palantir-observability
Set up comprehensive observability for Palantir integrations with metrics, traces, and alerts. Use when implementing monitoring for Palantir operations, setting up dashboards, or configuring alerting for Palantir integration health. Trigger with phrases like "palantir monitoring", "palantir metrics", "palantir observability", "monitor palantir", "palantir alerts", "palantir tracing".
Allowed Tools
Provided by Plugin
palantir-pack
Claude Code skill pack for Palantir (24 skills)
Installation
This skill is included in the palantir-pack plugin:
/plugin install palantir-pack@claude-code-plugins-plus
Click to copy
Instructions
Palantir Observability
Overview
Set up comprehensive observability for Palantir integrations.
Prerequisites
- Prometheus or compatible metrics backend
- OpenTelemetry SDK installed
- Grafana or similar dashboarding tool
- AlertManager configured
Metrics Collection
Key Metrics
| Metric | Type | Description |
|---|---|---|
palantirrequeststotal |
Counter | Total API requests |
palantirrequestduration_seconds |
Histogram | Request latency |
palantirerrorstotal |
Counter | Error count by type |
palantirratelimit_remaining |
Gauge | Rate limit headroom |
Prometheus Metrics
import { Registry, Counter, Histogram, Gauge } from 'prom-client';
const registry = new Registry();
const requestCounter = new Counter({
name: 'palantir_requests_total',
help: 'Total Palantir API requests',
labelNames: ['method', 'status'],
registers: [registry],
});
const requestDuration = new Histogram({
name: 'palantir_request_duration_seconds',
help: 'Palantir request duration',
labelNames: ['method'],
buckets: [0.05, 0.1, 0.25, 0.5, 1, 2.5, 5],
registers: [registry],
});
const errorCounter = new Counter({
name: 'palantir_errors_total',
help: 'Palantir errors by type',
labelNames: ['error_type'],
registers: [registry],
});
Instrumented Client
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
import { trace, SpanStatusCode } from '@opentelemetry/api';
const tracer = trace.getTracer('palantir-client');
async function tracedPalantirCall<T>(
operationName: string,
operation: () => Promise<T>
): Promise<T> {
return tracer.startActiveSpan(`palantir.${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
import pino from 'pino';
const logger = pino({
name: 'palantir',
level: process.env.LOG_LEVEL || 'info',
});
function logPalantirOperation(
operation: string,
data: Record<string, any>,
duration: number
) {
logger.info({
service: 'palantir',
operation,
duration_ms: duration,
...data,
});
}
Alert Configuration
Prometheus AlertManager Rules
# palantir_alerts.yaml
groups:
- name: palantir_alerts
rules:
- alert: PalantirHighErrorRate
expr: |
rate(palantir_errors_total[5m]) /
rate(palantir_requests_total[5m]) > 0.05
for: 5m
labels:
severity: warning
annotations:
summary: "Palantir error rate > 5%"
- alert: PalantirHighLatency
expr: |
histogram_quantile(0.95,
rate(palantir_request_duration_seconds_bucket[5m])
) > 2
for: 5m
labels:
severity: warning
annotations:
summary: "Palantir P95 latency > 2s"
- alert: PalantirDown
expr: up{job="palantir"} == 0
for: 1m
labels:
severity: critical
annotations:
summary: "Palantir integration is down"
Dashboard
Grafana Panel Queries
{
"panels": [
{
"title": "Palantir Request Rate",
"targets": [{
"expr": "rate(palantir_requests_total[5m])"
}]
},
{
"title": "Palantir Latency P50/P95/P99",
"targets": [{
"expr": "histogram_quantile(0.5, rate(palantir_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
app.get('/metrics', async (req, res) => {
res.set('Content-Type', registry.contentType);
res.send(await registry.metrics());
});
Resources
Next Steps
For incident response, see palantir-incident-runbook.