Agent Workflow Automation

by ruvnet6051f6702b61No license74K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

Agent skill for workflow-automation - invoke with $agent-workflow-automation

Instructions onlyDevOps & Cloud AI & Agents

AI-generated overview

Guides AI-agent coordination for GitHub Actions CI/CD pipelines, including workflow generation, optimization and failure analysis.

What it does: This instruction-only skill describes how to combine AI agent swarms with GitHub Actions to build and maintain CI/CD pipelines. It covers generating workflows, selecting tests, detecting languages, optimizing pipelines and costs, analyzing failures, and monitoring performance. It also documents multi-agent orchestration calls and workflow templates for deployment, security scanning and release automation.
When to use it: Use it when setting up or improving GitHub Actions CI/CD pipelines, especially when you want agent-assisted workflow generation, test selection, cost or performance optimization, or failure diagnosis. It also fits teams coordinating multiple agents around pipeline automation.
Requirements: Requires GitHub Actions and GitHub CLI access, plus the referenced MCP tools for GitHub and claude-flow and the ruv-swarm command-line tool. It ships no scripts; it is instructions only.

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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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

yaml

# .github$workflows$swarm-ci.ymlname: Intelligent CI with Swarmson: [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

# .github$workflows$swarm-ci.ymlname: Intelligent CI with Swarmson: [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

bash

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

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

3. Intelligent Test Selection

yaml

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

# 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

yaml

# .github$workflows$polyglot-swarm.ymlname: Polyglot Project Handleron: 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

# .github$workflows$polyglot-swarm.ymlname: Polyglot Project Handleron: 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

yaml

# .github$workflows$security-swarm.ymlname: Intelligent Security Scanon:  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

# .github$workflows$security-swarm.ymlname: Intelligent Security Scanon:  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

bash

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

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

Failure Analysis

bash

# Analyze failed runs using gh CLIgh run view ${{ github.run_id }} --json jobs,conclusion | \  npx ruv-swarm actions analyze-failure \    --suggest-fixes \    --auto-retry-flaky
# Create issue for persistent failuresif [ $? -ne 0 ]; then  gh issue create \    --title "CI Failure: Run ${{ github.run_id }}" \    --body "Automated analysis detected persistent failures" \    --label "ci-failure"fi

# Analyze failed runs using gh CLIgh run view ${{ github.run_id }} --json jobs,conclusion | \  npx ruv-swarm actions analyze-failure \    --suggest-fixes \    --auto-retry-flaky
# Create issue for persistent failuresif [ $? -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

bash

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

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

Advanced Workflows

1. Self-Healing CI/CD

yaml

# Auto-fix common CI failuresname: Self-Healing Pipelineon: 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

# Auto-fix common CI failuresname: Self-Healing Pipelineon: 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

yaml

# Intelligent deployment strategyname: Smart Deploymenton:  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

# Intelligent deployment strategyname: Smart Deploymenton:  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

yaml

# Automatic performance testingname: Performance Guardon: 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

# Automatic performance testingname: Performance Guardon: 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

javascript

// action.ymlname: 'Swarm Custom Action'description: 'Custom swarm-powered action'inputs:  task:    description: 'Task for swarm'    required: trueruns:  using: 'node16'  main: 'dist$index.js'
// index.jsconst { SwarmAction } = require('ruv-swarm');
async function run() {  const swarm = new SwarmAction({    topology: 'mesh',    agents: ['analyzer', 'optimizer']  });    await swarm.execute(core.getInput('task'));}

