Imported from jeremylongshore/tons-of-skills-marketplace (plugins/saas-packs/adobe-pack/skills/adobe-observability/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill adobe-observability. Copyright stays with the author (MIT).

Adobe Observability

Overview

Set up comprehensive observability for Adobe API integrations covering four pillars: metrics (Prometheus), traces (OpenTelemetry), logs (structured JSON), and alerts. Each Adobe API has different latency profiles requiring specific monitoring.

Prerequisites

• Prometheus or compatible metrics backend
• OpenTelemetry SDK (@opentelemetry/api)
• Grafana or similar dashboarding tool
• AlertManager or PagerDuty for alerts

Instructions

Step 1: Define Key Metrics by API

Step 2: Instrumented Adobe Client

import { Counter, Histogram, Gauge, Registry } from 'prom-client';

const registry = new Registry();

const apiRequests = new Counter({
  name: 'adobe_api_requests_total',
  help: 'Total Adobe API requests',
  labelNames: ['api', 'operation', 'status'] as const,
  registers: [registry],
});

const apiDuration = new Histogram({
  name: 'adobe_api_duration_seconds',
  help: 'Adobe API request duration in seconds',
  labelNames: ['api', 'operation'] as const,
  buckets: [0.5, 1, 2, 5, 10, 20, 30, 60], // Adobe APIs are slow
  registers: [registry],
});

const apiErrors = new Counter({
  name: 'adobe_api_errors_total',
  help: 'Adobe API errors by code',
  labelNames: ['api', 'error_code'] as const,
  registers: [registry],
});

export async function instrumentedAdobeCall<T>(
  api: string,
  operation: string,
  fn: () => Promise<T>
): Promise<T> {
  const timer = apiDuration.startTimer({ api, operation });
  try {
    const result = await fn();
    apiRequests.inc({ api, operation, status: 'success' });
    return result;
  } catch (error: any) {
    const errorCode = error.status || error.httpStatus || 'unknown';
    apiRequests.inc({ api, operation, status: 'error' });
    apiErrors.inc({ api, error_code: String(errorCode) });
    throw error;
  } finally {
    timer();
  }
}

// Usage
const image = await instrumentedAdobeCall('firefly', 'generate', () =>
  generateImage({ prompt: 'sunset landscape' })
);

Step 3: OpenTelemetry Distributed Tracing

import { trace, SpanStatusCode } from '@opentelemetry/api';

const tracer = trace.getTracer('adobe-integration');

export async function tracedAdobeCall<T>(
  api: string,
  operation: string,
  fn: () => Promise<T>
): Promise<T> {
  return tracer.startActiveSpan(`adobe.${api}.${operation}`, async (span) => {
    span.setAttribute('adobe.api', api);
    span.setAttribute('adobe.operation', operation);
    span.setAttribute('adobe.client_id', process.env.ADOBE_CLIENT_ID!);

    try {
      const result = await fn();
      span.setStatus({ code: SpanStatusCode.OK });
      return result;
    } catch (error: any) {
      span.setStatus({ code: SpanStatusCode.ERROR, message: error.message });
      span.setAttribute('adobe.error_code', error.status || 'unknown');
      span.recordException(error);
      throw error;
    } finally {
      span.end();
    }
  });
}

Step 4: Structured Logging

import pino from 'pino';

const logger = pino({
  name: 'adobe',
  level: process.env.LOG_LEVEL || 'info',
  redact: ['clientSecret', 'accessToken', 'req.headers.authorization'],
});

export function logAdobeOperation(entry: {
  api: string;
  operation: string;
  durationMs: number;
  status: 'success' | 'error';
  httpStatus?: number;
  jobId?: string;
  error?: string;
}) {
  if (entry.status === 'error') {
    logger.error(entry, `Adobe ${entry.api}.${entry.operation} failed`);
  } else {
    logger.info(entry, `Adobe ${entry.api}.${entry.operation} completed`);
  }
}

Step 5: Alert Rules

# prometheus/adobe-alerts.yml
groups:
  - name: adobe_alerts
    rules:
      - alert: AdobeAuthFailure
        expr: increase(adobe_api_errors_total{error_code="401"}[5m]) > 0
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "Adobe authentication failure — credentials may be expired or revoked"

      - alert: AdobeRateLimited
        expr: rate(adobe_api_errors_total{error_code="429"}[5m]) > 0.1
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Adobe API rate limited — reduce throughput or upgrade tier"

      - alert: AdobeHighLatency
        expr: |
          histogram_quantile(0.95,
            rate(adobe_api_duration_seconds_bucket{api="firefly"}[5m])
          ) > 30
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "Adobe Firefly P95 latency > 30s"

      - alert: AdobeApiDown
        expr: |
          rate(adobe_api_errors_total{error_code=~"5.."}[5m]) /
          rate(adobe_api_requests_total[5m]) > 0.1
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Adobe API server error rate > 10%"

      - alert: AdobePdfQuotaLow
        expr: adobe_pdf_transactions_used > 450
        labels:
          severity: warning
        annotations:
          summary: "PDF Services: < 50 free tier transactions remaining"

Metrics Endpoint

app.get('/metrics', async (req, res) => {
  res.set('Content-Type', registry.contentType);
  res.send(await registry.metrics());
});

Output

• Prometheus metrics for all Adobe API calls (latency, errors, rate limits)
• OpenTelemetry traces with Adobe-specific span attributes
• Structured JSON logging with credential redaction
• Alert rules for auth failures, rate limiting, latency, and quota

Error Handling

Examples

Start with the smallest applicable command or code example already provided in this guide, using a non-production Adobe environment and credentials. Confirm the documented response or validation result before applying the pattern to production.

Resources

• Prometheus Best Practices
• OpenTelemetry Node.js
• Adobe Status Page

Next Steps

For incident response, see adobe-incident-runbook.