Agent Workflow Automation

作者 ruvnet6051f6702b61無授權條款74K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新

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

僅含說明DevOps & CloudAI & Agents
AI 產生的概覽

指導 AI 代理協作 GitHub Actions CI/CD 流程，包括工作流程產生、最佳化與失敗分析。

功能
此技能僅含指示，說明如何將 AI 代理叢集與 GitHub Actions 結合，以建置與維護 CI/CD 流程。內容涵蓋產生工作流程、挑選測試、偵測語言、最佳化流程與成本、分析失敗，以及監控效能。它也記錄多代理協同呼叫，以及用於部署、安全掃描與發佈自動化的工作流程範本。
適用情境
適用於建置或改善 GitHub Actions CI/CD 流程，尤其是希望借助代理產生工作流程、挑選測試、最佳化成本或效能，或診斷失敗時。也適合圍繞流程自動化協調多個代理的團隊。
執行需求
需要 GitHub Actions 與 GitHub CLI 存取權，以及所引用的 GitHub 與 claude-flow MCP 工具和 ruv-swarm 命令列工具。此技能不附帶指令碼，僅為指示文件。

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

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

2. Dynamic Workflow Generation

bash
# 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

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

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

Action Commands

Pipeline Optimization

bash
# 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

Resource Management

bash
# 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

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

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

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'));}

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)}}

Intelligent Parallelization

bash
# 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

Cost Optimization

bash
# 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

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"

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

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

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

Workflow Recommendations

bash
# 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

Debugging & Troubleshooting

Debug Mode

yaml
- 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

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"]}

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%"  }}

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;};

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"}

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

來源與署名

來源：ruvnet/ruflo位於.agents/skills/agent-workflow-automation提交6051f67

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