Menü
Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.
Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.
Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.
| name | flow-nexus-swarm |
| description | Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform |
| tags | ["domain/skills","artifact/config","source/.claude"] |
Deploy and manage cloud-based AI agent swarms with event-driven workflow automation, message queue processing, and intelligent agent coordination.
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.