// action.ymlname: 'Swarm Custom Action'description: 'Custom swarm-powered action'inputs:  task:    description: 'Task for swarm'    required: trueruns:  using: 'node16'  main: 'dist$index.js'
// index.jsconst { 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

yaml

# Generate test matrix from code analysisjobs:  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)}}

# Generate test matrix from code analysisjobs:  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

bash

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

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

Monitoring & Insights

Workflow Analytics

bash

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

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

Cost Optimization

bash

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

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

Failure Patterns

bash

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

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

Integration Examples

1. PR Validation Swarm

yaml

name: PR Validation Swarmon: 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"

name: PR Validation Swarmon: 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

yaml

name: Intelligent Releaseon:  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

name: Intelligent Releaseon:  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

yaml

name: Auto Documentationon:  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

name: Auto Documentationon:  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

bash

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

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

Workflow Recommendations

bash

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

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

Automated Optimization

bash

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

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

Debugging & Troubleshooting

Debug Mode

yaml

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

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

Performance Profiling

bash

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

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

Advanced Swarm Workflow Automation

Multi-Agent Pipeline Orchestration

bash

# Initialize comprehensive workflow automation swarmmcp__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 rulesmcp__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 managementmcp__claude-flow__task_orchestrate {  task: "Manage intelligent CI/CD pipeline with continuous optimization",  strategy: "adaptive",  priority: "high",  dependencies: ["code_analysis", "test_optimization", "deployment_strategy"]}

# Initialize comprehensive workflow automation swarmmcp__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 rulesmcp__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 managementmcp__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

bash

# Generate comprehensive workflow performance reportsmcp__claude-flow__performance_report {  format: "detailed",  timeframe: "30d"}
# Analyze workflow bottlenecks with swarm intelligencemcp__claude-flow__bottleneck_analyze {  component: "github_actions_workflow",  metrics: ["build_time", "test_duration", "deployment_latency", "resource_utilization"]}
# Store performance insights in swarm memorymcp__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%"  }}

# Generate comprehensive workflow performance reportsmcp__claude-flow__performance_report {  format: "detailed",  timeframe: "30d"}
# Analyze workflow bottlenecks with swarm intelligencemcp__claude-flow__bottleneck_analyze {  component: "github_actions_workflow",  metrics: ["build_time", "test_duration", "deployment_latency", "resource_utilization"]}
# Store performance insights in swarm memorymcp__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

javascript

// Swarm-powered workflow creationconst 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;};

// Swarm-powered workflow creationconst 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

bash

# Implement continuous workflow learningmcp__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 recommendationsmcp__claude-flow__task_orchestrate {  task: "Analyze workflow performance and generate optimization recommendations",  strategy: "parallel",  priority: "medium"}

# Implement continuous workflow learningmcp__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 recommendationsmcp__claude-flow__task_orchestrate {  task: "Analyze workflow performance and generate optimization recommendations",  strategy: "parallel",  priority: "medium"}

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

Source and attribution

Source:ruvnet/rufloin.agents/skills/agent-workflow-automationat commit6051f67

License: No license

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Agent Workflow Automation

Workflow Automation - GitHub Actions Integration

Overview

Core Features

1. Swarm-Powered Actions

2. Dynamic Workflow Generation

3. Intelligent Test Selection

Workflow Templates

Multi-Language Detection

Adaptive Security Scanning

Action Commands

Pipeline Optimization

Failure Analysis

Resource Management

Advanced Workflows

1. Self-Healing CI/CD

2. Progressive Deployment

3. Performance Regression Detection

Custom Actions

Swarm Action Development

Matrix Strategies

Dynamic Test Matrix

Intelligent Parallelization

Monitoring & Insights

Workflow Analytics

Cost Optimization

Failure Patterns

Integration Examples

1. PR Validation Swarm

2. Release Automation

3. Documentation Updates

Best Practices

1. Workflow Organization

2. Security

3. Performance

Advanced Features

Predictive Failures

Workflow Recommendations

Automated Optimization

Debugging & Troubleshooting

Debug Mode

Performance Profiling

Advanced Swarm Workflow Automation

Multi-Agent Pipeline Orchestration

Intelligent Performance Monitoring

Dynamic Workflow Generation

Continuous Learning and Optimization

Source and attribution

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Cron Schedule

Autopilot Predict

Autopilot Loop

Worker Integration

Worker Benchmarks

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