Conciseness

The body is dense with domain-specific guidance ("Executive layer: 5-7 directional metrics", "Compare retention curves, not single-point snapshots") and explains nothing Claude already knows. Not 5: the 'When To Use' bullet list and the opening line largely restate the frontmatter description and could be trimmed.

Actionability

Fully copy-paste-ready commands with flags and output formats ('python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain week --format json') plus sample CSV schemas cover retention, cohort, and funnel cases, and guidance elsewhere is specific. Not 5: the 5-step Workflow never ties its steps to these commands — step 4 'Run cohort + retention analysis' does not reference the calculator script.

Workflow Clarity

The 5-step workflow (framework → KPIs → dashboard layers → cohort analysis → interpret and act) is clearly sequenced, with interpretive checkpoints such as 'Distinguish signal from noise using period-over-period context' and 'Connect metric movement to product changes and release timeline'. Not 5: no explicit validate-and-correct loop, acceptable here since the operations are non-destructive analysis.

Progressive Disclosure

References are real and one level deep — references/metrics-frameworks.md and references/dashboard-templates.md exist and carry the framework/dashboard detail, while the body keeps one-line summaries (e.g., 'AARRR for growth loops and funnel visibility'). Not 5: the mid-document 'See:' list is bare paths with no per-file labels, placed oddly under Dashboard Design Principles rather than a clearly signaled references section.

Specificity

Four concrete actions are named — "defining product KPIs", "building metric dashboards", "running cohort or retention analysis", "interpreting feature adoption trends" — matching the 'several specific actions, minor gaps' anchor. Not 5: the skill's funnel analysis and metric-framework selection (AARRR/North Star/HEART) capabilities from the body are absent from the description.

Completeness

An explicit "Use when defining product KPIs, building metric dashboards..." clause answers 'when' concretely, and the embedded activities convey the 'what'. Not 5: there is no standalone what-statement (e.g., 'Define, track, and interpret product metrics') — the 'what' exists only inside the trigger clause, short of the explicit dual what+when structure of the top anchor.

Trigger Term Quality

"product KPIs", "metric dashboards", "cohort", "retention analysis", and "feature adoption" are phrases users would naturally say when needing this skill. Not 5: common variations such as "funnel", "churn", "North Star", and "analytics" are missing, so coverage is good rather than comprehensive.

Distinctiveness Conflict Risk

The product-metrics niche ("product KPIs", "cohort or retention analysis", "feature adoption") is clearly distinct from generic skills. Not 5: "metric dashboards" overlaps visualization/dashboard skills and "retention analysis" brushes the churn/LTV territory of the sibling SaaS-metrics skill this body cross-references.