The skill is well-written but somewhat verbose for an advisory skill. Some sections could be tightened — e.g., the explanations of what guarded rollouts and experiments are, the repeated emphasis on event health, and the detailed tables for secondary metric types. However, it largely avoids explaining things Claude already knows and most content earns its place.

The skill provides concrete MCP tool calls, specific output formats to present to users, clear decision criteria (e.g., 'fewer than 5 metrics', 'only recommend metrics with events flowing'), and explicit examples of formatted recommendations. The guidance is specific and directly executable despite being an advisory skill without code.

The workflow is clearly sequenced across 5 steps with explicit branching by context type (experiment vs guarded rollout vs release policy). Validation is built in — checking event health before recommending, surfacing auto-attached metrics before adding new ones, and flagging at-risk metrics. The 'Important Context' section adds critical guardrails for error-prone scenarios like mid-experiment changes and context kind mismatches.

The skill is well-organized with clear sections, appropriate use of tables, and well-signaled references to related skills (metric-create, metric-instrument) at the end. For a standalone skill with no bundle files, the content is appropriately structured — the main workflow is in the body with advanced concerns in a separate 'Important Context' section.

Lists multiple 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', 'LaunchDarkly', 'events to monitor'. These cover the natural vocabulary a user would use when asking about experiment metrics.

Highly distinctive with a clear niche: LaunchDarkly experiment/rollout metric selection. The combination of LaunchDarkly-specific terminology, metric selection focus, and release policy context makes it very unlikely to conflict with other skills.