evernote-observability
Implement observability for Evernote integrations. Use when setting up monitoring, logging, tracing, or alerting for Evernote applications. Trigger with phrases like "evernote monitoring", "evernote logging", "evernote metrics", "evernote observability".
Best use case
evernote-observability is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Implement observability for Evernote integrations. Use when setting up monitoring, logging, tracing, or alerting for Evernote applications. Trigger with phrases like "evernote monitoring", "evernote logging", "evernote metrics", "evernote observability".
Teams using evernote-observability should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/evernote-observability/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How evernote-observability Compares
| Feature / Agent | evernote-observability | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Implement observability for Evernote integrations. Use when setting up monitoring, logging, tracing, or alerting for Evernote applications. Trigger with phrases like "evernote monitoring", "evernote logging", "evernote metrics", "evernote observability".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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).
```javascript
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.
```javascript
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%.
```yaml
# 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](references/implementation-guide.md).
## 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
- [Prometheus](https://prometheus.io/docs/)
- [OpenTelemetry Node.js](https://opentelemetry.io/docs/languages/js/)
- [Grafana](https://grafana.com/docs/)
- [prom-client npm](https://www.npmjs.com/package/prom-client)
## 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.Related Skills
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