swarm-advanced

仓库: proffesor-for-testing/agentic-qe
安装量: 47
排名: #15597

安装

npx skills add https://github.com/ruvnet/claude-flow --skill swarm-advanced

Advanced Swarm Orchestration Master advanced swarm patterns for distributed research, development, and testing workflows. This skill covers comprehensive orchestration strategies using both MCP tools and CLI commands. Quick Start Prerequisites

Ensure Claude Flow is installed

npm install -g claude-flow@alpha

Add MCP server (if using MCP tools)

claude mcp add claude-flow npx claude-flow@alpha mcp start Basic Pattern // 1. Initialize swarm topology mcp__claude - flow__swarm_init ( { topology : "mesh" , maxAgents : 6 } ) // 2. Spawn specialized agents mcp__claude - flow__agent_spawn ( { type : "researcher" , name : "Agent 1" } ) // 3. Orchestrate tasks mcp__claude - flow__task_orchestrate ( { task : "..." , strategy : "parallel" } ) Core Concepts Swarm Topologies Mesh Topology - Peer-to-peer communication, best for research and analysis All agents communicate directly High flexibility and resilience Use for: Research, analysis, brainstorming Hierarchical Topology - Coordinator with subordinates, best for development Clear command structure Sequential workflow support Use for: Development, structured workflows Star Topology - Central coordinator, best for testing Centralized control and monitoring Parallel execution with coordination Use for: Testing, validation, quality assurance Ring Topology - Sequential processing chain Step-by-step processing Pipeline workflows Use for: Multi-stage processing, data pipelines Agent Strategies Adaptive - Dynamic adjustment based on task complexity Balanced - Equal distribution of work across agents Specialized - Task-specific agent assignment Parallel - Maximum concurrent execution Pattern 1: Research Swarm Purpose Deep research through parallel information gathering, analysis, and synthesis. Architecture // Initialize research swarm mcp__claude - flow__swarm_init ( { "topology" : "mesh" , "maxAgents" : 6 , "strategy" : "adaptive" } ) // Spawn research team const researchAgents = [ { type : "researcher" , name : "Web Researcher" , capabilities : [ "web-search" , "content-extraction" , "source-validation" ] } , { type : "researcher" , name : "Academic Researcher" , capabilities : [ "paper-analysis" , "citation-tracking" , "literature-review" ] } , { type : "analyst" , name : "Data Analyst" , capabilities : [ "data-processing" , "statistical-analysis" , "visualization" ] } , { type : "analyst" , name : "Pattern Analyzer" , capabilities : [ "trend-detection" , "correlation-analysis" , "outlier-detection" ] } , { type : "documenter" , name : "Report Writer" , capabilities : [ "synthesis" , "technical-writing" , "formatting" ] } ] // Spawn all agents researchAgents . forEach ( agent => { mcp__claude - flow__agent_spawn ( { type : agent . type , name : agent . name , capabilities : agent . capabilities } ) } ) Research Workflow Phase 1: Information Gathering // Parallel information collection mcp__claude - flow__parallel_execute ( { "tasks" : [ { "id" : "web-search" , "command" : "search recent publications and articles" } , { "id" : "academic-search" , "command" : "search academic databases and papers" } , { "id" : "data-collection" , "command" : "gather relevant datasets and statistics" } , { "id" : "expert-search" , "command" : "identify domain experts and thought leaders" } ] } ) // Store research findings in memory mcp__claude - flow__memory_usage ( { "action" : "store" , "key" : "research-findings-" + Date . now ( ) , "value" : JSON . stringify ( findings ) , "namespace" : "research" , "ttl" : 604800 // 7 days } ) Phase 2: Analysis and Validation // Pattern recognition in findings mcp__claude - flow__pattern_recognize ( { "data" : researchData , "patterns" : [ "trend" , "correlation" , "outlier" , "emerging-pattern" ] } ) // Cognitive analysis mcp__claude - flow__cognitive_analyze ( { "behavior" : "research-synthesis" } ) // Quality assessment mcp__claude - flow__quality_assess ( { "target" : "research-sources" , "criteria" : [ "credibility" , "relevance" , "recency" , "authority" ] } ) // Cross-reference validation mcp__claude - flow__neural_patterns ( { "action" : "analyze" , "operation" : "fact-checking" , "metadata" : { "sources" : sourcesArray } } ) Phase 3: Knowledge Management // Search existing knowledge base mcp__claude - flow__memory_search ( { "pattern" : "topic X" , "namespace" : "research" , "limit" : 20 } ) // Create knowledge graph connections mcp__claude - flow__neural_patterns ( { "action" : "learn" , "operation" : "knowledge-graph" , "metadata" : { "topic" : "X" , "connections" : relatedTopics , "depth" : 3 } } ) // Store connections for future use mcp__claude - flow__memory_usage ( { "action" : "store" , "key" : "knowledge-graph-X" , "value" : JSON . stringify ( knowledgeGraph ) , "namespace" : "research$graphs" , "ttl" : 2592000 // 30 days } ) Phase 4: Report Generation // Orchestrate report generation mcp__claude - flow__task_orchestrate ( { "task" : "generate comprehensive research report" , "strategy" : "sequential" , "priority" : "high" , "dependencies" : [ "gather" , "analyze" , "validate" , "synthesize" ] } ) // Monitor research progress mcp__claude - flow__swarm_status ( { "swarmId" : "research-swarm" } ) // Generate final report mcp__claude - flow__workflow_execute ( { "workflowId" : "research-report-generation" , "params" : { "findings" : findings , "format" : "comprehensive" , "sections" : [ "executive-summary" , "methodology" , "findings" , "analysis" , "conclusions" , "references" ] } } ) CLI Fallback

