Agent 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"
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.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
```bash
# Generate workflows based on code analysis
npx 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.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
```yaml
# .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 \