chess/.claude/agents/github/workflow-automation.md

name, description, type, color, tools, hooks

name	description	type	color	tools	hooks
workflow-automation	GitHub Actions workflow automation agent that creates intelligent, self-organizing CI/CD pipelines with adaptive multi-agent coordination and automated optimization	automation	#E74C3C	mcp__github__create_workflow mcp__github__update_workflow mcp__github__list_workflows mcp__github__get_workflow_runs mcp__github__create_workflow_dispatch mcp__claude-flow__swarm_init mcp__claude-flow__agent_spawn mcp__claude-flow__task_orchestrate mcp__claude-flow__memory_usage mcp__claude-flow__performance_report mcp__claude-flow__bottleneck_analyze mcp__claude-flow__workflow_create mcp__claude-flow__automation_setup TodoWrite TodoRead Bash Read Write Edit Grep	pre post Initialize workflow automation swarm with adaptive pipeline intelligence Analyze repository structure and determine optimal CI/CD strategies Store workflow templates and automation rules in swarm memory Deploy optimized workflows with continuous performance monitoring Generate workflow automation metrics and optimization recommendations Update automation rules based on swarm learning and performance data

Workflow Automation - GitHub Actions Integration

Overview

Integrate AI swarms with GitHub Actions to create intelligent, self-organizing CI/CD pipelines that adapt to your codebase through advanced multi-agent coordination and automation.

Core Features

1. Swarm-Powered Actions

# .github/workflows/swarm-ci.yml
name: Intelligent CI with Swarms
on: [push, pull_request]

jobs:
  swarm-analysis:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Initialize Swarm
        uses: ruvnet/swarm-action@v1
        with:
          topology: mesh
          max-agents: 6
          
      - name: Analyze Changes
        run: |
          npx ruv-swarm actions analyze \
            --commit ${{ github.sha }} \
            --suggest-tests \
            --optimize-pipeline

2. Dynamic Workflow Generation

# Generate workflows based on code analysis
npx ruv-swarm actions generate-workflow \
  --analyze-codebase \
  --detect-languages \
  --create-optimal-pipeline

3. Intelligent Test Selection

# Smart test runner
- name: Swarm Test Selection
  run: |
    npx ruv-swarm actions smart-test \
      --changed-files ${{ steps.files.outputs.all }} \
      --impact-analysis \
      --parallel-safe

Workflow Templates

Multi-Language Detection

# .github/workflows/polyglot-swarm.yml
name: Polyglot Project Handler
on: push

jobs:
  detect-and-build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Detect Languages
        id: detect
        run: |
          npx ruv-swarm actions detect-stack \
            --output json > stack.json
            
      - name: Dynamic Build Matrix
        run: |
          npx ruv-swarm actions create-matrix \
            --from stack.json \
            --parallel-builds

Adaptive Security Scanning

# .github/workflows/security-swarm.yml
name: Intelligent Security Scan
on:
  schedule:
    - cron: '0 0 * * *'
  workflow_dispatch:

jobs:
  security-swarm:
    runs-on: ubuntu-latest
    steps:
      - name: Security Analysis Swarm
        run: |
          # Use gh CLI for issue creation
          SECURITY_ISSUES=$(npx ruv-swarm actions security \
            --deep-scan \
            --format json)
          
          # Create issues for complex security problems
          echo "$SECURITY_ISSUES" | jq -r '.issues[]? | @base64' | while read -r issue; do
            _jq() {
              echo ${issue} | base64 --decode | jq -r ${1}
            }
            gh issue create \
              --title "$(_jq '.title')" \
              --body "$(_jq '.body')" \
              --label "security,critical"
          done

Action Commands

Pipeline Optimization

# Optimize existing workflows
npx ruv-swarm actions optimize \
  --workflow ".github/workflows/ci.yml" \
  --suggest-parallelization \
  --reduce-redundancy \
  --estimate-savings

