customerio-deploy-pipeline

Deploy Customer.io integrations to production cloud platforms. Use when deploying to Cloud Run, Vercel, AWS Lambda, or Kubernetes with proper secrets management and health checks. Trigger: "deploy customer.io", "customer.io cloud run", "customer.io kubernetes", "customer.io lambda", "customer.io vercel".

1,868 stars

Best use case

customerio-deploy-pipeline is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Deploy Customer.io integrations to production cloud platforms. Use when deploying to Cloud Run, Vercel, AWS Lambda, or Kubernetes with proper secrets management and health checks. Trigger: "deploy customer.io", "customer.io cloud run", "customer.io kubernetes", "customer.io lambda", "customer.io vercel".

Teams using customerio-deploy-pipeline should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/customerio-deploy-pipeline/SKILL.md --create-dirs "https://raw.githubusercontent.com/jeremylongshore/claude-code-plugins-plus-skills/main/plugins/saas-packs/customerio-pack/skills/customerio-deploy-pipeline/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/customerio-deploy-pipeline/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How customerio-deploy-pipeline Compares

Feature / Agentcustomerio-deploy-pipelineStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Deploy Customer.io integrations to production cloud platforms. Use when deploying to Cloud Run, Vercel, AWS Lambda, or Kubernetes with proper secrets management and health checks. Trigger: "deploy customer.io", "customer.io cloud run", "customer.io kubernetes", "customer.io lambda", "customer.io vercel".

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Customer.io Deploy Pipeline

## Overview

Deploy Customer.io integrations to production: GCP Cloud Run with Secret Manager, Vercel serverless functions, AWS Lambda with SSM, Kubernetes with external secrets, plus health check endpoints and blue-green deployment scripts.

## Prerequisites

- CI/CD pipeline configured (see `customerio-ci-integration`)
- Cloud platform credentials and access
- Production Customer.io credentials in a secrets manager

## Instructions

### Step 1: Deploy to Google Cloud Run

```yaml
# .github/workflows/deploy-cloud-run.yml
name: Deploy to Cloud Run
on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      id-token: write  # Required for Workload Identity Federation
    steps:
      - uses: actions/checkout@v4

      - id: auth
        uses: google-github-actions/auth@v2
        with:
          workload_identity_provider: ${{ secrets.WIF_PROVIDER }}
          service_account: ${{ secrets.WIF_SA }}

      - uses: google-github-actions/setup-gcloud@v2

      - name: Build and push
        run: |
          gcloud builds submit --tag gcr.io/${{ secrets.GCP_PROJECT }}/cio-service

      - name: Deploy
        run: |
          gcloud run deploy cio-service \
            --image gcr.io/${{ secrets.GCP_PROJECT }}/cio-service \
            --region us-central1 \
            --set-secrets "CUSTOMERIO_SITE_ID=cio-site-id:latest,\
              CUSTOMERIO_TRACK_API_KEY=cio-track-key:latest,\
              CUSTOMERIO_APP_API_KEY=cio-app-key:latest" \
            --set-env-vars "CUSTOMERIO_REGION=us,NODE_ENV=production" \
            --min-instances 1 \
            --max-instances 10 \
            --memory 512Mi \
            --cpu 1 \
            --allow-unauthenticated
```

### Step 2: Health Check Endpoint

```typescript
// routes/health.ts
import { TrackClient, RegionUS } from "customerio-node";
import { Router } from "express";

const router = Router();

router.get("/health", async (_req, res) => {
  const checks: Record<string, { status: string; latency_ms?: number }> = {};

  // Check Track API
  const cio = new TrackClient(
    process.env.CUSTOMERIO_SITE_ID!,
    process.env.CUSTOMERIO_TRACK_API_KEY!,
    { region: RegionUS }
  );

  const start = Date.now();
  try {
    await cio.identify("health-check", {
      email: "health@internal.example.com",
      _health_check: true,
    });
    checks.track_api = { status: "ok", latency_ms: Date.now() - start };
  } catch (err: any) {
    checks.track_api = { status: `error: ${err.statusCode}` };
  }

  const allOk = Object.values(checks).every((c) => c.status === "ok");

  res.status(allOk ? 200 : 503).json({
    status: allOk ? "healthy" : "degraded",
    checks,
    version: process.env.npm_package_version ?? "unknown",
    uptime_seconds: Math.floor(process.uptime()),
    timestamp: new Date().toISOString(),
  });
});

export default router;
```

