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
.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
Generate workflows based on code analysis
npx ruv-swarm actions generate-workflow \ --analyze-codebase \ --detect-languages \ --create-optimal-pipeline 3. 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$polyglot-swarm.yml
name : Polyglot Project Handler on : push jobs : detect-and-build : runs-on : ubuntu - latest steps : - uses : actions$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
.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 1. Self-Healing CI/CD
Auto-fix common CI failures
name : Self - Healing Pipeline on : 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
Intelligent deployment strategy
name : Smart Deployment on : 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 ${{ steps.risk.outputs.level }} \ --auto-execute 3. Performance Regression Detection
Automatic performance testing
name : Performance Guard on : pull_request jobs : perf-swarm : runs-on : ubuntu - latest steps : - name : Performance Analysis run : | npx ruv-swarm actions perf-test \ --baseline main \ --threshold 10% \ --auto-profile-regression Custom Actions Swarm Action Development // action.yml name : 'Swarm Custom Action' description : 'Custom swarm-powered action' inputs : task : description : 'Task for swarm' required : true runs : using : 'node16' main : 'dist$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 1. PR Validation Swarm name : PR Validation Swarm on : 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 name : Intelligent Release on : 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 name : Auto Documentation on : 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
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