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Get Started Free →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".
.claude/skills/jeremylongshore-evernote-observability/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 32% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -11% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -6% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 21% | 0% |
Comprehensive observability setup for Evernote integrations: Prometheus metrics for API call tracking, structured JSON logging, OpenTelemetry tracing, health check endpoints, and alerting rules.
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).
javascriptconst { 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] });
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.
Use JSON-formatted logs with consistent fields: timestamp, level, method, duration, userId (hashed), noteGuid. Redact access tokens from all log output.
javascriptfunction 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)); }
Implement /health (liveness: is the process running?) and /ready (readiness: can we reach Evernote API?). Include cache connectivity check.
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.
| 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 |
For incident handling, see evernote-incident-runbook.
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.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 34,032 | 26,948 | -21% | 1 | 1 | 0% | 7,210 | 5,900 | -18% | 0 | 0 | — |
case-02 | fail→pass | 18,639 | 18,041 | -3% | 1 | 1 | 0% | 3,898 | 5,145 | +32% | 0 | 0 | — |
case-03 | fail→fail | 30,278 | 32,673 | +8% | 1 | 1 | 0% | 6,135 | 6,179 | +1% | 0 | 0 | — |
case-04 | fail→fail | 16,180 | 17,316 | +7% | 1 | 1 | 0% | 2,522 | 3,567 | +41% | 0 | 0 | — |
case-05 | fail→fail | 13,195 | 10,043 | -24% | 1 | 1 | 0% | 2,005 | 2,507 | +25% | 0 | 0 | — |
case-06 | fail→pass | 13,762 | 7,393 | -46% | 1 | 1 | 0% | 2,497 | 2,216 | -11% | 0 | 0 | — |
case-07 | fail→pass | 13,892 | 5,064 | -64% | 1 | 1 | 0% | 2,234 | 1,884 | -16% | 0 | 0 | — |
case-08 | fail→pass | 20,796 | 13,372 | -36% | 1 | 1 | 0% | 4,033 | 3,774 | -6% | 0 | 0 | — |
case-09 | pass→pass | 17,051 | 11,446 | -33% | 1 | 1 | 0% | 2,690 | 3,068 | +14% | 0 | 0 | — |
case-10 | fail→pass | 18,047 | 12,569 | -30% | 1 | 1 | 0% | 2,460 | 2,975 | +21% | 0 | 0 | — |
case-11 | pass→fail | 20,589 | 14,123 | -31% | 1 | 1 | 0% | 3,251 | 3,506 | +8% | 0 | 0 | — |
case-12 | fail→fail | 13,983 | 11,802 | -16% | 1 | 1 | 0% | 2,249 | 2,767 | +23% | 0 | 0 | — |
case-13 | pass→pass | 20,415 | 15,068 | -26% | 1 | 1 | 0% | 2,859 | 3,391 | +19% | 0 | 0 | — |
case-14 | fail→fail | 15,341 | 9,233 | -40% | 1 | 1 | 0% | 2,683 | 2,559 | -5% | 0 | 0 | — |
case-15 | fail→pass | 17,934 | 10,259 | -43% | 1 | 1 | 0% | 2,821 | 2,684 | -5% | 0 | 0 | — |
case-16 | pass→fail | 19,703 | 17,105 | -13% | 1 | 1 | 0% | 2,823 | 3,325 | +18% | 0 | 0 | — |
case-17 | pass→pass | 8,341 | 7,288 | -13% | 1 | 1 | 0% | 1,389 | 2,237 | +61% | 0 | 0 | — |
case-18 | pass→pass | 18,763 | 10,392 | -45% | 1 | 1 | 0% | 2,825 | 2,481 | -12% | 0 | 0 | — |
case-19 | pass→pass | 8,987 | 2,103 | -77% | 1 | 1 | 0% | 1,547 | 1,403 | -9% | 0 | 0 | — |
case-20 | fail→pass | 11,083 | 2,656 | -76% | 1 | 1 | 0% | 1,494 | 1,466 | -2% | 0 | 0 | — |
case-21 | pass→pass | 17,193 | 14,910 | -13% | 1 | 1 | 0% | 2,550 | 3,332 | +31% | 0 | 0 | — |
case-22 | pass→pass | 14,637 | 16,765 | +15% | 1 | 1 | 0% | 2,941 | 4,313 | +47% | 0 | 0 | — |
case-23 | pass→pass | 28,279 | 12,345 | -56% | 1 | 1 | 0% | 3,340 | 3,721 | +11% | 0 | 0 | — |
case-24 | pass→pass | 17,153 | 16,884 | -2% | 1 | 1 | 0% | 2,850 | 4,482 | +57% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +21 percentage points is the difference between those two pass rates over the 24 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.