Agent skill
webflow-observability
Set up observability for Webflow integrations — Prometheus metrics for API calls, OpenTelemetry tracing, structured logging with pino, Grafana dashboards, and alerting for rate limits, errors, and latency. Trigger with phrases like "webflow monitoring", "webflow metrics", "webflow observability", "monitor webflow", "webflow alerts", "webflow tracing".
Install this agent skill to your Project
npx add-skill https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/main/plugins/saas-packs/webflow-pack/skills/webflow-observability
SKILL.md
Webflow Observability
Overview
Full observability stack for Webflow Data API v2 integrations: Prometheus metrics for API call counting and latency, OpenTelemetry distributed tracing, structured JSON logging, and alerting rules for error rate and rate limit exhaustion.
Prerequisites
prom-clientfor Prometheus metrics@opentelemetry/apifor tracing (optional)pinofor structured logging- Prometheus + Grafana (or compatible backend)
Instructions
Step 1: Prometheus Metrics
// src/observability/metrics.ts
import { Registry, Counter, Histogram, Gauge } from "prom-client";
export const registry = new Registry();
// API request counter (by operation and status)
export const apiRequests = new Counter({
name: "webflow_api_requests_total",
help: "Total Webflow API requests",
labelNames: ["operation", "status_code", "method"] as const,
registers: [registry],
});
// Request duration histogram
export const apiDuration = new Histogram({
name: "webflow_api_request_duration_seconds",
help: "Webflow API request duration in seconds",
labelNames: ["operation"] as const,
buckets: [0.05, 0.1, 0.25, 0.5, 1, 2, 5, 10],
registers: [registry],
});
// Error counter by type
export const apiErrors = new Counter({
name: "webflow_api_errors_total",
help: "Webflow API errors by status code",
labelNames: ["operation", "status_code", "error_type"] as const,
registers: [registry],
});
// Rate limit remaining gauge
export const rateLimitRemaining = new Gauge({
name: "webflow_rate_limit_remaining",
help: "Remaining API calls before rate limit",
registers: [registry],
});
// CMS items gauge (track total items across collections)
export const cmsItemCount = new Gauge({
name: "webflow_cms_items_total",
help: "Total CMS items by collection",
labelNames: ["collection", "site"] as const,
registers: [registry],
});
// Webhook event counter
export const webhookEvents = new Counter({
name: "webflow_webhook_events_total",
help: "Received webhook events by trigger type",
labelNames: ["trigger_type", "status"] as const,
registers: [registry],
});
Step 2: Instrumented Client Wrapper
// src/observability/instrumented-client.ts
import { WebflowClient } from "webflow-api";
import { apiRequests, apiDuration, apiErrors, rateLimitRemaining } from "./metrics.js";
export async function instrumentedCall<T>(
operation: string,
method: string,
fn: () => Promise<T>
): Promise<T> {
const timer = apiDuration.startTimer({ operation });
try {
const result = await fn();
apiRequests.inc({ operation, status_code: "200", method });
timer();
return result;
} catch (error: any) {
const statusCode = String(error.statusCode || error.status || "unknown");
apiRequests.inc({ operation, status_code: statusCode, method });
apiErrors.inc({
operation,
status_code: statusCode,
error_type: statusCode === "429" ? "rate_limit" : statusCode >= "500" ? "server" : "client",
});
timer();
throw error;
}
}
// Usage
const { sites } = await instrumentedCall("sites.list", "GET", () =>
webflow.sites.list()
);
const { items } = await instrumentedCall("items.listLive", "GET", () =>
webflow.collections.items.listItemsLive(collectionId)
);
const item = await instrumentedCall("items.create", "POST", () =>
webflow.collections.items.createItem(collectionId, {
fieldData: { name: "Test", slug: "test" },
})
);
Step 3: Metrics Endpoint
// api/metrics.ts
import express from "express";
import { registry } from "../observability/metrics.js";
const app = express();
app.get("/metrics", async (req, res) => {
res.set("Content-Type", registry.contentType);
res.send(await registry.metrics());
});
Step 4: OpenTelemetry Distributed Tracing
// src/observability/tracing.ts
import { trace, SpanStatusCode, context } from "@opentelemetry/api";
const tracer = trace.getTracer("webflow-integration", "1.0.0");
export async function tracedCall<T>(
operationName: string,
attributes: Record<string, string>,
fn: () => Promise<T>
): Promise<T> {
