Skip to main content ホーム クリエイター comeonoliver skillshub customerio-observability
customerio-observability Set up Customer.io monitoring and observability.
Use when implementing metrics, structured logging, alerting,
or Grafana dashboards for Customer.io integrations.
Trigger: "customer.io monitoring", "customer.io metrics",
"customer.io dashboard", "customer.io alerts", "customer.io observability".
インストールへ移動 Skills Marketplace コミュニティが作成したAIスキルを発見・探索
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/ComeOnOliver/skillshub --skill customerio-observabilityコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Zipをダウンロード ダウンロード中... このリポジトリの他の Skills Review product and feature risk before an AI coding agent starts implementation.
Use Xquik for X data and confirmation-gated X actions: tweet search, user lookup, follower export, media download, monitors, webhooks, MCP, and SDK workflows.
Canton Network open-source ecosystem guide covering DAML SDK, Canton runtime, and Splice applications. Use when working with Canton Network, DAML smart contracts, or building decentralized applications.
name customerio-observability description Set up Customer.io monitoring and observability.
Use when implementing metrics, structured logging, alerting,
or Grafana dashboards for Customer.io integrations.
Trigger: "customer.io monitoring", "customer.io metrics",
"customer.io dashboard", "customer.io alerts", "customer.io observability".
allowed-tools Read, Write, Edit, Bash(npm:*), Bash(npx:*), Glob, Grep version 1.0.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> compatible-with claude-code, codex, openclaw tags ["saas","customer-io","monitoring","observability","prometheus"]
Customer.io Observability
Overview
Implement comprehensive observability for Customer.io integrations: Prometheus metrics (latency, error rates, delivery funnel), structured JSON logging with PII redaction, OpenTelemetry tracing, and Grafana dashboard definitions.
Prerequisites
Customer.io integration deployed
Prometheus + Grafana (or compatible metrics stack)
Structured logging system (pino recommended)
Key Metrics to Track
Metric Type Description Alert Threshold cio_api_duration_msHistogram API call latency p99 > 5000ms cio_api_requests_totalCounter Total API requests by operation N/A (rate) cio_api_errors_totalCounter API errors by status code > 1% error rate cio_email_sent_totalCounter Transactional + campaign emails N/A cio_email_bounced_totalCounter Bounce count > 5% of sends cio_email_complained_totalCounter Spam complaints > 0.1% of sends cio_webhook_received_totalCounter Webhook events by metric type N/A cio_queue_depthGauge Pending items in event queue > 10K
Instructions
Step 1: Prometheus Metrics
import { Counter , Histogram , Gauge , Registry } from "prom-client" ;
const registry = new Registry ();
export
const
apiDuration
new
Histogram
name
"cio_api_duration_ms"
help
"Customer.io API call duration in milliseconds"
labelNames
"operation"
"status"
as
const
buckets
10
25
50
100
250
500
1000
2500
5000
registers
apiRequests
new
Counter
name
"cio_api_requests_total"
help
"Total Customer.io API requests"
labelNames
"operation"
as
const
registers
apiErrors
new
Counter
name
"cio_api_errors_total"
help
"Customer.io API errors"
labelNames
"operation"
"status_code"
as
const
registers
emailSent
new
Counter
name
"cio_email_sent_total"
help
"Emails sent via Customer.io"
labelNames
"type"
as
const
registers
emailBounced
new
Counter
name
"cio_email_bounced_total"
help
"Email bounces from Customer.io webhooks"
registers
emailComplained
new
Counter
name
"cio_email_complained_total"
help
"Spam complaints from Customer.io webhooks"
registers
webhookReceived
new
Counter
name
"cio_webhook_received_total"
help
"Webhook events received"
labelNames
"metric"
as
const
registers
queueDepth
new
Gauge
name
"cio_queue_depth"
help
"Pending items in Customer.io event queue"
labelNames
"queue"
as
const
registers
export
Step 2: Instrumented Client
import { TrackClient , APIClient , SendEmailRequest , RegionUS } from "customerio-node" ;
import { cioMetrics } from "./customerio-metrics" ;
export class InstrumentedCioClient {
private track : TrackClient ;
private app : APIClient ;
constructor (siteId : string , trackKey : string , appKey : string this .track = new TrackClient (siteId, trackKey, { region : RegionUS });
this .app = new APIClient (appKey, { region : RegionUS });
}
async identify (userId : string , attrs : Record <string , any >): Promise <void > {
const timer = cioMetrics.apiDuration .startTimer ({ operation : "identify" });
cioMetrics.apiRequests .inc ({ operation : "identify" });
try {
await this .track .identify (userId, attrs);
timer ({ status : "success" });
} catch (err : any ) {
const code = String (err.statusCode ?? "unknown" );
