hubspot-observability
Set up comprehensive observability for HubSpot integrations with metrics, traces, and alerts. Use when implementing monitoring for HubSpot operations, setting up dashboards, or configuring alerting for HubSpot integration health. Trigger with phrases like "hubspot monitoring", "hubspot metrics", "hubspot observability", "monitor hubspot", "hubspot alerts", "hubspot tracing".
Allowed Tools
Provided by Plugin
hubspot-pack
Claude Code skill pack for HubSpot (30 skills)
Installation
This skill is included in the hubspot-pack plugin:
/plugin install hubspot-pack@claude-code-plugins-plus
Click to copy
Instructions
HubSpot Observability
Overview
Set up comprehensive observability for HubSpot integrations.
Prerequisites
- Prometheus or compatible metrics backend
- OpenTelemetry SDK installed
- Grafana or similar dashboarding tool
- AlertManager configured
Metrics Collection
Key Metrics
| Metric | Type | Description |
|---|---|---|
hubspotrequeststotal |
Counter | Total API requests |
hubspotrequestduration_seconds |
Histogram | Request latency |
hubspoterrorstotal |
Counter | Error count by type |
hubspotratelimit_remaining |
Gauge | Rate limit headroom |
Prometheus Metrics
import { Registry, Counter, Histogram, Gauge } from 'prom-client';
const registry = new Registry();
const requestCounter = new Counter({
name: 'hubspot_requests_total',
help: 'Total HubSpot API requests',
labelNames: ['method', 'status'],
registers: [registry],
});
const requestDuration = new Histogram({
name: 'hubspot_request_duration_seconds',
help: 'HubSpot request duration',
labelNames: ['method'],
buckets: [0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
registers: [registry],
});
const errorCounter = new Counter({
name: 'hubspot_errors_total',
help: 'HubSpot errors by type',
labelNames: ['error_type'],
registers: [registry],
});
Instrumented Client
async function instrumentedRequest<T>(
method: string,
operation: () => Promise<T>
): Promise<T> {
const timer = requestDuration.startTimer({ method });
try {
const result = await operation();
requestCounter.inc({ method, status: 'success' });
return result;
} catch (error: any) {
requestCounter.inc({ method, status: 'error' });
errorCounter.inc({ error_type: error.code || 'unknown' });
throw error;
} finally {
timer();
}
}
Distributed Tracing
OpenTelemetry Setup
import { trace, SpanStatusCode } from '@opentelemetry/api';
const tracer = trace.getTracer('hubspot-client');
async function tracedHubSpotCall<T>(
operationName: string,
operation: () => Promise<T>
): Promise<T> {
return tracer.startActiveSpan(`hubspot.${operationName}`, async (span) => {
try {
const result = await operation();
span.setStatus({ code: SpanStatusCode.OK });
return result;
} catch (error: any) {
span.setStatus({ code: SpanStatusCode.ERROR, message: error.message });
span.recordException(error);
throw error;
} finally {
span.end();
}
});
}
Logging Strategy
Structured Logging
import pino from 'pino';
const logger = pino({
name: 'hubspot',
level: process.env.LOG_LEVEL || 'info',
});
function logHubSpotOperation(
operation: string,
data: Record<string, any>,
duration: number
) {
logger.info({
service: 'hubspot',
operation,
duration_ms: duration,
...data,
});
}
Alert Configuration
Prometheus AlertManager Rules
# hubspot_alerts.yaml
groups:
- name: hubspot_alerts
rules:
- alert: HubSpotHighErrorRate
expr: |
rate(hubspot_errors_total[5m]) /
rate(hubspot_requests_total[5m]) > 0.05
for: 5m
labels:
severity: warning
annotations:
summary: "HubSpot error rate > 5%"
- alert: HubSpotHighLatency
expr: |
histogram_quantile(0.95,
rate(hubspot_request_duration_seconds_bucket[5m])
) > 2
for: 5m
labels:
severity: warning
annotations:
summary: "HubSpot P95 latency > 2s"
- alert: HubSpotDown
expr: up{job="hubspot"} == 0
for: 1m
labels:
severity: critical
annotations:
summary: "HubSpot integration is down"
Dashboard
Grafana Panel Queries
{
"panels": [
{
"title": "HubSpot Request Rate",
"targets": [{
"expr": "rate(hubspot_requests_total[5m])"
}]
},
{
"title": "HubSpot Latency P50/P95/P99",
"targets": [{
"expr": "histogram_quantile(0.5, rate(hubspot_request_duration_seconds_bucket[5m]))"
}]
}
]
}
Instructions
Step 1: Set Up Metrics Collection
Implement Prometheus counters, histograms, and gauges for key operations.
Step 2: Add Distributed Tracing
Integrate OpenTelemetry for end-to-end request tracing.
Step 3: Configure Structured Logging
Set up JSON logging with consistent field names.
Step 4: Create Alert Rules
Define Prometheus alerting rules for error rates and latency.
Output
- Metrics collection enabled
- Distributed tracing configured
- Structured logging implemented
- Alert rules deployed
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Missing metrics | No instrumentation | Wrap client calls |
| Trace gaps | Missing propagation | Check context headers |
| Alert storms | Wrong thresholds | Tune alert rules |
| High cardinality | Too many labels | Reduce label values |
Examples
Quick Metrics Endpoint
app.get('/metrics', async (req, res) => {
res.set('Content-Type', registry.contentType);
res.send(await registry.metrics());
});
Resources
Next Steps
For incident response, see hubspot-incident-runbook.