apollo-observability
Provides tools for Prometheus metrics, OpenTelemetry tracing, and logging to monitor Apollo API usage and health.
Install
mkdir -p .claude/skills/apollo-observability && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/3512" && unzip -o skill.zip -d .claude/skills/apollo-observability && rm skill.zipInstalls to .claude/skills/apollo-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.
Set up Apollo.io monitoring and observability.Key capabilities
- →Collect Prometheus metrics for Apollo API requests, duration, rate limits, and credit usage.
- →Implement structured logging with PII redaction for Apollo API interactions.
- →Add OpenTelemetry tracing to Apollo API calls for distributed tracing.
- →Configure alerting rules for Apollo API errors, rate limits, latency, and credit burn rate.
- →Expose HTTP endpoints for Prometheus metrics and health checks.
How it works
The skill instruments an Axios client with interceptors to automatically collect metrics, logs, and traces for Apollo API calls. It uses `prom-client` for metrics, `pino` for structured logging with PII redaction, and OpenTelemetry for distributed tracing.
Inputs & outputs
When to use apollo-observability
- →Monitor Apollo API credit usage
- →Set up Prometheus metrics for API latency
- →Implement structured logging for Apollo requests
- →Create alerts for API rate limits
About this skill
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
// 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
// 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
// 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
// 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
# 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
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
/metricsand/healthHTTP 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.
When not to use it
- →When the task involves tuning enrichment scoring thresholds, use `apollo-cost-tuning`.
- →When the task involves incident response, proceed to `apollo-incident-runbook`.
Prerequisites
Limitations
- →Metrics may be missing if `instrumentClient()` is not called before the first API call.
- →Alert noise can occur if `for` duration and thresholds are not tuned.
- →Log volume can be high if `LOG_LEVEL` is not set to `warn` in production.
How it compares
This skill provides pre-configured code for integrating observability tools specifically for Apollo.io, unlike manually setting up each component for a generic API.
Compared to similar skills
apollo-observability side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| apollo-observability (this skill) | 1 | 25d | Caution | Intermediate |
| langfuse | 7 | 6mo | No flags | Intermediate |
| appinsights-instrumentation | 6 | 7mo | Review | Beginner |
| instantly-observability | 2 | 25d | Caution | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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