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".
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/customerio-deploy-pipeline/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How customerio-deploy-pipeline Compares
| Feature / Agent | customerio-deploy-pipeline | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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.
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.Related Skills
vertex-ai-pipeline-creator
Vertex Ai Pipeline Creator - Auto-activating skill for GCP Skills. Triggers on: vertex ai pipeline creator, vertex ai pipeline creator Part of the GCP Skills skill category.
vertex-ai-deployer
Vertex Ai Deployer - Auto-activating skill for ML Deployment. Triggers on: vertex ai deployer, vertex ai deployer Part of the ML Deployment skill category.
sklearn-pipeline-builder
Sklearn Pipeline Builder - Auto-activating skill for ML Training. Triggers on: sklearn pipeline builder, sklearn pipeline builder Part of the ML Training skill category.
sagemaker-endpoint-deployer
Sagemaker Endpoint Deployer - Auto-activating skill for ML Deployment. Triggers on: sagemaker endpoint deployer, sagemaker endpoint deployer Part of the ML Deployment skill category.
preprocessing-data-with-automated-pipelines
Process automate data cleaning, transformation, and validation for ML tasks. Use when requesting "preprocess data", "clean data", "ETL pipeline", or "data transformation". Trigger with relevant phrases based on skill purpose.
pipeline-monitoring-setup
Pipeline Monitoring Setup - Auto-activating skill for Data Pipelines. Triggers on: pipeline monitoring setup, pipeline monitoring setup Part of the Data Pipelines skill category.
orchestrating-deployment-pipelines
Deploy use when you need to work with deployment and CI/CD. This skill provides deployment automation and orchestration with comprehensive guidance and automation. Trigger with phrases like "deploy application", "create pipeline", or "automate deployment".
deploying-monitoring-stacks
This skill deploys monitoring stacks, including Prometheus, Grafana, and Datadog. It is used when the user needs to set up or configure monitoring infrastructure for applications or systems. The skill generates production-ready configurations, implements best practices, and supports multi-platform deployments. Use this when the user explicitly requests to deploy a monitoring stack, or mentions Prometheus, Grafana, or Datadog in the context of infrastructure setup.
deploying-machine-learning-models
This skill enables Claude to deploy machine learning models to production environments. It automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. Use this skill when the user requests to deploy a model, serve a model via an API, or put a trained model into a production environment. The skill is triggered by requests containing terms like "deploy model," "productionize model," "serve model," or "model deployment."
managing-deployment-rollbacks
Deploy use when you need to work with deployment and CI/CD. This skill provides deployment automation and orchestration with comprehensive guidance and automation. Trigger with phrases like "deploy application", "create pipeline", or "automate deployment".
kubernetes-deployment-creator
Kubernetes Deployment Creator - Auto-activating skill for DevOps Advanced. Triggers on: kubernetes deployment creator, kubernetes deployment creator Part of the DevOps Advanced skill category.
jenkins-pipeline-intro
Jenkins Pipeline Intro - Auto-activating skill for DevOps Basics. Triggers on: jenkins pipeline intro, jenkins pipeline intro Part of the DevOps Basics skill category.