FI

fireflies-observability

Configures observability tools like Prometheus and Grafana for Fireflies.ai API tracking.

Install

mkdir -p .claude/skills/fireflies-observability && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8849" && unzip -o skill.zip -d .claude/skills/fireflies-observability && rm skill.zip

Installs to .claude/skills/fireflies-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.

Monitor Fireflies.ai integration health with metrics, alerts, and dashboards.
77 charsno explicit “when” trigger
Advanced

Key capabilities

  • Instrument GraphQL clients for request latency and error tracking
  • Monitor webhook event processing and queue depth
  • Execute periodic health check probes
  • Track seat utilization and transcript counts per user
  • Configure Prometheus alerting rules for integration health

How it works

It instruments code with Prometheus client libraries to track API performance and webhook processing, while providing alerting rules to monitor system health.

Inputs & outputs

You give it
GraphQL client operations and webhook events
You get back
Metrics, alerts, and observability dashboards

When to use fireflies-observability

  • Track Fireflies API request latency
  • Set up alerts for webhook failures
  • Monitor transcript processing reliability
  • Visualize API seat utilization in Grafana

About this skill

Fireflies.ai Observability

Overview

Monitor Fireflies.ai integration health: API connectivity, webhook delivery, transcript processing latency, and seat utilization. Built for Prometheus/Grafana but adaptable to any metrics system.

Prerequisites

  • Fireflies Business+ plan (for full API access)
  • Prometheus + Grafana (or equivalent metrics stack)
  • Webhook endpoint deployed and receiving events

Instructions

Step 1: Instrument the GraphQL Client

// lib/fireflies-instrumented.ts
import { Counter, Histogram, Gauge } from "prom-client";

const apiRequests = new Counter({
  name: "fireflies_api_requests_total",
  help: "Total Fireflies API requests",
  labelNames: ["operation", "status"],
});

const apiLatency = new Histogram({
  name: "fireflies_api_latency_seconds",
  help: "Fireflies API request latency",
  labelNames: ["operation"],
  buckets: [0.1, 0.25, 0.5, 1, 2, 5, 10],
});

const FIREFLIES_API = "https://api.fireflies.ai/graphql";

export async function firefliesQueryInstrumented(
  operation: string,
  query: string,
  variables?: any
) {
  const timer = apiLatency.startTimer({ operation });

  try {
    const res = await fetch(FIREFLIES_API, {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Authorization: `Bearer ${process.env.FIREFLIES_API_KEY}`,
      },
      body: JSON.stringify({ query, variables }),
    });

    const json = await res.json();

    if (json.errors) {
      apiRequests.inc({ operation, status: json.errors[0].code || "error" });
      throw new Error(json.errors[0].message);
    }

    apiRequests.inc({ operation, status: "success" });
    return json.data;
  } catch (err) {
    apiRequests.inc({ operation, status: "failure" });
    throw err;
  } finally {
    timer();
  }
}

Step 2: Webhook Event Metrics

const webhookEvents = new Counter({
  name: "fireflies_webhook_events_total",
  help: "Webhook events received",
  labelNames: ["event_type", "status"],
});

const webhookProcessingTime = new Histogram({
  name: "fireflies_webhook_processing_seconds",
  help: "Time to process webhook events",
  buckets: [0.1, 0.5, 1, 5, 10, 30],
});

const transcriptQueue = new Gauge({
  name: "fireflies_transcript_queue_depth",
  help: "Number of transcripts queued for processing",
});

export async function handleWebhookWithMetrics(event: any) {
  const timer = webhookProcessingTime.startTimer();
  transcriptQueue.inc();

  try {
    await processTranscriptReady(event.meetingId);
    webhookEvents.inc({ event_type: event.eventType, status: "success" });
  } catch (err) {
    webhookEvents.inc({ event_type: event.eventType, status: "error" });
    throw err;
  } finally {
    timer();
    transcriptQueue.dec();
  }
}

Step 3: Health Check Probe

const healthStatus = new Gauge({
  name: "fireflies_health_status",
  help: "Fireflies API health (1=healthy, 0=unhealthy)",
});

// Run every 5 minutes
async function healthProbe() {
  try {
    const start = Date.now();
    const data = await firefliesQueryInstrumented("health_check", "{ user { email } }");
    const latencyMs = Date.now() - start;

    healthStatus.set(1);
    console.log(`Fireflies health: OK (${latencyMs}ms)`);
  } catch (err) {
    healthStatus.set(0);
    console.error(`Fireflies health: FAILED - ${(err as Error).message}`);
  }
}

setInterval(healthProbe, 5 * 60 * 1000);

Step 4: Seat Utilization Tracking

const seatUtilization = new Gauge({
  name: "fireflies_seat_utilization",
  help: "Transcripts per user",
  labelNames: ["user_email"],
});

const totalSeats = new Gauge({
  name: "fireflies_total_seats",
  help: "Total Fireflies seats",
});

// Run daily
async function trackSeatUtilization() {
  const data = await firefliesQueryInstrumented("seat_audit", `{
    users { email num_transcripts }
  }`);

  totalSeats.set(data.users.length);
  for (const user of data.users) {
    seatUtilization.set({ user_email: user.email }, user.num_transcripts);
  }

  const inactive = data.users.filter((u: any) => u.num_transcripts < 2);
  if (inactive.length > 3) {
    console.warn(`${inactive.length} seats with <2 transcripts -- review for cost savings`);
  }
}

