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Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/ruvnet/RuVector --skill flow-nexus-swarm명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Evolve this harness with Darwin Mode — frozen model, evolving harness (real, sandboxed, safety-gated).
Create and run custom background analysis workers with composable phases. Use when you need automated code analysis, security scanning, pattern learning, or API documentation generation.
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
SOC 직업 분류 기준
SKILL.md 표시 중
| name | flow-nexus-swarm |
| description | Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform |
| category | orchestration |
| tags | ["swarm","workflow","cloud","agents","automation","message-queue"] |
| version | 1.0.0 |
| requires | ["flow-nexus MCP server","Active Flow Nexus account (register at flow-nexus.ruv.io)"] |
| hooks | {"pre":"echo \"🧠 Flow Nexus Swarm activated\"\nif [ -d \"/workspaces/ruvector/.claude/intelligence\" ]; then\n cd /workspaces/ruvector/.claude/intelligence\n INTELLIGENCE_MODE=treatment node cli.js pre-edit \"$FILE\" 2>/dev/null || true\nfi\n","post":"echo \"✅ Flow Nexus Swarm complete\"\nif [ -d \"/workspaces/ruvector/.claude/intelligence\" ]; then\n cd /workspaces/ruvector/.claude/intelligence\n INTELLIGENCE_MODE=treatment node cli.js post-edit \"$FILE\" \"true\" 2>/dev/null || true\nfi\n"} |
Deploy and manage cloud-based AI agent swarms with event-driven workflow automation, message queue processing, and intelligent agent coordination.
Integrates with RuVector's Q-learning and vector memory for improved performance.
CLI: node .claude/intelligence/cli.js stats
Flow Nexus provides cloud-based orchestration for AI agent swarms with:
Create a new swarm with specified topology and configuration:
mcp__flow-nexus__swarm_init({
topology: "hierarchical", // Options: mesh, ring, star, hierarchical
maxAgents: 8,
strategy: "balanced" // Options: balanced, specialized, adaptive
})
Topology Guide:
Strategy Guide:
Add specialized agents to the swarm:
mcp__flow-nexus__agent_spawn({
type: "researcher", // Options: researcher, coder, analyst, optimizer, coordinator
name: "Lead Researcher",
capabilities: ["web_search", "analysis", "summarization"]
})
Agent Types:
Distribute tasks across the swarm:
mcp__flow-nexus__task_orchestrate({
task: "Build a REST API with authentication and database integration",
strategy: "parallel", // Options: parallel, sequential, adaptive
maxAgents: 5,
priority: "high" // Options: low, medium, high, critical
})
Execution Strategies:
// Get detailed swarm status
mcp__flow-nexus__swarm_status({
swarm_id: "optional-id" // Uses active swarm if not provided
})
// List all active swarms
mcp__flow-nexus__swarm_list({
status: "active" // Options: active, destroyed, all
})
// Scale swarm up or down
mcp__flow-nexus__swarm_scale({
target_agents: 10,
swarm_id: "optional-id"
})
// Gracefully destroy swarm
mcp__flow-nexus__swarm_destroy({
swarm_id: "optional-id"
})
Define event-driven workflows with message queue processing:
mcp__flow-nexus__workflow_create({
name: "CI/CD Pipeline",
description: "Automated testing, building, and deployment",
steps: [
{
id: "test",
action: "run_tests",
agent: "tester",
parallel: true
},
{
id: "build",
action: "build_app",
agent: "builder",
depends_on: ["test"]
},
{
id: "deploy",
action: "deploy_prod",
agent: "deployer",
depends_on: ["build"]
}
],
triggers: ["push_to_main", "manual_trigger"],
metadata: {
priority: 10,
retry_policy: "exponential_backoff"
}
})
Workflow Features:
depends_onparallel: true for concurrent stepsRun workflows synchronously or asynchronously:
mcp__flow-nexus__workflow_execute({
workflow_id: "workflow_id",
input_data: {
branch: "main",
commit: "abc123",
environment: "production"
},
async: true // Queue-based execution for long-running workflows
})
Execution Modes:
// Get workflow status and metrics
mcp__flow-nexus__workflow_status({
workflow_id: "id",
execution_id: "specific-run-id", // Optional
include_metrics: true
})
// List workflows with filters
mcp__flow-nexus__workflow_list({
status: "running", // Options: running, completed, failed, pending
limit: 10,
offset: 0
})
// Get complete audit trail
mcp__flow-nexus__workflow_audit_trail({
workflow_id: "id",
limit: 50,
start_time: "2025-01-01T00:00:00Z"
})
Intelligently assign agents to workflow tasks:
mcp__flow-nexus__workflow_agent_assign({
task_id: "task_id",
agent_type: "coder", // Preferred agent type
use_vector_similarity: true // AI-powered capability matching
})
Vector Similarity Matching:
Monitor and manage message queues:
mcp__flow-nexus__workflow_queue_status({
queue_name: "optional-specific-queue",
include_messages: true // Show pending messages
})
// 1. Initialize swarm with hierarchical topology
mcp__flow-nexus__swarm_init({
