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monitoring-expert

Configures monitoring systems, implements structured logging pipelines, creates Prometheus/Grafana dashboards, defines alerting rules, and instruments distributed tracing. Implements Prometheus/Grafana stacks, conducts load testing, performs application profiling, and plans infrastructure capacity. Use when setting up application monitoring, adding observability to services, debugging production issues with logs/metrics/traces, running load tests with k6 or Artillery, profiling CPU/memory bottlenecks, or forecasting capacity needs.

97

1.17x
Quality

100%

Does it follow best practices?

Impact

95%

1.17x

Average score across 6 eval scenarios

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Evaluation results

96%

34%

Add Observability to Order Management API

Structured logging and Prometheus metrics instrumentation

Criteria
Without context
With context

Uses pino for logging

0%

100%

JSON structured fields

60%

100%

Request ID correlation

100%

100%

Sensitive field redaction

0%

100%

No sensitive data logged

100%

100%

Correct metric types

80%

100%

Metric naming convention

50%

50%

Business metrics present

100%

100%

Health check endpoint

100%

100%

Metrics endpoint

100%

100%

Consistent event naming

0%

100%

100%

1%

Set Up Alerting and Dashboards for a Payment Processing Service

Prometheus alerting rules and Grafana dashboard design

Criteria
Without context
With context

Severity classification

100%

100%

Threshold-based alerting

90%

100%

Alert 'for' duration

100%

100%

Runbook URL annotations

100%

100%

Summary and description annotations

100%

100%

Alertmanager severity routing

100%

100%

RED method in dashboard

100%

100%

USE method in dashboard

100%

100%

predict_linear for capacity

100%

100%

Capacity forecast horizon

100%

100%

Business metrics in dashboard

100%

100%

No noisy alert patterns

100%

100%

100%

10%

Load Test an E-Commerce Checkout API Before Black Friday

k6 load testing with staged profiles and performance thresholds

Criteria
Without context
With context

Uses k6

100%

100%

Staged load profile

100%

100%

p95 latency threshold

100%

100%

p99 latency threshold

0%

100%

Error rate threshold

100%

100%

Multi-step user journey

100%

100%

Think time between steps

100%

100%

Custom business metrics

100%

100%

Response validation

100%

100%

Test plan explains thresholds

100%

100%

96%

1%

Trace the Checkout Pipeline

OpenTelemetry distributed tracing instrumentation

Criteria
Without context
With context

NodeSDK import

100%

100%

OTLPTraceExporter used

100%

100%

Jaeger OTLP URL

50%

50%

Service name resource attribute

87%

100%

Auto-instrumentations enabled

100%

100%

Manual span creation

100%

100%

Span attributes set

100%

100%

Exception recording

100%

100%

SpanStatusCode set

100%

100%

Context extraction from incoming requests

100%

100%

Context injection into outgoing requests

100%

100%

span.end() always called

100%

100%

98%

2%

Diagnose a Sluggish Node.js API

Application profiling with CPU and memory diagnostics

Criteria
Without context
With context

clinic.js CPU profiling

100%

100%

clinic bubbleprof mentioned

50%

100%

performance.mark used

100%

100%

performance.measure used

100%

100%

PerformanceObserver used

100%

100%

process.memoryUsage called

100%

100%

v8.writeHeapSnapshot used

100%

100%

Profiling workflow documented

100%

100%

Symptom-to-tool mapping

100%

100%

No GUI-only tools prescribed

100%

80%

83%

38%

Plan Infrastructure Capacity for a Growing Platform

Capacity planning with resource forecasting and performance budgets

Criteria
Without context
With context

CPU buffer 30%

100%

100%

Memory buffer 20%

0%

100%

Connection buffer 25%

12%

100%

Storage buffer 40%

0%

100%

predict_linear for memory

100%

100%

predict_linear for disk

100%

100%

Performance budget apiP95

0%

100%

Performance budget apiP99

16%

100%

Performance budget error rate

100%

100%

Scale-up trigger at 80% CPU

0%

25%

Planning trigger at 70% CPU

50%

50%

Scale-down trigger defined

0%

0%

Instance count calculation

75%

100%

Repository
jeffallan/claude-skills
Evaluated
Agent
Claude Code
Model
Claude Sonnet 4.6

Table of Contents

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