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".
Best use case
apollo-observability is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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".
Teams using apollo-observability should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/apollo-observability/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How apollo-observability Compares
| Feature / Agent | apollo-observability | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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
- [Prometheus Node.js Client](https://github.com/siimon/prom-client)
- [OpenTelemetry JavaScript](https://opentelemetry.io/docs/languages/js/)
- [Pino Logger](https://getpino.io/)
- [Apollo API Usage Stats](https://docs.apollo.io/reference/view-api-usage-stats)
## Next Steps
Proceed to `apollo-incident-runbook` for incident response.Related Skills
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