topology: "hierarchical",
maxAgents: 8,
strategy: "specialized"
})
// 2. Spawn specialized agents
mcp__flow-nexus__agent_spawn({ type: "coordinator", name: "Project Manager" })
mcp__flow-nexus__agent_spawn({ type: "coder", name: "Backend Developer" })
mcp__flow-nexus__agent_spawn({ type: "coder", name: "Frontend Developer" })
mcp__flow-nexus__agent_spawn({ type: "coder", name: "Database Architect" })
mcp__flow-nexus__agent_spawn({ type: "analyst", name: "QA Engineer" })
// 3. Create development workflow
mcp__flow-nexus__workflow_create({
name: "Full-Stack Development",
steps: [
{ id: "requirements", action: "analyze_requirements", agent: "coordinator" },
{ id: "db_design", action: "design_schema", agent: "Database Architect" },
{ id: "backend", action: "build_api", agent: "Backend Developer", depends_on: ["db_design"] },
{ id: "frontend", action: "build_ui", agent: "Frontend Developer", depends_on: ["requirements"] },
{ id: "integration", action: "integrate", agent: "Backend Developer", depends_on: ["backend", "frontend"] },
{ id: "testing", action: "qa_testing", agent: "QA Engineer", depends_on: ["integration"] }
]
})
// 4. Execute workflow
mcp__flow-nexus__workflow_execute({
workflow_id: "workflow_id",
input_data: {
project: "E-commerce Platform",
tech_stack: ["Node.js", "React", "PostgreSQL"]
}
})
// 1. Initialize mesh topology for collaborative research
mcp__flow-nexus__swarm_init({
topology: "mesh",
maxAgents: 5,
strategy: "balanced"
})
// 2. Spawn research agents
mcp__flow-nexus__agent_spawn({ type: "researcher", name: "Primary Researcher" })
mcp__flow-nexus__agent_spawn({ type: "researcher", name: "Secondary Researcher" })
mcp__flow-nexus__agent_spawn({ type: "analyst", name: "Data Analyst" })
mcp__flow-nexus__agent_spawn({ type: "analyst", name: "Insights Analyst" })
// 3. Orchestrate research task
mcp__flow-nexus__task_orchestrate({
task: "Research machine learning trends for 2025 and analyze market opportunities",
strategy: "parallel",
maxAgents: 4,
priority: "high"
})
mcp__flow-nexus__workflow_create({
name: "Deployment Pipeline",
description: "Automated testing, building, and multi-environment deployment",
steps: [
{ id: "lint", action: "lint_code", agent: "code_quality", parallel: true },
{ id: "unit_test", action: "unit_tests", agent: "test_runner", parallel: true },
{ id: "integration_test", action: "integration_tests", agent: "test_runner", parallel: true },
{ id: "build", action: "build_artifacts", agent: "builder", depends_on: ["lint", "unit_test", "integration_test"] },
{ id: "security_scan", action: "security_scan", agent: "security", depends_on: ["build"] },
{ id: "deploy_staging", action: "deploy", agent: "deployer", depends_on: ["security_scan"] },
{ id: "smoke_test", action: "smoke_tests", agent: "test_runner", depends_on: ["deploy_staging"] },
{ id: "deploy_prod", action: "deploy", agent: "deployer", depends_on: ["smoke_test"] }
],
triggers: ["github_push", "github_pr_merged"],
metadata: {
priority: 10,
auto_rollback: true
}
})
mcp__flow-nexus__workflow_create({
name: "ETL Pipeline",
description: "Extract, Transform, Load data processing",
steps: [
{ id: "extract", action: "extract_data", agent: "data_extractor" },
{ id: "validate_raw", action: "validate_data", agent: "validator", depends_on: ["extract"] },
{ id: "transform", action: "transform_data", agent: "transformer", depends_on: ["validate_raw"] },
{ id: "enrich", action: "enrich_data", agent: "enricher", depends_on: ["transform"] },
{ id: "load", action: "load_data", agent: "loader", depends_on: ["enrich"] },
{ id: "validate_final", action: "validate_data", agent: "validator", depends_on: ["load"] }
],
triggers: ["schedule:0 2 * * *"], // Daily at 2 AM
metadata: {
retry_policy: "exponential_backoff",
max_retries: 3
}
})
// Create swarm from template
mcp__flow-nexus__swarm_create_from_template({
template_name: "full-stack-dev",
overrides: {
maxAgents: 6,
strategy: "specialized"
}
})
// List available templates
mcp__flow-nexus__swarm_templates_list({
category: "quickstart", // Options: quickstart, specialized, enterprise, custom, all
includeStore: true
})
Available Template Categories:
Quickstart Templates:
full-stack-dev: Complete web development swarmresearch-team: Research and analysis swarmcode-review: Automated code review swarmdata-pipeline: ETL and data processingSpecialized Templates:
ml-development: Machine learning project swarmmobile-dev: Mobile app developmentdevops-automation: Infrastructure and deploymentsecurity-audit: Security analysis and testingEnterprise Templates:
enterprise-migration: Large-scale system migrationmulti-repo-sync: Multi-repository coordinationcompliance-review: Regulatory compliance workflowsincident-response: Automated incident managementSave successful swarm configurations as reusable templates for future projects.
