Conciseness

The Kohavi statistics and the when/when-not lists earn their tokens, but the "Domain Context" section ("a proven product management framework... combines best practices from industry leaders") and the generic "Input Requirements" section are boilerplate padding that could be cut entirely.

Actionability

The body names capabilities ("Determine sample size and runtime for statistical power", "Diagnose why results look suspicious") without giving any procedure, formula, threshold, or example for executing them — it describes rather than instructs, offering only high-level hints like "Need tens of thousands minimum".

Workflow Clarity

Despite claiming to help design, run, and validate experiments, the body contains no sequenced steps whatsoever — there is no design procedure, no validation method for the "Validate experiment results before making ship/no-ship decisions" step, and no error-recovery guidance.

Progressive Disclosure

Section headers are clear and the body is short, but the skill claims seven distinct capabilities (sample sizing, OEC metrics, SRM diagnosis, speedup, culture) whose supporting detail lives nowhere — the only external pointer is a book citation rather than actual reference files.

Specificity

Names the domain ("controlled experiments") and the actions "design, run, and interpret", but these are generic verbs rather than concrete operations, matching the anchor for domain-plus-generic-actions rather than the several-specific-actions level.

Completeness

Explicitly answers both parts: an explicit trigger clause ("Use when asked to..." with six concrete quoted phrases) and a clear third-person statement of what it does ("Helps design, run, and interpret controlled experiments correctly"). Not the level below because the 'when' is fully explicit with concrete triggers, not merely present.

Trigger Term Quality

Natural phrases like "run an A/B test", "check statistical significance", and "avoid false positives" are exactly what users would say, but common synonyms such as "split test", "hypothesis test", or "p-value" are missing, so coverage is good rather than comprehensive.

Distinctiveness Conflict Risk

Triggers like "experiment guardrails", "avoid false positives", and the Kohavi framework citation carve out a fairly distinct experimentation niche, with only minor overlap risk against a generic statistics or data-analysis skill.