fireflies-observability
Set up comprehensive observability for Fireflies.ai integrations with metrics, traces, and alerts. Use when implementing monitoring for Fireflies.ai operations, setting up dashboards, or configuring alerting for Fireflies.ai integration health. Trigger with phrases like "fireflies monitoring", "fireflies metrics", "fireflies observability", "monitor fireflies", "fireflies alerts", "fireflies tracing".
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
skills listSkill Instructions
name: fireflies-observability description: | Set up comprehensive observability for Fireflies.ai integrations with metrics, traces, and alerts. Use when implementing monitoring for Fireflies.ai operations, setting up dashboards, or configuring alerting for Fireflies.ai integration health. Trigger with phrases like "fireflies monitoring", "fireflies metrics", "fireflies observability", "monitor fireflies", "fireflies alerts", "fireflies tracing". allowed-tools: Read, Write, Edit version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io
Fireflies.ai Observability
Overview
Set up comprehensive observability for Fireflies.ai integrations.
Prerequisites
- Prometheus or compatible metrics backend
- OpenTelemetry SDK installed
- Grafana or similar dashboarding tool
- AlertManager configured
Metrics Collection
Key Metrics
| Metric | Type | Description |
|---|---|---|
fireflies_requests_total | Counter | Total API requests |
fireflies_request_duration_seconds | Histogram | Request latency |
fireflies_errors_total | Counter | Error count by type |
fireflies_rate_limit_remaining | Gauge | Rate limit headroom |
Prometheus Metrics
import { Registry, Counter, Histogram, Gauge } from 'prom-client';
const registry = new Registry();
const requestCounter = new Counter({
name: 'fireflies_requests_total',
help: 'Total Fireflies.ai API requests',
labelNames: ['method', 'status'],
registers: [registry],
});
const requestDuration = new Histogram({
name: 'fireflies_request_duration_seconds',
help: 'Fireflies.ai request duration',
labelNames: ['method'],
buckets: [0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
registers: [registry],
});
const errorCounter = new Counter({
name: 'fireflies_errors_total',
help: 'Fireflies.ai 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('fireflies-client');
async function tracedFireflies.aiCall<T>(
operationName: string,
operation: () => Promise<T>
): Promise<T> {
return tracer.startActiveSpan(`fireflies.${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: 'fireflies',
level: process.env.LOG_LEVEL || 'info',
});
function logFireflies.aiOperation(
operation: string,
data: Record<string, any>,
duration: number
) {
logger.info({
service: 'fireflies',
operation,
duration_ms: duration,
...data,
});
}
Alert Configuration
Prometheus AlertManager Rules
# fireflies_alerts.yaml
groups:
- name: fireflies_alerts
rules:
- alert: Fireflies.aiHighErrorRate
expr: |
rate(fireflies_errors_total[5m]) /
rate(fireflies_requests_total[5m]) > 0.05
for: 5m
labels:
severity: warning
annotations:
summary: "Fireflies.ai error rate > 5%"
- alert: Fireflies.aiHighLatency
expr: |
histogram_quantile(0.95,
rate(fireflies_request_duration_seconds_bucket[5m])
) > 2
for: 5m
labels:
severity: warning
annotations:
summary: "Fireflies.ai P95 latency > 2s"
- alert: Fireflies.aiDown
expr: up{job="fireflies"} == 0
for: 1m
labels:
severity: critical
annotations:
summary: "Fireflies.ai integration is down"
Dashboard
Grafana Panel Queries
{
"panels": [
{
"title": "Fireflies.ai Request Rate",
"targets": [{
"expr": "rate(fireflies_requests_total[5m])"
}]
},
{
"title": "Fireflies.ai Latency P50/P95/P99",
"targets": [{
"expr": "histogram_quantile(0.5, rate(fireflies_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 fireflies-incident-runbook.
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