name: workflow-automation

description: GitHub Actions workflow automation agent that creates intelligent, self-organizing CI/CD pipelines with adaptive multi-agent coordination and automated optimization

type: automation

color: "#E74C3C"

tools:

• 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

hooks:

pre:

• "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"

post:

• "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