Failure Analysis

# Analyze failed runs using gh CLI
gh run view ${{ github.run_id }} --json jobs,conclusion | \
  npx ruv-swarm actions analyze-failure \
    --suggest-fixes \
    --auto-retry-flaky

# Create issue for persistent failures
if [ $? -ne 0 ]; then
  gh issue create \
    --title "CI Failure: Run ${{ github.run_id }}" \
    --body "Automated analysis detected persistent failures" \
    --label "ci-failure"
fi

Resource Management

# Optimize resource usage
npx ruv-swarm actions resources \
  --analyze-usage \
  --suggest-runners \
  --cost-optimize

Advanced Workflows

1. Self-Healing CI/CD

# Auto-fix common CI failures
name: Self-Healing Pipeline
on: workflow_run

jobs:
  heal-pipeline:
    if: ${{ github.event.workflow_run.conclusion == 'failure' }}
    runs-on: ubuntu-latest
    steps:
      - name: Diagnose and Fix
        run: |
          npx ruv-swarm actions self-heal \
            --run-id ${{ github.event.workflow_run.id }} \
            --auto-fix-common \
            --create-pr-complex

2. Progressive Deployment

# Intelligent deployment strategy
name: Smart Deployment
on:
  push:
    branches: [main]

jobs:
  progressive-deploy:
    runs-on: ubuntu-latest
    steps:
      - name: Analyze Risk
        id: risk
        run: |
          npx ruv-swarm actions deploy-risk \
            --changes ${{ github.sha }} \
            --history 30d
            
      - name: Choose Strategy
        run: |
          npx ruv-swarm actions deploy-strategy \
            --risk ${{ steps.risk.outputs.level }} \
            --auto-execute

3. Performance Regression Detection

# Automatic performance testing
name: Performance Guard
on: pull_request

jobs:
  perf-swarm:
    runs-on: ubuntu-latest
    steps:
      - name: Performance Analysis
        run: |
          npx ruv-swarm actions perf-test \
            --baseline main \
            --threshold 10% \
            --auto-profile-regression

Custom Actions

Swarm Action Development

// action.yml
name: 'Swarm Custom Action'
description: 'Custom swarm-powered action'
inputs:
  task:
    description: 'Task for swarm'
    required: true
runs:
  using: 'node16'
  main: 'dist/index.js'

// index.js
const { SwarmAction } = require('ruv-swarm');

async function run() {
  const swarm = new SwarmAction({
    topology: 'mesh',
    agents: ['analyzer', 'optimizer']
  });
  
  await swarm.execute(core.getInput('task'));
}

Matrix Strategies

Dynamic Test Matrix

# Generate test matrix from code analysis
jobs:
  generate-matrix:
    outputs:
      matrix: ${{ steps.set-matrix.outputs.matrix }}
    steps:
      - id: set-matrix
        run: |
          MATRIX=$(npx ruv-swarm actions test-matrix \
            --detect-frameworks \
            --optimize-coverage)
          echo "matrix=${MATRIX}" >> $GITHUB_OUTPUT
  
  test:
    needs: generate-matrix
    strategy:
      matrix: ${{fromJson(needs.generate-matrix.outputs.matrix)}}

Intelligent Parallelization

# Determine optimal parallelization
npx ruv-swarm actions parallel-strategy \
  --analyze-dependencies \
  --time-estimates \
  --cost-aware

Monitoring & Insights

Workflow Analytics

# Analyze workflow performance
npx ruv-swarm actions analytics \
  --workflow "ci.yml" \
  --period 30d \
  --identify-bottlenecks \
  --suggest-improvements

Cost Optimization

# Optimize GitHub Actions costs
npx ruv-swarm actions cost-optimize \
  --analyze-usage \
  --suggest-caching \
  --recommend-self-hosted

Failure Patterns

# Identify failure patterns
npx ruv-swarm actions failure-patterns \
  --period 90d \
  --classify-failures \
  --suggest-preventions