Quick research swarm

npx claude-flow swarm "research AI trends in 2025" \ --strategy research \ --mode distributed \ --max-agents 6 \ --parallel \ --output research-report.md Pattern 2: Development Swarm Purpose Full-stack development through coordinated specialist agents. Architecture // Initialize development swarm with hierarchy mcp__claude - flow__swarm_init ( { "topology" : "hierarchical" , "maxAgents" : 8 , "strategy" : "balanced" } ) // Spawn development team const devTeam = [ { type : "architect" , name : "System Architect" , role : "coordinator" } , { type : "coder" , name : "Backend Developer" , capabilities : [ "node" , "api" , "database" ] } , { type : "coder" , name : "Frontend Developer" , capabilities : [ "react" , "ui" , "ux" ] } , { type : "coder" , name : "Database Engineer" , capabilities : [ "sql" , "nosql" , "optimization" ] } , { type : "tester" , name : "QA Engineer" , capabilities : [ "unit" , "integration" , "e2e" ] } , { type : "reviewer" , name : "Code Reviewer" , capabilities : [ "security" , "performance" , "best-practices" ] } , { type : "documenter" , name : "Technical Writer" , capabilities : [ "api-docs" , "guides" , "tutorials" ] } , { type : "monitor" , name : "DevOps Engineer" , capabilities : [ "ci-cd" , "deployment" , "monitoring" ] } ] // Spawn all team members devTeam . forEach ( member => { mcp__claude - flow__agent_spawn ( { type : member . type , name : member . name , capabilities : member . capabilities , swarmId : "dev-swarm" } ) } ) Development Workflow Phase 1: Architecture and Design // System architecture design mcp__claude - flow__task_orchestrate ( { "task" : "design system architecture for REST API" , "strategy" : "sequential" , "priority" : "critical" , "assignTo" : "System Architect" } ) // Store architecture decisions mcp__claude - flow__memory_usage ( { "action" : "store" , "key" : "architecture-decisions" , "value" : JSON . stringify ( architectureDoc ) , "namespace" : "development$design" } ) Phase 2: Parallel Implementation // Parallel development tasks mcp__claude - flow__parallel_execute ( { "tasks" : [ { "id" : "backend-api" , "command" : "implement REST API endpoints" , "assignTo" : "Backend Developer" } , { "id" : "frontend-ui" , "command" : "build user interface components" , "assignTo" : "Frontend Developer" } , { "id" : "database-schema" , "command" : "design and implement database schema" , "assignTo" : "Database Engineer" } , { "id" : "api-documentation" , "command" : "create API documentation" , "assignTo" : "Technical Writer" } ] } ) // Monitor development progress mcp__claude - flow__swarm_monitor ( { "swarmId" : "dev-swarm" , "interval" : 5000 } ) Phase 3: Testing and Validation // Comprehensive testing mcp__claude - flow__batch_process ( { "items" : [ { type : "unit" , target : "all-modules" } , { type : "integration" , target : "api-endpoints" } , { type : "e2e" , target : "user-flows" } , { type : "performance" , target : "critical-paths" } ] , "operation" : "execute-tests" } ) // Quality assessment mcp__claude - flow__quality_assess ( { "target" : "codebase" , "criteria" : [ "coverage" , "complexity" , "maintainability" , "security" ] } ) Phase 4: Review and Deployment // Code review workflow mcp__claude - flow__workflow_execute ( { "workflowId" : "code-review-process" , "params" : { "reviewers" : [ "Code Reviewer" ] , "criteria" : [ "security" , "performance" , "best-practices" ] } } ) // CI/CD pipeline mcp__claude - flow__pipeline_create ( { "config" : { "stages" : [ "build" , "test" , "security-scan" , "deploy" ] , "environment" : "production" } } ) CLI Fallback

