MCPInstall

swarm-advanced

by ruvnetTesting & QA

Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/ruvnet/ruview --skill swarm-advanced
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads swarm-advanced automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

Related Skills

  • sparc-methodology - Systematic development workflow
  • github-integration - Repository management and automation
  • neural-patterns - AI-powered coordination optimization
  • memory-management - Cross-session state persistence

References

Version: 2.0.0 Last Updated: 2025-10-19 Skill Level: Advanced Estimated Learning Time: 2-3 hours

FAQ

What does the swarm-advanced skill do?

Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows

How do I install swarm-advanced?

Run npx skills add https://github.com/ruvnet/ruview --skill swarm-advanced in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/swarm-advanced/ (personal) or .claude/skills/swarm-advanced/ (project).

Which AI agents can use swarm-advanced?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is swarm-advanced safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains swarm-advanced?

It is published by ruvnet in ruvnet/ruview. It has 39 recorded installs.

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ruvnet/ruview

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.

44 installs

pair programming

ruvnet/ruview

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

Code Quality & Workflow42 installs

v3 deep integration

ruvnet/ruview

Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.

AI & Agents42 installs

v3 swarm coordination

ruvnet/ruview

Open the skill to read its SKILL.md instructions.

41 installs

swarm orchestration

ruvnet/ruview

Open the skill to read its SKILL.md instructions.

41 installs

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Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

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migrate-to-shoehorn

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Migrate test files from as type assertions to @total-typescript/shoehorn. Use when user mentions shoehorn, wants to replace as in tests, or needs partial test data.

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run-train

lllllllama/rigorpilot-skills

Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized trainoutputs/. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.

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test-driven-development

obra/superpowers

Use when implementing any feature or bugfix, before writing implementation code

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finishing-a-development-branch

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Use when implementation is complete, all tests pass, and you need to decide how to integrate the work

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