Agent skill

apollo-observability

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".

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npx add-skill https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/main/plugins/saas-packs/apollo-pack/skills/apollo-observability

SKILL.md

Apollo Observability

Overview

Comprehensive observability for Apollo.io integrations: Prometheus metrics (request count, latency, rate limits, credits), structured logging with PII redaction, OpenTelemetry tracing, and alerting rules. Tracks the metrics that matter: credit burn rate, enrichment success rate, and API health.

Prerequisites

  • Valid Apollo API key
  • Node.js 18+

Instructions

Step 1: Prometheus Metrics

typescript
// src/observability/metrics.ts
import { Counter, Histogram, Gauge, Registry } from 'prom-client';

export const registry = new Registry();

export const requestsTotal = new Counter({
  name: 'apollo_requests_total',
  help: 'Total Apollo API requests by endpoint and status',
  labelNames: ['endpoint', 'method', 'status'] as const,
  registers: [registry],
});

export const requestDuration = new Histogram({
  name: 'apollo_request_duration_seconds',
  help: 'Apollo API request duration',
  labelNames: ['endpoint'] as const,
  buckets: [0.1, 0.25, 0.5, 1, 2.5, 5, 10],
  registers: [registry],
});

export const rateLimitRemaining = new Gauge({
  name: 'apollo_rate_limit_remaining',
  help: 'Remaining requests in current rate limit window',
  labelNames: ['endpoint'] as const,
  registers: [registry],
});

export const creditsUsed = new Counter({
  name: 'apollo_credits_used_total',
  help: 'Total Apollo enrichment credits consumed',
  labelNames: ['type'] as const,  // 'person', 'organization', 'bulk'
  registers: [registry],
});

export const enrichmentSuccessRate = new Gauge({
  name: 'apollo_enrichment_success_rate',
  help: 'Percentage of enrichment calls that found a match',
  registers: [registry],
});

Step 2: Axios Interceptors for Auto-Collection

typescript
// src/observability/instrument.ts
import { AxiosInstance } from 'axios';
import { requestsTotal, requestDuration, rateLimitRemaining, creditsUsed } from './metrics';

const CREDIT_ENDPOINTS = ['/people/match', '/people/bulk_match', '/organizations/enrich'];

export function instrumentClient(client: AxiosInstance) {
  client.interceptors.request.use((config) => {
    (config as any)._startTime = Date.now();
    return config;
  });

  client.interceptors.response.use(
    (response) => {
      const endpoint = response.config.url ?? 'unknown';
      const duration = (Date.now() - (response.config as any)._startTime) / 1000;

      requestsTotal.inc({ endpoint, method: response.config.method?.toUpperCase() ?? 'GET', status: String(response.status) });
      requestDuration.observe({ endpoint }, duration);

      // Rate limit tracking
      const remaining = response.headers['x-rate-limit-remaining'];
      if (remaining) rateLimitRemaining.set({ endpoint }, parseInt(remaining, 10));

      // Credit tracking
      if (CREDIT_ENDPOINTS.some((ep) => endpoint.includes(ep))) {
        const type = endpoint.includes('bulk') ? 'bulk' : endpoint.includes('organization') ? 'organization' : 'person';
        const count = response.data?.matches?.length ?? 1;
        creditsUsed.inc({ type }, count);
      }

      return response;
    },
    (err) => {
      requestsTotal.inc({
        endpoint: err.config?.url ?? 'unknown',
        method: err.config?.method?.toUpperCase() ?? 'GET',
        status: String(err.response?.status ?? 0),
      });
      return Promise.reject(err);
    },
  );
}

Step 3: Structured Logging with PII Redaction

typescript
// src/observability/logger.ts
import pino from 'pino';

export const logger = pino({
  level: process.env.LOG_LEVEL ?? 'info',
  redact: {
    paths: ['*.email', '*.phone_numbers', '*.linkedin_url', 'headers.x-api-key'],
    censor: '[REDACTED]',
  },
  formatters: { level: (label) => ({ level: label }) },
  transport: process.env.NODE_ENV !== 'production' ? { target: 'pino-pretty' } : undefined,
});

export const apolloLog = logger.child({ service: 'apollo' });