Quick development swarm

npx claude-flow swarm "build REST API with authentication" \ --strategy development \ --mode hierarchical \ --monitor \ --output sqlite Pattern 3: Testing Swarm Purpose Comprehensive quality assurance through distributed testing. Architecture // Initialize testing swarm with star topology mcp__claude - flow__swarm_init ( { "topology" : "star" , "maxAgents" : 7 , "strategy" : "parallel" } ) // Spawn testing team const testingTeam = [ { type : "tester" , name : "Unit Test Coordinator" , capabilities : [ "unit-testing" , "mocking" , "coverage" , "tdd" ] } , { type : "tester" , name : "Integration Tester" , capabilities : [ "integration" , "api-testing" , "contract-testing" ] } , { type : "tester" , name : "E2E Tester" , capabilities : [ "e2e" , "ui-testing" , "user-flows" , "selenium" ] } , { type : "tester" , name : "Performance Tester" , capabilities : [ "load-testing" , "stress-testing" , "benchmarking" ] } , { type : "monitor" , name : "Security Tester" , capabilities : [ "security-testing" , "penetration-testing" , "vulnerability-scanning" ] } , { type : "analyst" , name : "Test Analyst" , capabilities : [ "coverage-analysis" , "test-optimization" , "reporting" ] } , { type : "documenter" , name : "Test Documenter" , capabilities : [ "test-documentation" , "test-plans" , "reports" ] } ] // Spawn all testers testingTeam . forEach ( tester => { mcp__claude - flow__agent_spawn ( { type : tester . type , name : tester . name , capabilities : tester . capabilities , swarmId : "testing-swarm" } ) } ) Testing Workflow Phase 1: Test Planning // Analyze test coverage requirements mcp__claude - flow__quality_assess ( { "target" : "test-coverage" , "criteria" : [ "line-coverage" , "branch-coverage" , "function-coverage" , "edge-cases" ] } ) // Identify test scenarios mcp__claude - flow__pattern_recognize ( { "data" : testScenarios , "patterns" : [ "edge-case" , "boundary-condition" , "error-path" , "happy-path" ] } ) // Store test plan mcp__claude - flow__memory_usage ( { "action" : "store" , "key" : "test-plan-" + Date . now ( ) , "value" : JSON . stringify ( testPlan ) , "namespace" : "testing$plans" } ) Phase 2: Parallel Test Execution // Execute all test suites in parallel mcp__claude - flow__parallel_execute ( { "tasks" : [ { "id" : "unit-tests" , "command" : "npm run test:unit" , "assignTo" : "Unit Test Coordinator" } , { "id" : "integration-tests" , "command" : "npm run test:integration" , "assignTo" : "Integration Tester" } , { "id" : "e2e-tests" , "command" : "npm run test:e2e" , "assignTo" : "E2E Tester" } , { "id" : "performance-tests" , "command" : "npm run test:performance" , "assignTo" : "Performance Tester" } , { "id" : "security-tests" , "command" : "npm run test:security" , "assignTo" : "Security Tester" } ] } ) // Batch process test suites mcp__claude - flow__batch_process ( { "items" : testSuites , "operation" : "execute-test-suite" } ) Phase 3: Performance and Security // Run performance benchmarks mcp__claude - flow__benchmark_run ( { "suite" : "comprehensive-performance" } ) // Bottleneck analysis mcp__claude - flow__bottleneck_analyze ( { "component" : "application" , "metrics" : [ "response-time" , "throughput" , "memory" , "cpu" ] } ) // Security scanning mcp__claude - flow__security_scan ( { "target" : "application" , "depth" : "comprehensive" } ) // Vulnerability analysis mcp__claude - flow__error_analysis ( { "logs" : securityScanLogs } ) Phase 4: Monitoring and Reporting // Real-time test monitoring mcp__claude - flow__swarm_monitor ( { "swarmId" : "testing-swarm" , "interval" : 2000 } ) // Generate comprehensive test report mcp__claude - flow__performance_report ( { "format" : "detailed" , "timeframe" : "current-run" } ) // Get test results mcp__claude - flow__task_results ( { "taskId" : "test-execution-001" } ) // Trend analysis mcp__claude - flow__trend_analysis ( { "metric" : "test-coverage" , "period" : "30d" } ) CLI Fallback