timer ({ status : "error" });
cioMetrics.apiErrors .inc ({ operation : "identify" , status_code : code });
throw err;
}
}
async trackEvent (
userId : string ,
name : string ,
data ?: Record <string , any >
): Promise <void > {
const timer = cioMetrics.apiDuration .startTimer ({ operation : "track" });
cioMetrics.apiRequests .inc ({ operation : "track" });
try {
await this .track .track (userId, { name, data });
timer ({ status : "success" });
} catch (err : any ) {
timer ({ status : "error" });
cioMetrics.apiErrors .inc ({
operation : "track" ,
status_code : String (err.statusCode ?? "unknown" ),
});
throw err;
}
}
async sendEmail (request : SendEmailRequest ): Promise <any > {
const timer = cioMetrics.apiDuration .startTimer ({ operation : "send_email" });
cioMetrics.apiRequests .inc ({ operation : "send_email" });
try {
const result = await this .app .sendEmail (request);
timer ({ status : "success" });
cioMetrics.emailSent .inc ({ type : "transactional" });
return result;
} catch (err : any ) {
timer ({ status : "error" });
cioMetrics.apiErrors .inc ({
operation : "send_email" ,
status_code : String (err.statusCode ?? "unknown" ),
});
throw err;
}
}
}
Step 3: Structured Logging with PII Redaction
import pino from "pino" ;
const logger = pino ({
name : "customerio" ,
level : process.env .CUSTOMERIO_LOG_LEVEL ?? "info" ,
redact : {
paths : [
"*.email" ,
"*.phone" ,
"*.ip_address" ,
"*.password" ,
"attrs.email" ,
"attrs.phone" ,
],
censor : "[REDACTED]" ,
},
});
export function logCioOperation (
operation : string ,
data : {
userId?: string ;
event?: string ;
latencyMs?: number ;
statusCode?: number ;
error?: string ;
attrs?: Record<string , any >;
}
): void {
if (data.error ) {
logger.error ({ operation, ...data }, `CIO ${operation} failed` );
} else {
logger.info ({ operation, ...data }, `CIO ${operation} completed` );
}
}
Step 4: Webhook Metrics Collection
function recordWebhookMetrics (event : { metric: string }void {
cioMetrics.webhookReceived .inc ({ metric : event.metric });
switch (event.metric ) {
case "bounced" :
cioMetrics.emailBounced .inc ();
break ;
case "spammed" :
cioMetrics.emailComplained .inc ();
break ;
case "sent" :
cioMetrics.emailSent .inc ({ type : "campaign" });
break ;
}
}
Step 5: Prometheus Metrics Endpoint
import { Router } from "express" ;
import { registry } from "../lib/customerio-metrics" ;
const router = Router ();
router.get ("/metrics" , async (_req, res) => {
res.set ("Content-Type" , registry.contentType );
res.end (await registry.metrics ());
});
export default router;
Step 6: Grafana Dashboard (JSON Model) {
"title" : "Customer.io Integration" ,
"panels" : [
{
"title" : "API Latency (p50/p95/p99)" ,
"type" : "timeseries" ,
"targets" : [
{ "expr" : "histogram_quantile(0.50, rate(cio_api_duration_ms_bucket[5m]))" } ,
{ "expr" : "histogram_quantile(0.95, rate(cio_api_duration_ms_bucket[5m]))" } ,
{ "expr" : "histogram_quantile(0.99, rate(cio_api_duration_ms_bucket[5m]))" }
]
} ,
{
"title" : "Request Rate by Operation" ,
"type" : "timeseries" ,
"targets" : [
{ "expr" : "rate(cio_api_requests_total[5m])" }
]
} ,
{
"title" : "Error Rate %" ,
"type" : "stat" ,
"targets" : [
{ "expr" : "rate(cio_api_errors_total[5m]) / rate(cio_api_requests_total[5m]) * 100" }
]
} ,
{
"title" : "Email Delivery Funnel" ,
"type" : "bargauge" ,
"targets" : [
{ "expr" : "cio_email_sent_total" } ,
{ "expr" : "cio_email_bounced_total" } ,
{ "expr" : "cio_email_complained_total" }
]
}
]
}
Step 7: Alerting Rules
groups:
- name: customerio
rules:
- alert: CioHighErrorRate
expr: rate(cio_api_errors_total[5m]) / rate(cio_api_requests_total[5m]) > 0.05
for: 5m
labels: { severity: critical }
annotations:
summary: "Customer.io API error rate > 5%"
- alert: CioHighLatency
expr: histogram_quantile(0.99, rate(cio_api_duration_ms_bucket[5m])) > 5000
for: 5m
labels: { severity: warning }
annotations:
summary: "Customer.io p99 latency > 5 seconds"
- alert: CioHighBounceRate
expr: rate(cio_email_bounced_total[1h]) / rate(cio_email_sent_total[1h]) > 0.05
for: 15m
labels: { severity: warning }
annotations:
summary: "Email bounce rate > 5%"
- alert: CioSpamComplaints
expr: rate(cio_email_complained_total[1h]) / rate(cio_email_sent_total[1h]) > 0.001
for: 5m
labels: { severity: critical }
annotations:
summary: "Spam complaint rate > 0.1% — sender reputation at risk"
Error Handling Issue Solution High cardinality metrics Don't use userId as a label — use operation + status only Log volume too high Set CUSTOMERIO_LOG_LEVEL=warn in production Missing metrics Check metric registration and scrape config PII in logs Verify pino redact paths cover all sensitive fields
Resources
Next Steps After observability setup, proceed to customerio-advanced-troubleshooting for debugging.
