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
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TodoRead
Bash
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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

Swarm-Powered Actions

.github$workflows$swarm-ci.yml

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

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

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

Self-Healing CI/CD

Auto-fix common CI failures

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

Progressive Deployment

Intelligent deployment strategy

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

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

PR Validation Swarm

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

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

Documentation Updates

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

Workflow Organization

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

Security

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

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

agent-workflow-automation

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