MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Agent skill for consensus-coordinator - invoke with $agent-consensus-coordinator
$ npx skills add ruvnet/ruflo --skill agent-consensus-coordinator -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ruvnet/ruflo agent-consensus-coordinator --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/agent-consensus-coordinator .claude/skills/agent-consensus-coordinator && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "agent-consensus-coordinator" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-consensus-coordinator into .claude/skills/agent-consensus-coordinator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-consensus-coordinator", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-consensus-coordinatorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ruvnet/ruflo --skill agent-consensus-coordinator -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ruvnet/ruflo agent-consensus-coordinator --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/agent-consensus-coordinator .agents/skills/agent-consensus-coordinator && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "agent-consensus-coordinator" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-consensus-coordinator into .agents/skills/agent-consensus-coordinator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-consensus-coordinator", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ruvnet/ruflo --skill agent-consensus-coordinator -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ruvnet/ruflo agent-consensus-coordinator --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/agent-consensus-coordinator .cursor/skills/agent-consensus-coordinator && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "agent-consensus-coordinator" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-consensus-coordinator into .cursor/skills/agent-consensus-coordinator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-consensus-coordinator", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ruvnet/ruflo.git --path .agents/skills/agent-consensus-coordinator--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ruvnet/ruflo --skill agent-consensus-coordinator -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ruvnet/ruflo agent-consensus-coordinator --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/agent-consensus-coordinator .gemini/skills/agent-consensus-coordinator && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "agent-consensus-coordinator" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-consensus-coordinator into .gemini/skills/agent-consensus-coordinator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-consensus-coordinator", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ruvnet/ruflo agent-consensus-coordinatorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ruvnet/ruflo --skill agent-consensus-coordinator -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/agent-consensus-coordinator .github/skills/agent-consensus-coordinator && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "agent-consensus-coordinator" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-consensus-coordinator into .github/skills/agent-consensus-coordinator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-consensus-coordinator", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ruvnet/ruflo --skill agent-consensus-coordinator -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ruvnet/ruflo agent-consensus-coordinator --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/agent-consensus-coordinator .opencode/skills/agent-consensus-coordinator && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "agent-consensus-coordinator" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-consensus-coordinator into .opencode/skills/agent-consensus-coordinator/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-consensus-coordinator", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
agent-consensus-coordinatorAgent skill for consensus-coordinator - invoke with $agent-consensus-coordinator
Agent Consensus Coordinator is an agent skill from ruvnet/ruflo. Agent skill for consensus-coordinator - invoke with $agent-consensus-coordinator
Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows. It works with Model Context Protocol. The repository describes itself as: 🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory…. The licence is MIT.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit de590e1. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Agent Consensus Coordinator loads about 3.2k tokens when it runs. Until then it costs about 27 tokens; SKILL.md has 692 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from ruvnet/ruflo at commit de590e1, republished under its MIT licence (© ruvnet). 692 words, ~3,155 tokens.
.claude/skills/agent-consensus-coordinator/SKILL.md (or your agent's skills folder).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.
© ruvnet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .agents/skills/agent-consensus-coordinator of ruvnet/ruflo.
Open the folder on GitHubat commit de590e1
We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in ruvnet/ruflo, which our catalogue first saw on October 7, 2026.
Agent Consensus Coordinator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Agent Consensus Coordinator this skillruvnet/ruflo | 74k | 3 repos | ~3.2k | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 62 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 5 repos | ~1.2k | Automated safety check: Pass | MIT | |
| MCP Integration for Pluginsanthropics/claude-plugins-official | 37k | 11 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Fastmcp Client CLIPrefectHQ/fastmcp | 28k | 1 repos | ~823 | Automated safety check: Pass | Apache-2.0 | |
| MemPalace Memory SearchMemPalace/mempalace | 59k | — | ~1.4k | Automated safety check: Pass | MIT |
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
shareAI-lab/learn-claude-code
Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.
anthropics/claude-plugins-official
Explains how to bundle Model Context Protocol servers in a Claude Code plugin, covering config files, stdio, SSE, HTTP and WebSocket server types, and authentication.
PrefectHQ/fastmcp
Query and invoke tools on MCP servers using fastmcp list and fastmcp call.
MemPalace/mempalace
Mines project files and conversation exports into a local, searchable memory palace and recalls past work by semantic search through the mempalace CLI.
charmbracelet/crush
Explains how to configure the Crush coding agent with crushrc or crush.json, covering providers, models, LSPs, MCP servers, hooks, permissions and config precedence.
ruvnet/ruflo
Stores, searches, and retrieves successful patterns with HNSW-indexed semantic search so agents can reuse past solutions instead of relearning them.
ruvnet/ruflo
Runs claude-flow CLI security scans for input validation, path traversal, SQL injection, XSS, hardcoded secrets and known CVEs, and writes an audit report.
ruvnet/ruflo
Applies the SPARC method (specification, pseudocode, architecture, refinement, completion) with 17 specialized modes and multi-agent orchestration, from research to deployment.
ruvnet/ruflo
Coordinates a hierarchical swarm of specialized agents through the claude-flow CLI for work that spans several files or modules at once.
ruvnet/ruflo
Sets up and drives Ruflo, an npm-installed orchestration layer for multi-agent swarms, persistent memory, routing, hooks and its MCP tool catalog.
ruvnet/ruflo
Reference for spawning, listing, monitoring and stopping agents with claude-flow commands, with agent type families, routing codes and coordination tips.
Works with
Categories
Agent skill for consensus-coordinator - invoke with $agent-consensus-coordinator. Agent Consensus Coordinator is an agent skill from ruvnet/ruflo.
Agent Consensus Coordinator fits situations like: agent Workflows work in your project.
Run `npx skills add ruvnet/ruflo --skill agent-consensus-coordinator -a claude-code`. Or copy the skill folder (.agents/skills/agent-consensus-coordinator in ruvnet/ruflo) into .claude/skills/agent-consensus-coordinator in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ruvnet/ruflo --skill agent-consensus-coordinator -a codex`. Or copy the skill folder (.agents/skills/agent-consensus-coordinator in ruvnet/ruflo) into .agents/skills/agent-consensus-coordinator in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ruvnet/ruflo --skill agent-consensus-coordinator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-consensus-coordinator, .gemini/skills/agent-consensus-coordinator, .github/skills/agent-consensus-coordinator and .opencode/skills/agent-consensus-coordinator in your project.
SKILL.md names no scripts, command-line tools or credentials: Agent Consensus Coordinator is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Agent Consensus Coordinator is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Agent Consensus Coordinator: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 37k stars) and Fastmcp Client CLI (PrefectHQ/fastmcp, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ruvnet (a GitHub user) maintains it in ruvnet/ruflo, which has 74,012 GitHub stars. The repository holds 264 skills in this directory. The repository was last updated on October 7, 2026.
Source: ruvnet/ruflo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.