Quick testing swarm

npx claude-flow swarm
"test application comprehensively"
\
--strategy
testing
\
--mode
star
\
--parallel
\
--timeout
600
Pattern 4: Analysis Swarm
Purpose
Deep code and system analysis through specialized analyzers.
Architecture
// Initialize analysis swarm
mcp__claude
-
flow__swarm_init
(
{
"topology"
:
"mesh"
,
"maxAgents"
:
5
,
"strategy"
:
"adaptive"
}
)
// Spawn analysis specialists
const
analysisTeam
=
[
{
type
:
"analyst"
,
name
:
"Code Analyzer"
,
capabilities
:
[
"static-analysis"
,
"complexity-analysis"
,
"dead-code-detection"
]
}
,
{
type
:
"analyst"
,
name
:
"Security Analyzer"
,
capabilities
:
[
"security-scan"
,
"vulnerability-detection"
,
"dependency-audit"
]
}
,
{
type
:
"analyst"
,
name
:
"Performance Analyzer"
,
capabilities
:
[
"profiling"
,
"bottleneck-detection"
,
"optimization"
]
}
,
{
type
:
"analyst"
,
name
:
"Architecture Analyzer"
,
capabilities
:
[
"dependency-analysis"
,
"coupling-detection"
,
"modularity-assessment"
]
}
,
{
type
:
"documenter"
,
name
:
"Analysis Reporter"
,
capabilities
:
[
"reporting"
,
"visualization"
,
"recommendations"
]
}
]
// Spawn all analysts
analysisTeam
.
forEach
(
analyst
=>
{
mcp__claude
-
flow__agent_spawn
(
{
type
:
analyst
.
type
,
name
:
analyst
.
name
,
capabilities
:
analyst
.
capabilities
}
)
}
)
Analysis Workflow
// Parallel analysis execution
mcp__claude
-
flow__parallel_execute
(
{
"tasks"
:
[
{
"id"
:
"analyze-code"
,
"command"
:
"analyze codebase structure and quality"
}
,
{
"id"
:
"analyze-security"
,
"command"
:
"scan for security vulnerabilities"
}
,
{
"id"
:
"analyze-performance"
,
"command"
:
"identify performance bottlenecks"
}
,
{
"id"
:
"analyze-architecture"
,
"command"
:
"assess architectural patterns"
}
]
}
)
// Generate comprehensive analysis report
mcp__claude
-
flow__performance_report
(
{
"format"
:
"detailed"
,
"timeframe"
:
"current"
}
)
// Cost analysis
mcp__claude
-
flow__cost_analysis
(
{
"timeframe"
:
"30d"
}
)
Advanced Techniques
Error Handling and Fault Tolerance
// Setup fault tolerance for all agents
mcp__claude
-
flow__daa_fault_tolerance
(
{
"agentId"
:
"all"
,
"strategy"
:
"auto-recovery"
}
)
// Error handling pattern
try
{
await
mcp__claude
-
flow__task_orchestrate
(
{
"task"
:
"complex operation"
,
"strategy"
:
"parallel"
,
"priority"
:
"high"
}
)
}
catch
(
error
)
{
// Check swarm health
const
status
=
await
mcp__claude
-
flow__swarm_status
(
{
}
)
// Analyze error patterns
await
mcp__claude
-
flow__error_analysis
(
{
"logs"
:
[
error
.
message
]
}
)
// Auto-recovery attempt
if
(
status
.
healthy
)
{
await
mcp__claude
-
flow__task_orchestrate
(
{
"task"
:
"retry failed operation"
,
"strategy"
:
"sequential"
}
)
}
}
Memory and State Management
// Cross-session persistence
mcp__claude
-
flow__memory_persist
(
{
"sessionId"
:
"swarm-session-001"
}
)
// Namespace management for different swarms
mcp__claude
-
flow__memory_namespace
(
{
