Conciseness

The body is lean and assumes competence — no concept explanations of what retention or A/B testing are — with two minor trimmable redundancies: the same two skill links appear in both 'Skill Locations' and the 'References' section, and Usage Notes lines like 'Pair statistical interpretation with practical business significance' restate Workflow 3 step 3. This matches the score-4 anchor 'efficient; minor instances that could be trimmed'; it is not a 5 because of the duplicated links and restated guidance.

Actionability

Fully executable, copy-paste-ready commands with realistic arguments: 'python .../metrics_calculator.py retention events.csv' with subcommands enumerated (retention, cohort, funnel) and 'python .../sample_size_calculator.py --baseline-rate 0.12 --mde 0.02 --mde-type absolute --daily-samples 800'. Each workflow pairs concrete steps with a concrete 'Expected Output' artifact, covering the common cases — matching the score-5 anchor.

Workflow Clarity

All three workflows use a clear Goal → numbered Steps → Expected Output structure, and Workflow 3 includes decision checkpoints ('check guardrails; pair statistical significance with practical significance before recommending'). Not a 5 because validation is implicit in places: Workflow 2 never verifies the CSV export format before running the calculator, and no workflow has an explicit fix/retry loop; this matches the score-4 anchor 'clear sequence with most checkpoints present; minor validation gaps'. The operations are non-destructive, so the destructive/batch cap does not apply.

Progressive Disclosure

The body is a well-sectioned overview (Purpose, Skill Integration, Workflows, Usage Notes, Related Agents, References) with one-level-deep, clearly signaled external references; no own bundle files exist to organize. It sits at 4 rather than 5 because the same two skill links are duplicated across 'Skill Locations' and 'References', a minor organization gap.

Specificity

Lists multiple specific concrete actions — 'KPI definition, dashboard setup, experiment design, and test result interpretation' — and reinforces them with concrete examples ('defining activation/retention KPIs', 'sizing an A/B test', 'judging whether the result is significant enough to ship'), comprehensively covering the stated domain. It goes beyond the score-4 anchor ('several specific actions; minor gaps') because the e.g. clause covers all four capabilities; third-person voice, so no voice penalty.

Completeness

Explicitly answers both questions: the 'what' ('KPI definition, dashboard setup, experiment design, and test result interpretation') and the 'when' ('Use when a product question needs numbers — e.g., ...') with concrete trigger phrases. This clearly matches the score-5 anchor; the 'when' clause is explicit and illustrated, not weakly implied as in a 3 or thin as in a 4.

Trigger Term Quality

Good natural keyword coverage: 'KPI', 'dashboard', 'activation', 'retention', 'A/B test', 'experiment', and the natural trigger phrase 'a product question needs numbers'. A few natural terms users would say are missing (e.g., 'funnel', 'cohort', 'conversion', 'split test'), matching the score-4 anchor 'good keyword coverage; a few natural terms missing' rather than the comprehensive synonym coverage of a 5.

Distinctiveness Conflict Risk

The quantitative framing ('product question needs numbers', 'sizing an A/B test', 'significant enough to ship') carves a fairly clear niche distinct from qualitative PM or general data-analysis skills, but 'analytics' and 'dashboard' are broad terms with minor overlap risk against closely related data/BI skills — matching the score-4 anchor rather than the minimal-conflict clarity of a 5.