas GEPA: carga y valida un genoma, renderiza el system prompt que compila, o clasifica los modos de fallo de una transcripción de ejecución.
Completion de un solo turno contra el modelo deepseek-chat de DeepSeek vía /v1/chat/completions. Lee DEEPSEEK_API_KEY y degrada con status:degraded si falta o la API no responde. Para tareas sin razonamiento.
Completion en modo razonamiento contra deepseek-reasoner (R1) de DeepSeek. Devuelve el chain-of-thought por separado de la respuesta final. Lee DEEPSEEK_API_KEY y degrada si falta o la API no responde.
Construye o reconstruye el índice de ADRs y su grafo de dependencias ejecutando scripts/import.mjs, en vez de cientos de llamadas MCP.
Crea un nuevo Architecture Decision Record con numeración secuencial y registro en AgentDB.
Muestra el estado de la integración AGNTCY/SLIM/CASA: si los paquetes están instalados, qué transporte está activo y si el enforcement de CASA está habilitado.
Coordinador de swarm que cambia dinámicamente de topología (jerárquica, malla, anillo, híbrida) y se reoptimiza en tiempo real según patrones de rendimiento y carga de trabajo, con aprendizaje integrado.
Especialista en Goal-Oriented Action Planning (GOAP) que crea planes dinámicos e inteligentes para objetivos complejos, combinando técnicas de IA de videojuegos, replanificación adaptativa y optimización sublineal.
Especialista en coordinación inteligente y generación dinámica de agentes: analiza tareas y crea automáticamente los agentes con las capacidades óptimas para cada trabajo.