"namespace"
:
"research-swarm"
,
"action"
:
"create"
}
)
// Create state snapshot
mcp__claude
-
flow__state_snapshot
(
{
"name"
:
"development-checkpoint-1"
}
)
// Restore from snapshot if needed
mcp__claude
-
flow__context_restore
(
{
"snapshotId"
:
"development-checkpoint-1"
}
)
// Backup memory stores
mcp__claude
-
flow__memory_backup
(
{
"path"
:
"$workspaces$claude-code-flow$backups$swarm-memory.json"
}
)
Neural Pattern Learning
// Train neural patterns from successful workflows
mcp__claude
-
flow__neural_train
(
{
"pattern_type"
:
"coordination"
,
"training_data"
:
JSON
.
stringify
(
successfulWorkflows
)
,
"epochs"
:
50
}
)
// Adaptive learning from experience
mcp__claude
-
flow__learning_adapt
(
{
"experience"
:
{
"workflow"
:
"research-to-report"
,
"success"
:
true
,
"duration"
:
3600
,
"quality"
:
0.95
}
}
)
// Pattern recognition for optimization
mcp__claude
-
flow__pattern_recognize
(
{
"data"
:
workflowMetrics
,
"patterns"
:
[
"bottleneck"
,
"optimization-opportunity"
,
"efficiency-gain"
]
}
)
Workflow Automation
// Create reusable workflow
mcp__claude
-
flow__workflow_create
(
{
"name"
:
"full-stack-development"
,
"steps"
:
[
{
"phase"
:
"design"
,
"agents"
:
[
"architect"
]
}
,
{
"phase"
:
"implement"
,
"agents"
:
[
"backend-dev"
,
"frontend-dev"
]
,
"parallel"
:
true
}
,
{
"phase"
:
"test"
,
"agents"
:
[
"tester"
,
"security-tester"
]
,
"parallel"
:
true
}
,
{
"phase"
:
"review"
,
"agents"
:
[
"reviewer"
]
}
,
{
"phase"
:
"deploy"
,
"agents"
:
[
"devops"
]
}
]
,
"triggers"
:
[
"on-commit"
,
"scheduled-daily"
]
}
)
// Setup automation rules
mcp__claude
-
flow__automation_setup
(
{
"rules"
:
[
{
"trigger"
:
"file-changed"
,
"pattern"
:
"*.js"
,
"action"
:
"run-tests"
}
,
{
"trigger"
:
"PR-created"
,
"action"
:
"code-review-swarm"
}
]
}
)
// Event-driven triggers
mcp__claude
-
flow__trigger_setup
(
{
"events"
:
[
"code-commit"
,
"PR-merge"
,
"deployment"
]
,
"actions"
:
[
"test"
,
"analyze"
,
"document"
]
}
)
Performance Optimization
// Topology optimization
mcp__claude
-
flow__topology_optimize
(
{
"swarmId"
:
"current-swarm"
}
)
// Load balancing
mcp__claude
-
flow__load_balance
(
{
"swarmId"
:
"development-swarm"
,
"tasks"
:
taskQueue
}
)
// Agent coordination sync
mcp__claude
-
flow__coordination_sync
(
{
"swarmId"
:
"development-swarm"
}
)
// Auto-scaling
mcp__claude
-
flow__swarm_scale
(
{
"swarmId"
:
"development-swarm"
,
"targetSize"
:
12
}
)
Monitoring and Metrics
// Real-time swarm monitoring
mcp__claude
-
flow__swarm_monitor
(
{
"swarmId"
:
"active-swarm"
,
"interval"
:
3000
}
)
// Collect comprehensive metrics
mcp__claude
-
flow__metrics_collect
(
{
"components"
:
[
"agents"
,
"tasks"
,
"memory"
,
"performance"
]
}
)
// Health monitoring
mcp__claude
-
flow__health_check
(
{
"components"
:
[
"swarm"
,
"agents"
,
"neural"
,
"memory"
]
}
)
// Usage statistics
mcp__claude
-
flow__usage_stats
(
{
"component"
:
"swarm-orchestration"