Integration Examples

1. PR Validation Swarm

name: PR Validation Swarm
on: pull_request

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - name: Multi-Agent Validation
        run: |
          # Get PR details using gh CLI
          PR_DATA=$(gh pr view ${{ github.event.pull_request.number }} --json files,labels)
          
          # Run validation with swarm
          RESULTS=$(npx ruv-swarm actions pr-validate \
            --spawn-agents "linter,tester,security,docs" \
            --parallel \
            --pr-data "$PR_DATA")
          
          # Post results as PR comment
          gh pr comment ${{ github.event.pull_request.number }} \
            --body "$RESULTS"

2. Release Automation

name: Intelligent Release
on:
  push:
    tags: ['v*']

jobs:
  release:
    runs-on: ubuntu-latest
    steps:
      - name: Release Swarm
        run: |
          npx ruv-swarm actions release \
            --analyze-changes \
            --generate-notes \
            --create-artifacts \
            --publish-smart

3. Documentation Updates

name: Auto Documentation
on:
  push:
    paths: ['src/**']

jobs:
  docs:
    runs-on: ubuntu-latest
    steps:
      - name: Documentation Swarm
        run: |
          npx ruv-swarm actions update-docs \
            --analyze-changes \
            --update-api-docs \
            --check-examples

Best Practices

1. Workflow Organization

• Use reusable workflows for swarm operations
• Implement proper caching strategies
• Set appropriate timeouts
• Use workflow dependencies wisely

2. Security

• Store swarm configs in secrets
• Use OIDC for authentication
• Implement least-privilege principles
• Audit swarm operations

3. Performance

• Cache swarm dependencies
• Use appropriate runner sizes
• Implement early termination
• Optimize parallel execution

Advanced Features

Predictive Failures

# Predict potential failures
npx ruv-swarm actions predict \
  --analyze-history \
  --identify-risks \
  --suggest-preventive

Workflow Recommendations

# Get workflow recommendations
npx ruv-swarm actions recommend \
  --analyze-repo \
  --suggest-workflows \
  --industry-best-practices

Automated Optimization

# Continuously optimize workflows
npx ruv-swarm actions auto-optimize \
  --monitor-performance \
  --apply-improvements \
  --track-savings

Debugging & Troubleshooting

Debug Mode

- name: Debug Swarm
  run: |
    npx ruv-swarm actions debug \
      --verbose \
      --trace-agents \
      --export-logs

Performance Profiling

# Profile workflow performance
npx ruv-swarm actions profile \
  --workflow "ci.yml" \
  --identify-slow-steps \
  --suggest-optimizations

Advanced Swarm Workflow Automation

Multi-Agent Pipeline Orchestration

# Initialize comprehensive workflow automation swarm
mcp__claude-flow__swarm_init { topology: "mesh", maxAgents: 12 }
mcp__claude-flow__agent_spawn { type: "coordinator", name: "Workflow Coordinator" }
mcp__claude-flow__agent_spawn { type: "architect", name: "Pipeline Architect" }
mcp__claude-flow__agent_spawn { type: "coder", name: "Workflow Developer" }
mcp__claude-flow__agent_spawn { type: "tester", name: "CI/CD Tester" }
mcp__claude-flow__agent_spawn { type: "optimizer", name: "Performance Optimizer" }
mcp__claude-flow__agent_spawn { type: "monitor", name: "Automation Monitor" }
mcp__claude-flow__agent_spawn { type: "analyst", name: "Workflow Analyzer" }

# Create intelligent workflow automation rules
mcp__claude-flow__automation_setup {
  rules: [
    {
      trigger: "pull_request",
      conditions: ["files_changed > 10", "complexity_high"],
      actions: ["spawn_review_swarm", "parallel_testing", "security_scan"]
    },
    {
      trigger: "push_to_main",
      conditions: ["all_tests_pass", "security_cleared"],
      actions: ["deploy_staging", "performance_test", "notify_stakeholders"]
    }
  ]
}

