Conciseness

The workflow itself is lean, but three spots pad the token budget: the 'Statistical Interpretation Guardrails' section re-teaches basics Claude already knows ("p-value < alpha indicates evidence against null", "Wide intervals imply low precision even when significant"), the Tooling section repeats the sample-size command already shown in step 3, and 'When To Use' restates the frontmatter description. This matches 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than the minor-trim 4 anchor.

Actionability

Concrete, executable guidance throughout: an If/Then/Because hypothesis template with placeholder slots, metric-role definitions, the ICE formula, and a copy-paste-ready command (`python3 scripts/sample_size_calculator.py --baseline-rate 0.12 --mde 0.02 --mde-type absolute`) whose flags match the actual script. It stops short of 5 because there is no worked example (a filled-in hypothesis or numeric ICE calculation) and no sample calculator output to interpret.

Workflow Clarity

The six-step sequence (hypothesis, metrics, sample size, prioritize, launch, interpret) is clearly ordered, with an explicit Hypothesis Quality Checklist as a checkpoint and pre-registered stopping rules ("Decide fixed sample size or fixed duration in advance"), matching the 'clear sequence with most checkpoints present; minor validation gaps' anchor. It is not 5 because there is no explicit validate-and-iterate feedback loop after launch or when guardrails trip.

Progressive Disclosure

Clear sections with both referenced files (`references/experiment-playbook.md`, `references/statistics-reference.md`) verified to exist exactly one level deep, plus a documented script. Minor gaps keep it at 4: the 'See:' list is bare with no annotation of what each file covers, and the inline Statistical Interpretation Guardrails duplicate content that belongs in `references/statistics-reference.md`.

Specificity

The description lists several concrete actions — "planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes" — covering the experimentation lifecycle, matching the 'several specific actions; minor gaps' anchor. It falls short of 5 because the actions appear only inside the 'when' clause rather than as explicit capability statements, and "with practical statistical rigor" is soft padding.

Completeness

The 'when' is explicit and specific ("Use when planning product experiments, ... or interpreting A/B outcomes"), and the 'what' is only implicitly conveyed through the gerund action list rather than a standalone capability statement. This sits between the 3 anchor (what present, when missing) and the 5 anchor (both explicitly stated), matching anchor 4's 'both present but one could be more explicit'.

Trigger Term Quality

Natural trigger phrases like "product experiments", "testable hypotheses", "sample size", and "A/B outcomes" match how users would phrase these needs, fitting the 'good keyword coverage; a few natural terms missing' anchor. It is not 5 because common synonyms such as "split test", "significance", "power", or "conversion rate" are absent.

Distinctiveness Conflict Risk

The experimentation/statistics niche is clear and mostly distinct, but "prioritizing tests" could collide with software test-prioritization requests and "practical statistical rigor" overlaps generic data-analysis skills, fitting the 'mostly distinct; minor overlap risk' anchor rather than the fully distinct 5 anchor.