// Usage:
// apolloLog.info({ endpoint: '/mixed_people/api_search', results: 25 }, 'Search completed');
// apolloLog.warn({ endpoint: '/people/match', status: 429 }, 'Rate limited');
// apolloLog.error({ err, endpoint: '/contacts' }, 'Request failed');

Step 4: OpenTelemetry Tracing

typescript
// src/observability/tracing.ts
import { trace, SpanStatusCode } from '@opentelemetry/api';
import { AxiosInstance } from 'axios';

const tracer = trace.getTracer('apollo-integration');

export function addTracing(client: AxiosInstance) {
  client.interceptors.request.use((config) => {
    const span = tracer.startSpan(`apollo.${config.method?.toUpperCase()} ${config.url}`);
    span.setAttribute('apollo.endpoint', config.url ?? '');
    (config as any)._span = span;
    return config;
  });

  client.interceptors.response.use(
    (response) => {
      const span = (response.config as any)._span;
      if (span) {
        span.setAttribute('http.status_code', response.status);
        span.setAttribute('apollo.rate_limit_remaining', response.headers['x-rate-limit-remaining'] ?? 'unknown');
        span.setStatus({ code: SpanStatusCode.OK });
        span.end();
      }
      return response;
    },
    (err) => {
      const span = (err.config as any)?._span;
      if (span) {
        span.setAttribute('http.status_code', err.response?.status ?? 0);
        span.setStatus({ code: SpanStatusCode.ERROR, message: err.message });
        span.end();
      }
      return Promise.reject(err);
    },
  );
}

Step 5: Alerting Rules

yaml
# prometheus/apollo-alerts.yml
groups:
  - name: apollo-integration
    rules:
      - alert: ApolloHighErrorRate
        expr: rate(apollo_requests_total{status=~"4..|5.."}[5m]) / rate(apollo_requests_total[5m]) > 0.1
        for: 5m
        labels: { severity: critical }
        annotations: { summary: "Apollo API error rate > 10% for 5 minutes" }

      - alert: ApolloRateLimitLow
        expr: apollo_rate_limit_remaining < 20
        for: 1m
        labels: { severity: warning }
        annotations: { summary: "Apollo rate limit below 20 remaining requests" }

      - alert: ApolloHighLatency
        expr: histogram_quantile(0.95, rate(apollo_request_duration_seconds_bucket[5m])) > 5
        for: 10m
        labels: { severity: warning }
        annotations: { summary: "Apollo p95 latency > 5s for 10 minutes" }

      - alert: ApolloCreditBurnRate
        expr: rate(apollo_credits_used_total[1h]) * 24 > 500
        for: 30m
        labels: { severity: warning }
        annotations: { summary: "Apollo credit burn rate projects > 500/day" }

Step 6: Metrics Endpoint

typescript
import express from 'express';
import { registry } from './metrics';

const metricsApp = express();
metricsApp.get('/metrics', async (_, res) => {
  res.set('Content-Type', registry.contentType);
  res.end(await registry.metrics());
});
metricsApp.get('/health', (_, res) => res.json({ status: 'ok' }));
metricsApp.listen(9090, () => console.log('Metrics on :9090'));

Output

  • Prometheus metrics: requests, duration, rate limits, credits, enrichment success
  • Axios interceptors for automatic collection on every API call
  • Pino structured logger with PII redaction
  • OpenTelemetry tracing spans for distributed tracing
  • Alerting rules for errors, rate limits, latency, and credit burn rate
  • /metrics and /health HTTP endpoints

Error Handling

Issue Resolution
Missing metrics Verify instrumentClient() called before first API call
Alert noise Tune for duration and thresholds
Log volume Use LOG_LEVEL=warn in production
Credit burn alert Review enrichment scoring thresholds in apollo-cost-tuning

Resources

Next Steps

Proceed to apollo-incident-runbook for incident response.

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