cloud-run-deploy

Cloud Run Deployment Skill

Target Token Efficiency: 65% (350 tokens -> 120 tokens)

Purpose

Automate Cloud Run AI Engine deployment with free tier optimized configuration, without manual script explanation.

Trigger Keywords

• "deploy cloud run"
• "cloud run deploy"
• "deploy ai engine"
• "ai engine deploy"
• "production deploy"
• "배포"
• "클라우드 런 배포"
• "AI 엔진 배포"
• "프로덕션 배포"
• "gcp deploy"

Context

• Project: OpenManager VIBE v5.85.0
• Region: asia-northeast1 (Seoul)
• Service: ai-engine
• Tier: FREE TIER OPTIMIZED
• Free Limits: 180,000 vCPU-sec, 360,000 GB-sec, 2M requests/month

Configuration (Free Tier)

Workflow

1. Pre-deployment Check

# Check current working directory
cd /mnt/d/cursor/openmanager-vibe-v5/cloud-run/ai-engine

# Verify GCP project
gcloud config get-value project

# Check for uncommitted changes
git status --short

Expected:

• Project: openmanager-free-tier
• Clean working tree (recommended)

2. TypeScript Build Check

# Verify TypeScript compiles
npm run build --prefix /mnt/d/cursor/openmanager-vibe-v5/cloud-run/ai-engine

Expected:

• No compilation errors
• dist/ directory updated

3. Execute Deployment

# Run deployment script
cd /mnt/d/cursor/openmanager-vibe-v5/cloud-run/ai-engine && bash deploy.sh

Deployment Steps (automated by script):

1. Build container image (Cloud Build, ~2-3 min)
2. Deploy to Cloud Run (~1 min)
3. Health check
4. Cleanup old images/revisions (background)

4. Verify Deployment

# Health check
curl -s https://ai-engine-490817238363.asia-northeast1.run.app/health | jq

# Check monitoring endpoint
curl -s https://ai-engine-490817238363.asia-northeast1.run.app/monitoring | jq '.agents'

Expected Health Response:

{
  "status": "healthy",
  "version": "5.84.x",
  "agents": {
    "active": 4,
    "available": ["NLQ", "Analyst", "Reporter", "Advisor"]
  }
}

Report Summary Format

🚀 Cloud Run Deployment Results
├─ Service: ai-engine
├─ Region: asia-northeast1
├─ Version: {SHORT_SHA}
├─ URL: https://ai-engine-*.run.app
├─ Status: ✅ Success / ❌ Failed
│
├─ Resource Config (FREE TIER):
│   ├─ CPU: 1 vCPU (~50 hrs/month free)
│   ├─ Memory: 512Mi (~200 hrs/month free)
│   └─ Max Instances: 3
│
└─ Health Check: ✅ Pass / ❌ Fail

Token Optimization Strategy

Before (Manual):

User: "배포해줘"
Assistant: [reads Dockerfile, explains config, explains free tier, runs commands, checks logs]
Tokens: ~350

After (Skill):

User: "deploy cloud run"
Skill: [executes workflow, reports summary]
Tokens: ~120 (65% reduction)

Edge Cases

Case 1: Build Failure

• Check: Dockerfile syntax
• Check: npm dependencies
• Action: Review build logs in Cloud Console

Case 2: Deployment Timeout

• Default timeout: 600s for build
• Action: Check Cloud Build logs
• Verify: Image size < 500MB

Case 3: Health Check Failure

• May still be starting (cold start ~10-20s)
• Wait 30s and retry
• Check: /health endpoint logs

Case 4: Permission Denied

• Verify: gcloud auth login
• Verify: Project access rights
• Action: Re-authenticate

Rollback

# List recent revisions
gcloud run revisions list --service ai-engine --region asia-northeast1

# Rollback to previous revision
gcloud run services update-traffic ai-engine \
  --region asia-northeast1 \
  --to-revisions REVISION_NAME=100

Related Files

Success Criteria

• Build: Completed without errors
• Deploy: Service updated successfully
• Health: HTTP 200 from /health
• Agents: All 5 agents active

Related Skills

• lint-smoke - Pre-deployment code quality check
• validation-analysis - Post-deployment validation

Changelog

• 2026-01-06: v1.0.0 - Initial implementation
  • Free tier optimized configuration
  • 1 vCPU, 512Mi memory, 3 max instances
  • Integrated health check verification
  • 10 trigger keywords (Korean/English)