opentelemetry

Getting Started

1. Install the skill using the command above
2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
3. Reference the skill in your prompt
4. The AI will use the skill's capabilities automatically

Example Prompts

• "Deploy the latest build to the staging environment and run smoke tests"
• "Check the CI pipeline status and summarize any recent failures"

Overview

OpenTelemetry (OTel) is the unified observability standard for instrumenting applications with traces, metrics, and logs. It supports auto-instrumentation across Node.js, Python, Java, and Go, and exports telemetry to backends like Jaeger, Grafana, Datadog, and Honeycomb through a flexible Collector pipeline.

Instructions

• When adding tracing, create spans with meaningful names, set span kinds (CLIENT, SERVER, PRODUCER, CONSUMER), add business-relevant attributes, and use W3C Trace Context for propagation.
• When adding metrics, choose the right instrument type: Counter for monotonic values, Histogram for distributions like latency, UpDownCounter for fluctuating values, and Gauge for point-in-time readings.
• When setting up auto-instrumentation, use the language-specific packages (@opentelemetry/auto-instrumentations-node, opentelemetry-instrumentation for Python, etc.) to capture HTTP, database, and messaging spans without code changes.
• When configuring the OTel Collector, define pipelines with receivers (OTLP, Prometheus), processors (batch, memory_limiter, tail_sampling), and exporters (OTLP, Jaeger, Datadog) in the collector config.
• When deploying Collectors, choose sidecar mode for per-pod collection, agent mode for per-node, or gateway mode for centralized processing.
• When setting resource attributes, always include service.name, service.version, and deployment.environment, and use cloud/container resource detectors for infrastructure metadata.
• When naming attributes, follow OTel semantic conventions (http.request.method, db.system, messaging.system) instead of inventing custom names.

Examples

Example 1: Add distributed tracing to a Node.js microservice

User request: "Instrument my Express API with OpenTelemetry tracing"

Actions:

1. Install @opentelemetry/auto-instrumentations-node and OTLP exporter
2. Configure SDK with service name, version, and BatchSpanProcessor
3. Set up OTLP exporter pointing to the Collector endpoint
4. Add custom spans with business attributes for key operations

Output: An auto-instrumented Express API sending traces to the OTel Collector with correlated spans across services.

Example 2: Set up an OTel Collector pipeline

User request: "Configure an OTel Collector to receive traces and export to Grafana Tempo"

Actions:

1. Define OTLP gRPC receiver in the Collector config
2. Add batch processor and memory_limiter for production safety
3. Configure Tempo exporter with endpoint and authentication
4. Wire the traces pipeline: receiver -> processor -> exporter

Output: A Collector config file routing traces from applications to Grafana Tempo with batching and memory protection.

Guidelines

• Always set service.name and service.version as resource attributes.
• Use semantic conventions for attribute names; never invent custom names when a standard exists.
• Configure BatchSpanProcessor in production, not SimpleSpanProcessor, to avoid blocking the application.
• Set memory_limiter processor on the Collector to prevent OOM crashes.
• Sample in production: TraceIdRatioBased(0.1) captures 10% of traces, sufficient for most services.
• Add custom attributes to spans for business context (user.tier, feature.flag, order.total).
• Never log sensitive data in span attributes (PII, secrets, tokens).