The skill is well-structured but somewhat verbose in places — the explanatory prose around each context type (experiment, guarded rollout, release policy) could be tightened. Some guidance like 'Guarded rollouts are safety mechanisms, not experiments' and 'The discipline of choosing is the point' are stylistic rather than instructional. However, it largely avoids explaining things Claude already knows and most content earns its place.

The skill provides specific MCP tool calls (list-metrics, list-metric-events, list-release-policies), concrete output formats to display to users, explicit criteria for metric selection (e.g., 'Only recommend metrics with events actively flowing'), and typed recommendation categories with examples. The guidance is specific enough to execute without ambiguity.

The 5-step workflow is clearly sequenced with explicit branching by context type (experiment/guarded rollout/release policy). Step 2 has a clear skip condition for experiments. Step 3 includes a health-check validation gate before recommendations. The 'Important Context' section serves as a validation checklist covering edge cases like mid-experiment changes, CUPED incompatibility, and context kind mismatches.

The skill is appropriately self-contained as an advisory document, with clear one-level-deep references to related skills (metric-create, metric-instrument) at the end. Content is well-organized with headers, tables, and collapsible context sections. The document doesn't try to inline creation or instrumentation workflows — it defers those cleanly.

Lists multiple specific concrete actions: choosing metrics, identifying primary metrics for experiments, adding guardrails, monitoring events in rollouts, and surfacing auto-attached metrics from existing release policies.

Clearly answers both 'what' (choose the right metrics, surface auto-attached metrics, make recommendations) and 'when' (explicit 'Use when...' clause covering multiple trigger scenarios like wanting to know which metrics to use, primary metric selection, guardrail addition, and event monitoring).

Includes strong natural trigger terms users would say: 'metrics', 'experiment', 'primary metric', 'guardrails', 'rollout', 'release policy', 'events to monitor', 'LaunchDarkly'. These cover the natural vocabulary of someone working with LaunchDarkly experimentation.

Highly distinctive with a clear niche: LaunchDarkly metric selection for experiments and rollouts. The specificity to LaunchDarkly, combined with the focus on metric selection (not flag creation or general configuration), makes it unlikely to conflict with other skills.