adobe-observability
Set up comprehensive observability for Adobe integrations with metrics, traces, and alerts. Use when implementing monitoring for Adobe operations, setting up dashboards, or configuring alerting for Adobe integration health. Trigger with phrases like "adobe monitoring", "adobe metrics", "adobe observability", "monitor adobe", "adobe alerts", "adobe tracing".
Allowed Tools
Provided by Plugin
adobe-pack
Claude Code skill pack for Adobe (30 skills)
Installation
This skill is included in the adobe-pack plugin:
/plugin install adobe-pack@claude-code-plugins-plus
Click to copy
Instructions
Adobe Observability
Overview
Set up comprehensive observability for Adobe integrations.
Prerequisites
- Prometheus or compatible metrics backend
- OpenTelemetry SDK installed
- Grafana or similar dashboarding tool
- AlertManager configured
Metrics Collection
Key Metrics
| Metric | Type | Description |
|---|---|---|
adoberequeststotal |
Counter | Total API requests |
adoberequestduration_seconds |
Histogram | Request latency |
adobeerrorstotal |
Counter | Error count by type |
adoberatelimit_remaining |
Gauge | Rate limit headroom |
Prometheus Metrics
import { Registry, Counter, Histogram, Gauge } from 'prom-client';
const registry = new Registry();
const requestCounter = new Counter({
name: 'adobe_requests_total',
help: 'Total Adobe API requests',
labelNames: ['method', 'status'],
registers: [registry],
});
const requestDuration = new Histogram({
name: 'adobe_request_duration_seconds',
help: 'Adobe request duration',
labelNames: ['method'],
buckets: [0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
registers: [registry],
});
const errorCounter = new Counter({
name: 'adobe_errors_total',
help: 'Adobe 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('adobe-client');
async function tracedAdobeCall<T>(
operationName: string,
operation: () => Promise<T>
): Promise<T> {
return tracer.startActiveSpan(`adobe.${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: 'adobe',
level: process.env.LOG_LEVEL || 'info',
});
function logAdobeOperation(
operation: string,
data: Record<string, any>,
duration: number
) {
logger.info({
service: 'adobe',
operation,
duration_ms: duration,
...data,
});
}
Alert Configuration
Prometheus AlertManager Rules
# adobe_alerts.yaml
groups:
- name: adobe_alerts
rules:
- alert: AdobeHighErrorRate
expr: |
rate(adobe_errors_total[5m]) /
rate(adobe_requests_total[5m]) > 0.05
for: 5m
labels:
severity: warning
annotations:
summary: "Adobe error rate > 5%"
- alert: AdobeHighLatency
expr: |
histogram_quantile(0.95,
rate(adobe_request_duration_seconds_bucket[5m])
) > 2
for: 5m
labels:
severity: warning
annotations:
summary: "Adobe P95 latency > 2s"
- alert: AdobeDown
expr: up{job="adobe"} == 0
for: 1m
labels:
severity: critical
annotations:
summary: "Adobe integration is down"
Dashboard
Grafana Panel Queries
{
"panels": [
{
"title": "Adobe Request Rate",
"targets": [{
"expr": "rate(adobe_requests_total[5m])"
}]
},
{
"title": "Adobe Latency P50/P95/P99",
"targets": [{
"expr": "histogram_quantile(0.5, rate(adobe_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 adobe-incident-runbook.