telemetry-tagging

Tagging production telemetry with REQ-* keys enables end-to-end traceability from runtime incidents back to requirements. Inject REQ-* identifiers into logs, attach req:<REQ-ID> labels to metrics, and add REQ-* attributes to distributed traces across your deployed code to link observability signals directly to functional requirements.

Yes. Telemetry tagging supports Python, TypeScript, Datadog, Prometheus, and OpenTelemetry instrumentations. The Skill applies REQ-* identifiers across these platforms during deployments and observability setup, allowing you to tag logs, metrics, and traces consistently regardless of your stack.

Verification steps confirm tags are present in your instrumented logs, metrics, and traces. After instrumenting code with REQ-* identifiers, check observability platforms and logs to confirm tags appear in production telemetry events, validating that end-to-end traceability from requirements to runtime is working.

Instrument login() to emit logs, metrics, and traces tagged with a specific REQ-ID such as REQ-USER-LOGIN-001. This attaches the requirement identifier to all telemetry signals from that function, enabling you to trace login-related incidents directly back to the original requirement.

Use REQ-* tagging in incident investigations to correlate runtime issues with functional requirements. When an incident occurs, REQ-* keys in logs, metrics, and traces create a direct path from the problem back to the requirement it violates, accelerating root-cause analysis and impact assessment.

Yes. REQ-* attributes attached to distributed traces enable pinpoint debugging across microservices. Tags propagate through trace spans, allowing you to follow a single requirement-tagged request through your entire service topology and identify where issues occur.