// Subscribe to execution streams
mcp__flow-nexus__execution_stream_subscribe({
stream_type: "claude-flow-swarm",
deployment_id: "deployment_id"
})
// Get execution status
mcp__flow-nexus__execution_stream_status({
stream_id: "stream_id"
})
// List files created during execution
mcp__flow-nexus__execution_files_list({
stream_id: "stream_id",
created_by: "claude-flow"
})
// Get swarm performance metrics
mcp__flow-nexus__swarm_status({
swarm_id: "id"
})
// Analyze workflow efficiency
mcp__flow-nexus__workflow_status({
workflow_id: "id",
include_metrics: true
})
Coordinate multiple swarms for complex, multi-phase projects:
// Phase 1: Research swarm
const researchSwarm = await mcp__flow-nexus__swarm_init({
topology: "mesh",
maxAgents: 4
})
// Phase 2: Development swarm
const devSwarm = await mcp__flow-nexus__swarm_init({
topology: "hierarchical",
maxAgents: 8
})
// Phase 3: Testing swarm
const testSwarm = await mcp__flow-nexus__swarm_init({
topology: "star",
maxAgents: 5
})
// Simple projects: Star
mcp__flow-nexus__swarm_init({ topology: "star", maxAgents: 3 })
// Collaborative work: Mesh
mcp__flow-nexus__swarm_init({ topology: "mesh", maxAgents: 5 })
// Complex projects: Hierarchical
mcp__flow-nexus__swarm_init({ topology: "hierarchical", maxAgents: 10 })
// Sequential workflows: Ring
mcp__flow-nexus__swarm_init({ topology: "ring", maxAgents: 4 })
// Use vector similarity for optimal matching
mcp__flow-nexus__workflow_agent_assign({
task_id: "complex-task",
use_vector_similarity: true
})
mcp__flow-nexus__workflow_create({
name: "Resilient Workflow",
steps: [...],
metadata: {
retry_policy: "exponential_backoff",
max_retries: 3,
timeout: 300000, // 5 minutes
on_failure: "notify_and_rollback"
}
})
// Regular monitoring
const status = await mcp__flow-nexus__swarm_status()
// Scale based on workload
if (status.workload > 0.8) {
await mcp__flow-nexus__swarm_scale({ target_agents: status.agents + 2 })
}
// Long-running workflows should use message queues
mcp__flow-nexus__workflow_execute({
workflow_id: "data-pipeline",
async: true // Non-blocking execution
})
// Monitor progress
mcp__flow-nexus__workflow_queue_status({ include_messages: true })
// Destroy swarm when complete
mcp__flow-nexus__swarm_destroy({ swarm_id: "id" })
// Use proven templates instead of building from scratch
mcp__flow-nexus__swarm_create_from_template({
template_name: "code-review",
overrides: { maxAgents: 4 }
})
Flow Nexus swarms integrate seamlessly with Claude Flow hooks:
# Pre-task coordination setup
npx claude-flow@alpha hooks pre-task --description "Initialize swarm"
# Post-task metrics export
npx claude-flow@alpha hooks post-task --task-id "swarm-execution"
# Install Flow Nexus
npm install -g flow-nexus@latest
# Register account
npx flow-nexus@latest register
# Login
npx flow-nexus@latest login
# Add MCP server to Claude Code
claude mcp add flow-nexus npx flow-nexus@latest mcp start
Remember: Flow Nexus provides cloud-based orchestration infrastructure. For local execution and coordination, use the core claude-flow MCP server alongside Flow Nexus for maximum flexibility.