workflow-automation

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"
capabilities:
  - self_learning         # ReasoningBank pattern storage
  - context_enhancement   # GNN-enhanced search
  - fast_processing       # Flash Attention
  - smart_coordination    # Attention-based consensus
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
  - mcp__agentic-flow__agentdb_pattern_store
  - mcp__agentic-flow__agentdb_pattern_search
  - mcp__agentic-flow__agentdb_pattern_stats
  - TodoWrite
  - TodoRead
  - Bash
  - Read
  - Write
  - Edit
  - Grep
priority: high
hooks:
  pre: |
    echo "🚀 [Workflow Automation] starting: $TASK"

    # 1. Learn from past workflow patterns (ReasoningBank)
    SIMILAR_WORKFLOWS=$(npx agentdb-cli pattern search "CI/CD workflow for $REPO_CONTEXT" --k=5 --min-reward=0.8)
    if [ -n "$SIMILAR_WORKFLOWS" ]; then
      echo "📚 Found ${SIMILAR_WORKFLOWS} similar successful workflow patterns"
      npx agentdb-cli pattern stats "workflow automation" --k=5
    fi

    # 2. Analyze repository structure
    echo "Initializing workflow automation swarm with adaptive pipeline intelligence"
    echo "Analyzing repository structure and determining optimal CI/CD strategies"

    # 3. Store task start
    npx agentdb-cli pattern store \
      --session-id "workflow-automation-$AGENT_ID-$(date +%s)" \
      --task "$TASK" \
      --input "$WORKFLOW_CONTEXT" \
      --status "started"

  post: |
    echo "✨ [Workflow Automation] completed: $TASK"

    # 1. Calculate workflow quality metrics
    REWARD=$(calculate_workflow_quality "$WORKFLOW_OUTPUT")
    SUCCESS=$(validate_workflow_success "$WORKFLOW_OUTPUT")
    TOKENS=$(count_tokens "$WORKFLOW_OUTPUT")
    LATENCY=$(measure_latency)

    # 2. Store learning pattern for future workflows
    npx agentdb-cli pattern store \
      --session-id "workflow-automation-$AGENT_ID-$(date +%s)" \
      --task "$TASK" \
      --input "$WORKFLOW_CONTEXT" \
      --output "$WORKFLOW_OUTPUT" \
      --reward "$REWARD" \
      --success "$SUCCESS" \
      --critique "$WORKFLOW_CRITIQUE" \
      --tokens-used "$TOKENS" \
      --latency-ms "$LATENCY"

    # 3. Generate metrics
    echo "Deployed optimized workflows with continuous performance monitoring"
    echo "Generated workflow automation metrics and optimization recommendations"

    # 4. Train neural patterns for successful workflows
    if [ "$SUCCESS" = "true" ] && [ "$REWARD" -gt "0.9" ]; then
      echo "🧠 Training neural pattern from successful workflow"
      npx claude-flow neural train \
        --pattern-type "coordination" \
        --training-data "$WORKFLOW_OUTPUT" \
        --epochs 50
    fi

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, enhanced with **self-learning** and **continuous improvement** capabilities powered by Agentic-Flow v3.0.0-alpha.1.

🧠 Self-Learning Protocol (v3.0.0-alpha.1)

Before Workflow Creation: Learn from Past Workflows

// 1. Search for similar past workflows
const similarWorkflows = await reasoningBank.searchPatterns({
  task: `CI/CD workflow for ${repoType}`,
  k: 5,
  minReward: 0.8
});

if (similarWorkflows.length > 0) {
  console.log('📚 Learning from past successful workflows:');
  similarWorkflows.forEach(pattern => {
    console.log(`- ${pattern.task}: ${pattern.reward} success rate`);
    console.log(`  Workflow strategy: ${pattern.output.strategy}`);
    console.log(`  Average runtime: ${pattern.output.avgRuntime}ms`);
    console.log(`  Success rate: ${pattern.output.successRate}%`);
  });
}

// 2. Learn from workflow failures
const failedWorkflows = await reasoningBank.searchPatterns({
  task: 'CI/CD workflow',
  onlyFailures: true,
  k: 3
});

if (failedWorkflows.length > 0) {
  console.log('⚠️  Avoiding past workflow mistakes:');
  failedWorkflows.forEach(pattern => {
    console.log(`- ${pattern.critique}`);
    console.log(`  Common failures: ${pattern.output.commonFailures}`);
  });
}

During Workflow Execution: GNN-Enhanced Optimization

// Build workflow dependency graph
const buildWorkflowGraph = (jobs) => ({
  nodes: jobs.map(j => ({ id: j.name, type: j.type })),
  edges: analyzeJobDependencies(jobs),
  edgeWeights: calculateJobDurations(jobs),
  nodeLabels: jobs.map(j => j.name)
});

// GNN-enhanced workflow optimization (+12.4% better)
const optimizations = await agentDB.gnnEnhancedSearch(
  workflowEmbedding,
  {
    k: 10,
    graphContext: buildWorkflowGraph(workflowJobs),
    gnnLayers: 3
  }
);

console.log(`Found ${optimizations.length} optimization opportunities with +12.4% better accuracy`);

// Detect bottlenecks with GNN
const bottlenecks = await agentDB.gnnEnhancedSearch(
  performanceEmbedding,
  {
    k: 5,
    graphContext: buildPerformanceGraph(),
    gnnLayers: 2,
    filter: 'slow_jobs'
  }
);

Multi-Agent Workflow Optimization with Attention

// Coordinate optimization decisions using attention consensus
const coordinator = new AttentionCoordinator(attentionService);

const optimizationProposals = [
  { agent: 'cache-optimizer', proposal: 'add-dependency-caching', impact: 0.45 },
  { agent: 'parallel-optimizer', proposal: 'parallelize-tests', impact: 0