home / skills / jeremylongshore / claude-code-plugins-plus-skills / perplexity-observability

This skill helps you implement comprehensive perplexity observability with metrics, traces, logging, and alerts for reliable integration health.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill perplexity-observability

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
6.1 KB
---
name: perplexity-observability
description: |
  Set up comprehensive observability for Perplexity integrations with metrics, traces, and alerts.
  Use when implementing monitoring for Perplexity operations, setting up dashboards,
  or configuring alerting for Perplexity integration health.
  Trigger with phrases like "perplexity monitoring", "perplexity metrics",
  "perplexity observability", "monitor perplexity", "perplexity alerts", "perplexity tracing".
allowed-tools: Read, Write, Edit
version: 1.0.0
license: MIT
author: Jeremy Longshore <[email protected]>
---

# Perplexity Observability

## Overview
Set up comprehensive observability for Perplexity integrations.

## Prerequisites
- Prometheus or compatible metrics backend
- OpenTelemetry SDK installed
- Grafana or similar dashboarding tool
- AlertManager configured

## Metrics Collection

### Key Metrics
| Metric | Type | Description |
|--------|------|-------------|
| `perplexity_requests_total` | Counter | Total API requests |
| `perplexity_request_duration_seconds` | Histogram | Request latency |
| `perplexity_errors_total` | Counter | Error count by type |
| `perplexity_rate_limit_remaining` | Gauge | Rate limit headroom |

### Prometheus Metrics

```typescript
import { Registry, Counter, Histogram, Gauge } from 'prom-client';

const registry = new Registry();

const requestCounter = new Counter({
  name: 'perplexity_requests_total',
  help: 'Total Perplexity API requests',
  labelNames: ['method', 'status'],
  registers: [registry],
});

const requestDuration = new Histogram({
  name: 'perplexity_request_duration_seconds',
  help: 'Perplexity request duration',
  labelNames: ['method'],
  buckets: [0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
  registers: [registry],
});

const errorCounter = new Counter({
  name: 'perplexity_errors_total',
  help: 'Perplexity errors by type',
  labelNames: ['error_type'],
  registers: [registry],
});
```

### Instrumented Client

```typescript
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

```typescript
import { trace, SpanStatusCode } from '@opentelemetry/api';

const tracer = trace.getTracer('perplexity-client');

async function tracedPerplexityCall<T>(
  operationName: string,
  operation: () => Promise<T>
): Promise<T> {
  return tracer.startActiveSpan(`perplexity.${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

```typescript
import pino from 'pino';

const logger = pino({
  name: 'perplexity',
  level: process.env.LOG_LEVEL || 'info',
});

function logPerplexityOperation(
  operation: string,
  data: Record<string, any>,
  duration: number
) {
  logger.info({
    service: 'perplexity',
    operation,
    duration_ms: duration,
    ...data,
  });
}
```

## Alert Configuration

### Prometheus AlertManager Rules

```yaml
# perplexity_alerts.yaml
groups:
  - name: perplexity_alerts
    rules:
      - alert: PerplexityHighErrorRate
        expr: |
          rate(perplexity_errors_total[5m]) /
          rate(perplexity_requests_total[5m]) > 0.05
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Perplexity error rate > 5%"

      - alert: PerplexityHighLatency
        expr: |
          histogram_quantile(0.95,
            rate(perplexity_request_duration_seconds_bucket[5m])
          ) > 2
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Perplexity P95 latency > 2s"

      - alert: PerplexityDown
        expr: up{job="perplexity"} == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "Perplexity integration is down"
```

## Dashboard

### Grafana Panel Queries

```json
{
  "panels": [
    {
      "title": "Perplexity Request Rate",
      "targets": [{
        "expr": "rate(perplexity_requests_total[5m])"
      }]
    },
    {
      "title": "Perplexity Latency P50/P95/P99",
      "targets": [{
        "expr": "histogram_quantile(0.5, rate(perplexity_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
```typescript
app.get('/metrics', async (req, res) => {
  res.set('Content-Type', registry.contentType);
  res.send(await registry.metrics());
});
```

## Resources
- [Prometheus Best Practices](https://prometheus.io/docs/practices/naming/)
- [OpenTelemetry Documentation](https://opentelemetry.io/docs/)
- [Perplexity Observability Guide](https://docs.perplexity.com/observability)

## Next Steps
For incident response, see `perplexity-incident-runbook`.

Overview

This skill sets up comprehensive observability for Perplexity integrations with metrics, traces, structured logs, dashboards, and alerts. It provides concrete instrumentation patterns, alert rules, and dashboard queries so teams can monitor Perplexity operations and react to issues quickly. Use it to enable end-to-end visibility and reduce time-to-detect for integration problems.

How this skill works

It instruments Perplexity client calls with Prometheus metrics (counters, histograms, gauges), attaches OpenTelemetry traces to operations, and emits consistent JSON logs. Prometheus scrapes the metrics endpoint, Grafana visualizes key panels, and AlertManager fires rules for error rate, latency, and availability. The skill includes example code patterns and recommended alert expressions to deploy immediately.

When to use it

  • When integrating Perplexity API into production services
  • When you need latency and error visibility for Perplexity operations
  • When building dashboards or runbooks for Perplexity health
  • When configuring alerts for rate limits, errors, or downtime
  • When diagnosing distributed call flows involving Perplexity

Best practices

  • Instrument every external Perplexity call with a histogram for latency and a counter for success/error status
  • Keep label cardinality low: prefer method and status labels, avoid dynamic IDs
  • Propagate context headers for OpenTelemetry to avoid trace gaps across services
  • Expose a /metrics endpoint and restrict access; let Prometheus scrape it
  • Tune alert thresholds gradually and add silences for deployments to avoid alert storms
  • Log structured JSON with consistent field names: service, operation, duration_ms, error_type

Example use cases

  • Track Perplexity request rate and P95 latency on a Grafana dashboard
  • Alert when error rate exceeds 5% over 5 minutes using Prometheus rules
  • Trace a user request through multiple services to find Perplexity call bottlenecks via OpenTelemetry
  • Monitor rate limit headroom with a gauge and trigger warning before throttling
  • Integrate metrics endpoint into existing Prometheus jobs and import dashboard panels

FAQ

What metrics should I start with?

Start with request counter (perplexity_requests_total), request latency histogram (perplexity_request_duration_seconds), error counter (perplexity_errors_total), and a rate-limit gauge (perplexity_rate_limit_remaining).

How do I avoid alert storms during deploys?

Use short-term silences around deploy windows, add a for: duration in alerts (e.g., 5m), and incrementally tune thresholds based on observed baselines.