evernote-observability
Set up Prometheus metrics, structured logging, and health checks for Evernote API integrations.
Install
mkdir -p .claude/skills/evernote-observability && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/7448" && unzip -o skill.zip -d .claude/skills/evernote-observability && rm skill.zipInstalls to .claude/skills/evernote-observability
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Implement observability for Evernote integrations.Key capabilities
- →Track API metrics using Prometheus counters and histograms
- →Implement structured JSON logging with token redaction
- →Configure health and readiness endpoints
- →Define Prometheus alert rules for rate limits and latency
- →Wrap NoteStore with a Proxy for automatic metric recording
How it works
The skill instruments the Evernote NoteStore client using a Proxy to capture API call data, which is then recorded as Prometheus metrics and structured JSON logs.
Inputs & outputs
When to use evernote-observability
- →Setup Prometheus metrics
- →Configure API logging
- →Implement health checks
About this skill
Evernote Observability
Overview
Comprehensive observability setup for Evernote integrations: Prometheus metrics for API call tracking, structured JSON logging, OpenTelemetry tracing, health check endpoints, and alerting rules.
Prerequisites
- Monitoring infrastructure (Prometheus, Datadog, or CloudWatch)
- Log aggregation (ELK, Loki, or CloudWatch Logs)
- Alerting system (PagerDuty, Opsgenie, or Slack webhooks)
Instructions
Step 1: Metrics Collection
Track key metrics with Prometheus counters and histograms: evernote_api_calls_total (by method and status), evernote_api_duration_seconds (latency histogram), evernote_rate_limits_total (rate limit hits), evernote_quota_usage_bytes (upload quota consumption).
const { Counter, Histogram } = require('prom-client');
const apiCalls = new Counter({
name: 'evernote_api_calls_total',
help: 'Total Evernote API calls',
labelNames: ['method', 'status']
});
const apiDuration = new Histogram({
name: 'evernote_api_duration_seconds',
help: 'Evernote API call duration',
labelNames: ['method'],
buckets: [0.1, 0.5, 1, 2, 5, 10]
});
Step 2: Instrumented Client
Wrap the NoteStore with a Proxy that automatically records metrics for every API call. Increment counters on success/failure, observe latency in histograms, and count rate limit events.
Step 3: Structured Logging
Use JSON-formatted logs with consistent fields: timestamp, level, method, duration, userId (hashed), noteGuid. Redact access tokens from all log output.
function logApiCall(method, duration, error) {
const entry = {
timestamp: new Date().toISOString(),
service: 'evernote-integration',
method,
duration_ms: duration,
status: error ? 'error' : 'success',
error_code: error?.errorCode
};
console.log(JSON.stringify(entry));
}
Step 4: Health and Readiness Endpoints
Implement /health (liveness: is the process running?) and /ready (readiness: can we reach Evernote API?). Include cache connectivity check.
Step 5: Alert Rules
Configure Prometheus alerts: rate limit hits > 5 in 10 minutes, API error rate > 10%, p95 latency > 5 seconds, quota usage > 90%.
# prometheus-alerts.yml
groups:
- name: evernote
rules:
- alert: EvernoteRateLimited
expr: rate(evernote_rate_limits_total[10m]) > 0.5
for: 5m
labels: { severity: warning }
annotations:
summary: "Evernote rate limits detected"
For the complete metrics setup, Grafana dashboard JSON, tracing configuration, and alert rules, see Implementation Guide.
Output
- Prometheus metrics: API calls, latency histogram, rate limits, quota usage
- Instrumented NoteStore client with automatic metric recording
- Structured JSON logging with token redaction
- Health and readiness endpoints
- Prometheus alert rules for rate limits, errors, and latency
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Metrics endpoint not scraped | Prometheus target missing | Add service to Prometheus scrape config |
| Missing trace context | OpenTelemetry not initialized | Initialize tracer before creating Evernote client |
| Log volume too high | Logging every API call | Sample debug logs, always log errors and rate limits |
| Alert fatigue | Thresholds too low | Tune alert thresholds based on baseline metrics |
Resources
Next Steps
For incident handling, see evernote-incident-runbook.
Examples
Grafana dashboard: Display API call rate, p50/p95/p99 latency, error rate, rate limit frequency, and quota usage on a single dashboard. Set time range to last 24 hours.
Rate limit alerting: Alert on-call when rate limit hits exceed 5 per 10-minute window. Include runbook link to evernote-rate-limits in the alert annotation.
When not to use it
- →When logging raw access tokens
- →When ignoring rate limit monitoring
Prerequisites
Limitations
- →Metrics endpoint requires manual scrape configuration
- →OpenTelemetry must be initialized before the Evernote client
How it compares
Unlike manual logging, this approach provides automated, standardized observability including latency histograms and rate limit tracking.
Compared to similar skills
evernote-observability side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| evernote-observability (this skill) | 1 | 27d | Review | Intermediate |
| langfuse | 7 | 6mo | No flags | Intermediate |
| appinsights-instrumentation | 6 | 7mo | Review | Beginner |
| instantly-observability | 2 | 27d | Caution | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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