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fireflies-observability

Name: fireflies-observability
Rating: 78 (1 reviews)
Author: jeremylongshore

tessl install github:jeremylongshore/claude-code-plugins-plus-skills --skill fireflies-observability

github.com/jeremylongshore/claude-code-plugins-plus-skills

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".

Review Score

78%

Validation Score

12/16

Implementation Score

65%

Activation Score

90%

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.