Lists multiple specific concrete actions: designing experiments (A/B tests, multivariate, holdouts), hypothesis writing, sample size calculation, duration planning, segment analysis, interpretation, and decision-making. Also mentions failure modes.

Clearly answers 'what does this do' with detailed capabilities, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The 'when' is only implied through the domain description, which per the rubric caps completeness at 2.

Includes strong natural keywords users would say: 'A/B tests', 'multivariate', 'holdouts', 'hypothesis', 'sample size', 'duration', 'segment analysis', 'experiments'. These are terms practitioners naturally use when seeking help with experimentation.

Occupies a clear niche around experimentation and A/B testing with distinct trigger terms like 'A/B tests', 'holdouts', 'sample size', and 'multivariate'. Unlikely to conflict with other skills unless there's a dedicated statistics skill.

The skill is extremely verbose at ~3500+ words. It extensively explains concepts Claude already knows (what p-hacking is, what novelty effects are, what network effects are, basic statistics). Entire paragraphs explain why things matter rather than just stating the actionable rule. The 'What NOT to A/B test' section spends hundreds of words explaining obvious things like 'don't A/B test legal requirements.' The introductory framing ('The default state of experimentation in most companies is sloppy...') and closing philosophical paragraphs add no actionable value.

The skill provides concrete frameworks (four-part hypothesis structure, three-bucket decision model, pre-commitment checklist items) and good/bad hypothesis examples. However, it lacks executable artifacts—no code, no templates inline, no sample size formulas, no concrete decision-rule syntax. The actionable content is described rather than provided directly, with templates and checklists deferred to reference files that don't exist in the bundle.

The 12-consideration framework provides a clear sequence, and the three-bucket decision model with ranked inconclusive resolution paths is well-structured. However, there is no explicit step-by-step workflow with validation checkpoints for the full experiment lifecycle. The pre-experiment readiness checklist and results interpretation checklist are referenced but not provided inline or in bundle files, leaving gaps in the actual workflow.

The skill references seven well-organized reference files and three related skills, with clear one-level-deep navigation. However, none of the referenced bundle files actually exist, meaning the progressive disclosure structure is promised but not delivered. The main SKILL.md also contains far too much inline content that should be in the reference files (e.g., the detailed network effects discussion, the full peeking math explanation).