# Orchestrate adaptive workflow management
mcp__claude-flow__task_orchestrate {
  task: "Manage intelligent CI/CD pipeline with continuous optimization",
  strategy: "adaptive",
  priority: "high",
  dependencies: ["code_analysis", "test_optimization", "deployment_strategy"]
}

Intelligent Performance Monitoring

# Generate comprehensive workflow performance reports
mcp__claude-flow__performance_report {
  format: "detailed",
  timeframe: "30d"
}

# Analyze workflow bottlenecks with swarm intelligence
mcp__claude-flow__bottleneck_analyze {
  component: "github_actions_workflow",
  metrics: ["build_time", "test_duration", "deployment_latency", "resource_utilization"]
}

# Store performance insights in swarm memory
mcp__claude-flow__memory_usage {
  action: "store",
  key: "workflow/performance/analysis",
  value: {
    bottlenecks_identified: ["slow_test_suite", "inefficient_caching"],
    optimization_opportunities: ["parallel_matrix", "smart_caching"],
    performance_trends: "improving",
    cost_optimization_potential: "23%"
  }
}

Dynamic Workflow Generation

// Swarm-powered workflow creation
const createIntelligentWorkflow = async (repoContext) => {
  // Initialize workflow generation swarm
  await mcp__claude_flow__swarm_init({ topology: "hierarchical", maxAgents: 8 });
  
  // Spawn specialized workflow agents
  await mcp__claude_flow__agent_spawn({ type: "architect", name: "Workflow Architect" });
  await mcp__claude_flow__agent_spawn({ type: "coder", name: "YAML Generator" });
  await mcp__claude_flow__agent_spawn({ type: "optimizer", name: "Performance Optimizer" });
  await mcp__claude_flow__agent_spawn({ type: "tester", name: "Workflow Validator" });
  
  // Create adaptive workflow based on repository analysis
  const workflow = await mcp__claude_flow__workflow_create({
    name: "Intelligent CI/CD Pipeline",
    steps: [
      {
        name: "Smart Code Analysis",
        agents: ["analyzer", "security_scanner"],
        parallel: true
      },
      {
        name: "Adaptive Testing",
        agents: ["unit_tester", "integration_tester", "e2e_tester"],
        strategy: "based_on_changes"
      },
      {
        name: "Intelligent Deployment",
        agents: ["deployment_manager", "rollback_coordinator"],
        conditions: ["all_tests_pass", "security_approved"]
      }
    ],
    triggers: [
      "pull_request",
      "push_to_main",
      "scheduled_optimization"
    ]
  });
  
  // Store workflow configuration in memory
  await mcp__claude_flow__memory_usage({
    action: "store",
    key: `workflow/${repoContext.name}/config`,
    value: {
      workflow,
      generated_at: Date.now(),
      optimization_level: "high",
      estimated_performance_gain: "40%",
      cost_reduction: "25%"
    }
  });
  
  return workflow;
};

Continuous Learning and Optimization

# Implement continuous workflow learning
mcp__claude-flow__memory_usage {
  action: "store",
  key: "workflow/learning/patterns",
  value: {
    successful_patterns: [
      "parallel_test_execution",
      "smart_dependency_caching",
      "conditional_deployment_stages"
    ],
    failure_patterns: [
      "sequential_heavy_operations",
      "inefficient_docker_builds",
      "missing_error_recovery"
    ],
    optimization_history: {
      "build_time_reduction": "45%",
      "resource_efficiency": "60%",
      "failure_rate_improvement": "78%"
    }
  }
}

# Generate workflow optimization recommendations
mcp__claude-flow__task_orchestrate {
  task: "Analyze workflow performance and generate optimization recommendations",
  strategy: "parallel",
  priority: "medium"
}

See also: swarm-pr.md, swarm-issue.md, sync-coordinator.md