// Agent skill for consensus-coordinator - invoke with $agent-consensus-coordinator
[HINT] Laden Sie das komplette Skill-Verzeichnis einschließlich SKILL.md und aller zugehörigen Dateien herunter
| name | agent-consensus-coordinator |
| description | Agent skill for consensus-coordinator - invoke with $agent-consensus-coordinator |
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.