home / skills / jeremylongshore / claude-code-plugins-plus-skills / clay-observability
This skill helps you implement comprehensive observability for Clay by configuring metrics, tracing, logging, and alerts across integrations.
npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill clay-observabilityReview the files below or copy the command above to add this skill to your agents.
---
name: clay-observability
description: |
Set up comprehensive observability for Clay integrations with metrics, traces, and alerts.
Use when implementing monitoring for Clay operations, setting up dashboards,
or configuring alerting for Clay integration health.
Trigger with phrases like "clay monitoring", "clay metrics",
"clay observability", "monitor clay", "clay alerts", "clay tracing".
allowed-tools: Read, Write, Edit
version: 1.0.0
license: MIT
author: Jeremy Longshore <[email protected]>
---
# Clay Observability
## Overview
Set up comprehensive observability for Clay integrations.
## Prerequisites
- Prometheus or compatible metrics backend
- OpenTelemetry SDK installed
- Grafana or similar dashboarding tool
- AlertManager configured
## Metrics Collection
### Key Metrics
| Metric | Type | Description |
|--------|------|-------------|
| `clay_requests_total` | Counter | Total API requests |
| `clay_request_duration_seconds` | Histogram | Request latency |
| `clay_errors_total` | Counter | Error count by type |
| `clay_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: 'clay_requests_total',
help: 'Total Clay API requests',
labelNames: ['method', 'status'],
registers: [registry],
});
const requestDuration = new Histogram({
name: 'clay_request_duration_seconds',
help: 'Clay request duration',
labelNames: ['method'],
buckets: [0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
registers: [registry],
});
const errorCounter = new Counter({
name: 'clay_errors_total',
help: 'Clay 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('clay-client');
async function tracedClayCall<T>(
operationName: string,
operation: () => Promise<T>
): Promise<T> {
return tracer.startActiveSpan(`clay.${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: 'clay',
level: process.env.LOG_LEVEL || 'info',
});
function logClayOperation(
operation: string,
data: Record<string, any>,
duration: number
) {
logger.info({
service: 'clay',
operation,
duration_ms: duration,
...data,
});
}
```
## Alert Configuration
### Prometheus AlertManager Rules
```yaml
# clay_alerts.yaml
groups:
- name: clay_alerts
rules:
- alert: ClayHighErrorRate
expr: |
rate(clay_errors_total[5m]) /
rate(clay_requests_total[5m]) > 0.05
for: 5m
labels:
severity: warning
annotations:
summary: "Clay error rate > 5%"
- alert: ClayHighLatency
expr: |
histogram_quantile(0.95,
rate(clay_request_duration_seconds_bucket[5m])
) > 2
for: 5m
labels:
severity: warning
annotations:
summary: "Clay P95 latency > 2s"
- alert: ClayDown
expr: up{job="clay"} == 0
for: 1m
labels:
severity: critical
annotations:
summary: "Clay integration is down"
```
## Dashboard
### Grafana Panel Queries
```json
{
"panels": [
{
"title": "Clay Request Rate",
"targets": [{
"expr": "rate(clay_requests_total[5m])"
}]
},
{
"title": "Clay Latency P50/P95/P99",
"targets": [{
"expr": "histogram_quantile(0.5, rate(clay_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/)
- [Clay Observability Guide](https://docs.clay.com/observability)
## Next Steps
For incident response, see `clay-incident-runbook`.This skill helps you set up end-to-end observability for Clay integrations, covering metrics, distributed traces, structured logs, dashboards, and alerting. It codifies recommended Prometheus metrics, OpenTelemetry tracing patterns, JSON logging conventions, and example Prometheus alert rules. Use it to make Clay operations measurable, debuggable, and alertable.
Instrument application code to emit Prometheus metrics (counters, histograms, gauges) for Clay API calls and errors. Add OpenTelemetry spans around Clay operations to capture latency and failures across services. Emit consistent structured JSON logs for context-rich events. Export metrics to Prometheus, traces to an OpenTelemetry collector/back end, and visualize with Grafana while Prometheus Alertmanager drives alerts.
What metrics should I prioritize first?
Start with total requests, request latency histogram, and error counts. These cover availability, performance, and correctness.
How do I avoid high-cardinality metric issues?
Limit label sets to low-cardinality values (method, status). Drop or aggregate labels that include user IDs or request IDs.