flexport-observability
Set up observability for Flexport logistics integrations with metrics, structured logging, distributed tracing, and alerting dashboards. Trigger: "flexport monitoring", "flexport observability", "flexport metrics", "flexport alerts".
Best use case
flexport-observability is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Set up observability for Flexport logistics integrations with metrics, structured logging, distributed tracing, and alerting dashboards. Trigger: "flexport monitoring", "flexport observability", "flexport metrics", "flexport alerts".
Teams using flexport-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/flexport-observability/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How flexport-observability Compares
| Feature / Agent | flexport-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 observability for Flexport logistics integrations with metrics, structured logging, distributed tracing, and alerting dashboards. Trigger: "flexport monitoring", "flexport observability", "flexport metrics", "flexport 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
# Flexport Observability
## Overview
Full observability stack for Flexport integrations: Prometheus metrics for API health, pino structured logging for debugging, OpenTelemetry tracing for latency analysis, and Grafana dashboards for monitoring.
## Instructions
### Step 1: Prometheus Metrics
```typescript
import { Counter, Histogram, Gauge, register } from 'prom-client';
const flexportRequests = new Counter({
name: 'flexport_api_requests_total',
help: 'Total Flexport API requests',
labelNames: ['method', 'endpoint', 'status'],
});
const flexportLatency = new Histogram({
name: 'flexport_api_latency_seconds',
help: 'Flexport API response time',
labelNames: ['endpoint'],
buckets: [0.1, 0.25, 0.5, 1, 2.5, 5, 10],
});
const flexportRateLimit = new Gauge({
name: 'flexport_rate_limit_remaining',
help: 'Remaining API calls in current window',
});
// Instrumented fetch wrapper
async function instrumentedFlexport(path: string, options: RequestInit = {}) {
const endpoint = path.split('?')[0];
const timer = flexportLatency.startTimer({ endpoint });
try {
const res = await fetch(`https://api.flexport.com${path}`, { ...options, headers: { ...headers, ...options.headers } });
flexportRequests.inc({ method: options.method || 'GET', endpoint, status: res.status.toString() });
const remaining = res.headers.get('X-RateLimit-Remaining');
if (remaining) flexportRateLimit.set(parseInt(remaining));
timer();
return res;
} catch (err) {
flexportRequests.inc({ method: options.method || 'GET', endpoint, status: 'error' });
timer();
throw err;
}
}
```
### Step 2: Structured Logging
```typescript
import pino from 'pino';
const logger = pino({
name: 'flexport-integration',
level: process.env.LOG_LEVEL || 'info',
redact: ['headers.Authorization', 'apiKey'],
});
// Log every API call with context
async function loggedFlexport(path: string, options: RequestInit = {}) {
const start = Date.now();
const res = await instrumentedFlexport(path, options);
logger.info({
service: 'flexport',
path,
method: options.method || 'GET',
status: res.status,
latencyMs: Date.now() - start,
rateRemaining: res.headers.get('X-RateLimit-Remaining'),
}, 'Flexport API call');
return res;
}
```
### Step 3: Alert Rules
```yaml
# prometheus-alerts.yml
groups:
- name: flexport
rules:
- alert: FlexportAPIErrors
expr: rate(flexport_api_requests_total{status=~"5.."}[5m]) > 0.1
for: 5m
labels: { severity: critical }
annotations:
summary: "Flexport API error rate elevated"
- alert: FlexportRateLimitLow
expr: flexport_rate_limit_remaining < 10
for: 1m
labels: { severity: warning }
annotations:
summary: "Flexport rate limit nearly exhausted"
- alert: FlexportHighLatency
expr: histogram_quantile(0.99, flexport_api_latency_seconds_bucket) > 5
for: 5m
labels: { severity: warning }
```
### Grafana Dashboard Panels
| Panel | Query | Purpose |
|-------|-------|---------|
| Request rate | `rate(flexport_api_requests_total[5m])` | Throughput |
| Error rate | `rate(flexport_api_requests_total{status=~"4..|5.."}[5m])` | Reliability |
| p99 latency | `histogram_quantile(0.99, rate(flexport_api_latency_seconds_bucket[5m]))` | Performance |
| Rate limit headroom | `flexport_rate_limit_remaining` | Quota |
## Resources
- [prom-client](https://github.com/siimon/prom-client)
- [pino](https://github.com/pinojs/pino)
- [Flexport Status](https://status.flexport.com)
## Next Steps
For incident response, see `flexport-incident-runbook`.Related Skills
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