Conciseness

Mostly high-signal, but the OpenInference fallback / semconv dual-write / normalizer-gate concept is explained four separate times (Detect step 3, the Instrument fallback bullet, the Enrich "Fallback path" paragraph, and the Validate Inputs note), and message-content capture handling is repeated across "Where this skill must stop and ask" and Enrich. Fits anchor 3 ('could be tightened') rather than 4, where trim instances would be minor; the fourfold repetition of one gate is more than minor.

Actionability

Concrete and executable throughout — exact env vars (OTEL_EXPORTER_OTLP_METRICS_TEMPORALITY_PREFERENCE=delta), endpoint forms per exporter type, token scope openTelemetryTrace.ingest, dtctl version / dtctl query commands, and specific attribute names — which satisfies the instruction-only-skill standard. Not 5: line 114-115 is corrupted ("Python instrumentors can dual-write it validates like native.") losing the actual dual-write condition/flag, and the common-shape wiring has no copy-paste snippet, leaving minor gaps.

Workflow Clarity

The four steps are explicitly sequenced with a flow diagram, per-step Inputs/Outputs, a drawn feedback loop ("validate failure → fix in enrich or instrument"), explicit gates (normalizer gate before Validate, stop-and-ask list), failure routing back to Enrich/Instrument, and a re-run instruction — matching the anchor-5 profile of clear sequence, explicit validation, and error-recovery loops. Not 4: validation is mechanical (dtctl contract assertions), not just 'verify output', and every checkpoint is explicit.

Progressive Disclosure

The body is a decision backbone with all six reference files (examples-index, enrich-normalization, multi-sdk-apps, no-native-instrumentation, enhance-existing, validate-contract — all verified to exist) linked inline at point of need plus a References section with one-line descriptions; cross-links between references are lateral, not nested. Not 4: content split is appropriate (contract details, recipes, and normalizer configs all live in references one level deep) and navigation is easy from both the body and the References list.

Specificity

Names multiple concrete actions in third person — "decision process for instrumenting a GenAI/LLM app ... so it emits OTel GenAI semantic-convention (gen_ai.*) spans queryable in Dynatrace", "Four steps: detect, instrument, enrich, validate", "Decides which approach a given SDK needs" — with comprehensive coverage of what the skill does and produces. Not 4: coverage goes beyond 'several actions with minor gaps' by also stating the decision output and the relationship to the examples repo.

Completeness

Explicitly answers both what ("decision process for instrumenting ... Four steps: detect, instrument, enrich, validate") and when (a dedicated "Trigger:" clause with concrete phrases), matching the anchor-5 example structure. Not 4: the 'when' is not merely present but given as quoted trigger phrases plus negative scope, so it cannot be more explicit.

Trigger Term Quality

The explicit Trigger list gives four natural utterances — "instrument this GenAI app", "add tracing to this LLM app", "how do I get gen_ai spans into Dynatrace", "onboard this agent app to AI Observability" — covering synonym variants for both the app (GenAI/LLM/agent) and the action (instrument/tracing/onboard). Not 4: the phrase set is comprehensive for the domain and every phrase is something a user would naturally say, exceeding the anchor-4 'a few natural terms missing' profile.

Distinctiveness Conflict Risk

A clear niche (instrumenting Python/TS GenAI apps for Dynatrace) with explicit disambiguation — "Do NOT use for analyzing telemetry from an already-instrumented app (use dt-obs-genai)" and "does not replace the concrete per-framework recipes in dynatrace-oss/..." — minimizing conflict with the sibling analysis skill. Not 4: overlap risk is not merely minor; the negative triggers actively route away from the nearest competing skill.