### Step 3: Vercel Serverless Functions

```typescript
// api/customerio/identify.ts (Vercel serverless function)
import type { VercelRequest, VercelResponse } from "@vercel/node";
import { TrackClient, RegionUS } from "customerio-node";

const cio = new TrackClient(
  process.env.CUSTOMERIO_SITE_ID!,
  process.env.CUSTOMERIO_TRACK_API_KEY!,
  { region: RegionUS }
);

export default async function handler(req: VercelRequest, res: VercelResponse) {
  if (req.method !== "POST") {
    return res.status(405).json({ error: "Method not allowed" });
  }

  const { userId, attributes } = req.body;
  if (!userId || !attributes?.email) {
    return res.status(400).json({ error: "userId and attributes.email required" });
  }

  try {
    await cio.identify(userId, {
      ...attributes,
      last_seen_at: Math.floor(Date.now() / 1000),
    });
    return res.status(200).json({ success: true });
  } catch (err: any) {
    return res.status(err.statusCode ?? 500).json({ error: err.message });
  }
}
```

### Step 4: Kubernetes Deployment

```yaml
# k8s/customerio-service.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: customerio-service
spec:
  replicas: 2
  selector:
    matchLabels:
      app: customerio-service
  template:
    metadata:
      labels:
        app: customerio-service
    spec:
      containers:
        - name: app
          image: gcr.io/my-project/cio-service:latest
          ports:
            - containerPort: 3000
          env:
            - name: CUSTOMERIO_SITE_ID
              valueFrom:
                secretKeyRef:
                  name: customerio-secrets
                  key: site-id
            - name: CUSTOMERIO_TRACK_API_KEY
              valueFrom:
                secretKeyRef:
                  name: customerio-secrets
                  key: track-api-key
            - name: CUSTOMERIO_APP_API_KEY
              valueFrom:
                secretKeyRef:
                  name: customerio-secrets
                  key: app-api-key
            - name: CUSTOMERIO_REGION
              value: "us"
            - name: NODE_ENV
              value: "production"
          resources:
            requests:
              cpu: 100m
              memory: 256Mi
            limits:
              cpu: 500m
              memory: 512Mi
          readinessProbe:
            httpGet:
              path: /health
              port: 3000
            initialDelaySeconds: 5
            periodSeconds: 10
          livenessProbe:
            httpGet:
              path: /health
              port: 3000
            initialDelaySeconds: 15
            periodSeconds: 30
---
apiVersion: v1
kind: Service
metadata:
  name: customerio-service
spec:
  selector:
    app: customerio-service
  ports:
    - port: 80
      targetPort: 3000
```

### Step 5: Blue-Green Deployment

```bash
#!/usr/bin/env bash
set -euo pipefail
# scripts/blue-green-deploy.sh

SERVICE="cio-service"
REGION="us-central1"
IMAGE="gcr.io/${GCP_PROJECT}/${SERVICE}:${COMMIT_SHA}"

echo "=== Blue-Green Deploy: ${SERVICE} ==="

# 1. Deploy with no traffic
gcloud run deploy "${SERVICE}" \
  --image "${IMAGE}" \
  --region "${REGION}" \
  --no-traffic \
  --tag "canary"

echo "Deployed canary. Running health check..."

# 2. Health check on canary
CANARY_URL=$(gcloud run services describe "${SERVICE}" \
  --region "${REGION}" --format 'value(status.url)' \
  | sed 's|https://|https://canary---|')

HEALTH=$(curl -s -o /dev/null -w "%{http_code}" "${CANARY_URL}/health")
if [ "${HEALTH}" != "200" ]; then
  echo "FAIL: Health check returned ${HEALTH}. Aborting."
  exit 1
fi

# 3. Shift traffic: 10% → 50% → 100%
for pct in 10 50 100; do
  echo "Shifting ${pct}% traffic to canary..."
  gcloud run services update-traffic "${SERVICE}" \
    --region "${REGION}" \
    --to-tags "canary=${pct}"
  sleep 30
done

echo "Deploy complete. 100% traffic on new revision."
```

## Deployment Checklist

- [ ] Production secrets in secrets manager (not env files)
- [ ] Health check endpoint responds 200
- [ ] Readiness and liveness probes configured
- [ ] Resource limits set (CPU, memory)
- [ ] Min instances > 0 (avoid cold starts)
- [ ] Blue-green or canary deployment configured
- [ ] Rollback procedure documented
- [ ] Post-deploy smoke test automated

## Error Handling

| Issue | Solution |
|-------|----------|
| Secret not found | Verify secret name in secrets manager |
| Health check timeout | Increase initialDelaySeconds, check CIO connectivity |
| Cold start latency | Set `--min-instances 1` (Cloud Run) or keep-alive |
| Memory OOM | Increase memory limits, check for event queue buildup |

## Resources

- [Cloud Run Documentation](https://cloud.google.com/run/docs)
- [Vercel Serverless Functions](https://vercel.com/docs/functions)
- [Kubernetes Secrets](https://kubernetes.io/docs/concepts/configuration/secret/)

## Next Steps

After deployment, proceed to `customerio-webhooks-events` for webhook handling.

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