Design and implement ways for multiple agents or distributed computers to agree on decisions. Covers weighted voting, task allocation, coordination, and protocols that handle faulty or malicious participants, with tools for mathematical analysis.
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Agent skill for consensus-coordinator - invoke with $agent-consensus-coordinator
安装前可以查看交给 AI 代理的指令内容。
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.
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
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Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
中文简介正在准备中。当前显示英语简介。
Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
中文简介正在准备中。当前显示英语简介。
Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
中文简介正在准备中。当前显示英语简介。
Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
中文简介正在准备中。当前显示英语简介。
Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.
中文简介正在准备中。当前显示英语简介。