}
)
// Trend analysis
mcp__claude
-
flow__trend_analysis
(
{
"metric"
:
"agent-performance"
,
"period"
:
"7d"
}
)
Best Practices
1. Choosing the Right Topology
Mesh
Research, brainstorming, collaborative analysis
Hierarchical
Structured development, sequential workflows
Star
Testing, validation, centralized coordination
Ring
Pipeline processing, staged workflows
2. Agent Specialization
Assign specific capabilities to each agent
Avoid overlapping responsibilities
Use coordination agents for complex workflows
Leverage memory for agent communication
3. Parallel Execution
Identify independent tasks for parallelization
Use sequential execution for dependent tasks
Monitor resource usage during parallel execution
Implement proper error handling
4. Memory Management
Use namespaces to organize memory
Set appropriate TTL values
Create regular backups
Implement state snapshots for checkpoints
5. Monitoring and Optimization
Monitor swarm health regularly
Collect and analyze metrics
Optimize topology based on performance
Use neural patterns to learn from success
6. Error Recovery
Implement fault tolerance strategies
Use auto-recovery mechanisms
Analyze error patterns
Create fallback workflows
Real-World Examples
Example 1: AI Research Project
// Research AI trends, analyze findings, generate report
mcp__claude
-
flow__swarm_init
(
{
topology
:
"mesh"
,
maxAgents
:
6
}
)
// Spawn: 2 researchers, 2 analysts, 1 synthesizer, 1 documenter
// Parallel gather → Analyze patterns → Synthesize → Report
Example 2: Full-Stack Application
// Build complete web application with testing
mcp__claude
-
flow__swarm_init
(
{
topology
:
"hierarchical"
,
maxAgents
:
8
}
)
// Spawn: 1 architect, 2 devs, 1 db engineer, 2 testers, 1 reviewer, 1 devops
// Design → Parallel implement → Test → Review → Deploy
Example 3: Security Audit
// Comprehensive security analysis
mcp__claude
-
flow__swarm_init
(
{
topology
:
"star"
,
maxAgents
:
5
}
)
// Spawn: 1 coordinator, 1 code analyzer, 1 security scanner, 1 penetration tester, 1 reporter
// Parallel scan → Vulnerability analysis → Penetration test → Report
Example 4: Performance Optimization
// Identify and fix performance bottlenecks
mcp__claude
-
flow__swarm_init
(
{
topology
:
"mesh"
,
maxAgents
:
4
}
)
// Spawn: 1 profiler, 1 bottleneck analyzer, 1 optimizer, 1 tester
// Profile → Identify bottlenecks → Optimize → Validate
Troubleshooting
Common Issues
Issue
Swarm agents not coordinating properly
Solution
Check topology selection, verify memory usage, enable monitoring
Issue
Parallel execution failing
Solution
Verify task dependencies, check resource limits, implement error handling
Issue
Memory persistence not working
Solution
Verify namespaces, check TTL settings, ensure backup configuration
Issue
Performance degradation
Solution
Optimize topology, reduce agent count, analyze bottlenecks
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