return tracer.startActiveSpan(`webflow.${operationName}`, async (span) => {
span.setAttributes({
"webflow.operation": operationName,
...attributes,
});
try {
const result = await fn();
span.setStatus({ code: SpanStatusCode.OK });
return result;
} catch (error: any) {
span.setStatus({
code: SpanStatusCode.ERROR,
message: error.message,
});
span.recordException(error);
span.setAttributes({
"webflow.error.status_code": String(error.statusCode || "unknown"),
});
throw error;
} finally {
span.end();
}
});
}
// Usage
const { collections } = await tracedCall(
"collections.list",
{ "webflow.site_id": siteId },
() => webflow.collections.list(siteId)
);
Step 5: Structured Logging
// src/observability/logger.ts
import pino from "pino";
export const logger = pino({
name: "webflow-integration",
level: process.env.LOG_LEVEL || "info",
serializers: {
err: pino.stdSerializers.err,
},
// Redact sensitive fields
redact: {
paths: ["accessToken", "apiToken", "*.authorization", "req.headers.authorization"],
censor: "[REDACTED]",
},
});
// Log API calls with consistent structure
export function logApiCall(
operation: string,
durationMs: number,
status: "success" | "error",
metadata?: Record<string, any>
) {
const logFn = status === "error" ? logger.error.bind(logger) : logger.info.bind(logger);
logFn({
service: "webflow",
operation,
durationMs,
status,
...metadata,
}, `webflow.${operation} ${status} (${durationMs}ms)`);
}
// Log webhook events
export function logWebhook(triggerType: string, status: "processed" | "failed" | "skipped") {
logger.info({
service: "webflow",
event: "webhook",
triggerType,
status,
}, `webhook.${triggerType} ${status}`);
}
Step 6: AlertManager Rules
# prometheus/webflow-alerts.yml
groups:
- name: webflow
rules:
- alert: WebflowHighErrorRate
expr: |
(
rate(webflow_api_errors_total[5m]) /
rate(webflow_api_requests_total[5m])
) > 0.05
for: 5m
labels:
severity: warning
annotations:
summary: "Webflow API error rate > 5%"
description: "{{ $value | humanizePercentage }} errors in last 5m"
- alert: WebflowRateLimited
expr: |
rate(webflow_api_errors_total{status_code="429"}[5m]) > 0
for: 2m
labels:
severity: warning
annotations:
summary: "Webflow API rate limited"
- alert: WebflowHighLatency
expr: |
histogram_quantile(0.95,
rate(webflow_api_request_duration_seconds_bucket[5m])
) > 3
for: 5m
labels:
severity: warning
annotations:
summary: "Webflow P95 latency > 3s"
- alert: WebflowDown
expr: |
sum(rate(webflow_api_requests_total{status_code=~"5.."}[5m])) /
sum(rate(webflow_api_requests_total[5m])) > 0.5
for: 2m
labels:
severity: critical
annotations:
summary: "Webflow API > 50% server errors"
- alert: WebflowRateLimitLow
expr: webflow_rate_limit_remaining < 10
for: 1m
labels:
severity: warning
annotations:
summary: "Webflow rate limit nearly exhausted"
Step 7: Grafana Dashboard Queries
{
"panels": [
{
"title": "Request Rate by Operation",
"targets": [{ "expr": "sum by (operation) (rate(webflow_api_requests_total[5m]))" }]
},
{
"title": "Error Rate",
"targets": [{ "expr": "sum(rate(webflow_api_errors_total[5m])) / sum(rate(webflow_api_requests_total[5m]))" }]
},
{
"title": "Latency P50 / P95 / P99",
"targets": [
{ "expr": "histogram_quantile(0.5, rate(webflow_api_request_duration_seconds_bucket[5m]))", "legendFormat": "p50" },
{ "expr": "histogram_quantile(0.95, rate(webflow_api_request_duration_seconds_bucket[5m]))", "legendFormat": "p95" },
{ "expr": "histogram_quantile(0.99, rate(webflow_api_request_duration_seconds_bucket[5m]))", "legendFormat": "p99" }
]
},
{
"title": "Rate Limit Remaining",
"targets": [{ "expr": "webflow_rate_limit_remaining" }]
},
{
"title": "Webhook Events by Type",
"targets": [{ "expr": "sum by (trigger_type) (rate(webflow_webhook_events_total[5m]))" }]
}
]
}
Output
- Prometheus metrics: request count, latency histogram, error rate, rate limit gauge
- OpenTelemetry tracing for end-to-end request visibility
- Structured JSON logging with PII redaction
- AlertManager rules for error rate, latency, and rate limits
- Grafana dashboard panels
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Missing metrics | Calls not instrumented | Wrap with instrumentedCall() |
| High cardinality | Too many label values | Limit operation to known set |
| Trace gaps | Missing context propagation | Pass OTel context in async calls |
| Alert storms | Thresholds too sensitive | Increase for duration |
Resources
Next Steps
For incident response, see webflow-incident-runbook.
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