Step 5: Alerting Rules

# prometheus/rules/fireflies.yml
groups:
  - name: fireflies
    rules:
      - alert: FirefliesAPIDown
        expr: fireflies_health_status == 0
        for: 10m
        labels:
          severity: critical
        annotations:
          summary: "Fireflies API unreachable for 10+ minutes"

      - alert: FirefliesHighErrorRate
        expr: rate(fireflies_api_requests_total{status!="success"}[5m]) > 0.1
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Fireflies API error rate >10% over 5 minutes"

      - alert: FirefliesRateLimited
        expr: rate(fireflies_api_requests_total{status="too_many_requests"}[5m]) > 0
        labels:
          severity: warning
        annotations:
          summary: "Fireflies API rate limiting detected"

      - alert: FirefliesWebhookBacklog
        expr: fireflies_transcript_queue_depth > 50
        for: 15m
        labels:
          severity: warning
        annotations:
          summary: "Webhook processing backlog exceeds 50 transcripts"

      - alert: FirefliesSlowProcessing
        expr: histogram_quantile(0.95, rate(fireflies_webhook_processing_seconds_bucket[1h])) > 30
        labels:
          severity: warning
        annotations:
          summary: "Webhook processing P95 exceeds 30 seconds"

Step 6: Dashboard Panels (Grafana)

Key panels to create:

  • API Health: fireflies_health_status (stat panel, green/red)
  • Request Rate: rate(fireflies_api_requests_total[5m]) by status
  • Latency P50/P95/P99: histogram_quantile on fireflies_api_latency_seconds
  • Webhook Events/Hour: increase(fireflies_webhook_events_total[1h])
  • Queue Depth: fireflies_transcript_queue_depth (gauge)
  • Seat Utilization: fireflies_seat_utilization (table, sorted ascending)

Error Handling

AlertCauseResponse
API DownFireflies outage or key revokedCheck status page, verify API key
High Error RateSchema change or auth issueInspect error codes in logs
Rate LimitedBurst of requestsEnable request queuing
Webhook BacklogProcessing bottleneckScale webhook workers

Output

  • Instrumented GraphQL client with latency and error metrics
  • Webhook event tracking with queue depth monitoring
  • Health probe running on 5-minute interval
  • Prometheus alerting rules for critical conditions

Resources

Next Steps

For incident response, see fireflies-incident-runbook.

When not to use it

  • Real-time incident response
  • Direct modification of Fireflies.ai account settings

Prerequisites

Fireflies Business+ planPrometheus and Grafana metrics stack

Limitations

  • Requires Fireflies Business+ plan for full API access
  • Alerting rules assume Prometheus/Grafana stack

How it compares

It provides pre-built instrumentation patterns and alerting rules instead of requiring manual setup of monitoring logic.

Compared to similar skills

fireflies-observability side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
fireflies-observability (this skill)027dCautionAdvanced
langfuse76moNo flagsIntermediate
appinsights-instrumentation67moReviewBeginner
instantly-observability227dCautionIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

More by jeremylongshore

View all by jeremylongshore

analyzing-logs

jeremylongshore

Analyze application logs to detect performance issues, identify error patterns, and improve stability by extracting key insights.

14123

ollama-setup

jeremylongshore

Configure auto-configure Ollama when user needs local LLM deployment, free AI alternatives, or wants to eliminate hosted API costs. Trigger phrases: "install ollama", "local AI", "free LLM", "self-hosted AI", "replace OpenAI", "no API costs". Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

1167

backtesting-trading-strategies

jeremylongshore

Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".

1071

generating-database-seed-data

jeremylongshore

Process this skill enables AI assistant to generate realistic test data and database seed scripts for development and testing environments. it uses faker libraries to create realistic data, maintains relational integrity, and allows configurable data volumes. u... Use when working with databases or data models. Trigger with phrases like 'database', 'query', or 'schema'.

1033

cursor-codebase-indexing

jeremylongshore

Execute set up and optimize Cursor codebase indexing. Triggers on "cursor index setup", "codebase indexing", "index codebase", "cursor semantic search". Use when working with cursor codebase indexing functionality. Trigger with phrases like "cursor codebase indexing", "cursor indexing", "cursor".

885

testing-mobile-apps

jeremylongshore

Execute mobile app testing on iOS and Android devices/simulators. Use when performing specialized testing. Trigger with phrases like "test mobile app", "run iOS tests", or "validate Android functionality".

810

You might also like

langfuse

davila7

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

743

appinsights-instrumentation

github

Instrument a webapp to send useful telemetry data to Azure App Insights

66

instantly-observability

jeremylongshore

Set up comprehensive observability for Instantly integrations with metrics, traces, and alerts. Use when implementing monitoring for Instantly operations, setting up dashboards, or configuring alerting for Instantly integration health. Trigger with phrases like "instantly monitoring", "instantly metrics", "instantly observability", "monitor instantly", "instantly alerts", "instantly tracing".

26

apollo-observability

jeremylongshore

Set up Apollo.io monitoring and observability. Use when implementing logging, metrics, tracing, and alerting for Apollo integrations. Trigger with phrases like "apollo monitoring", "apollo metrics", "apollo observability", "apollo logging", "apollo alerts".

12

evernote-observability

jeremylongshore

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".

10

linear-observability

jeremylongshore

Implement monitoring, logging, and alerting for Linear integrations. Use when setting up metrics collection, creating dashboards, or configuring alerts for Linear API usage. Trigger with phrases like "linear monitoring", "linear observability", "linear metrics", "linear logging", "monitor linear integration".

10

Search skills

Search the agent skills registry