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product-analytics

Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.

72

Quality

87%

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The risk profile of this skill

SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-organized, actionable skill body with excellent executable tooling examples and a clear analytical workflow. Main gaps are minor redundancy with the description and lightly signaled reference navigation.

Suggestions

Consolidate the opening tagline and "When To Use" list with the frontmatter description to avoid restating triggers; lead the body with the workflow instead.

Promote the "See:" reference lines into a dedicated "References" section (or place them under each relevant heading) so the deeper materials are clearly discoverable rather than buried under Dashboard Design Principles.

Add an explicit verification step to the cohort/retention workflow (e.g., sanity-check cohort sizes and confirm the retained-behavior definition before interpreting curves) to give the analysis sequence a concrete checkpoint.

DimensionReasoningScore

Conciseness

Mostly lean with dense bullet lists and tables and no padding of concepts Claude already knows, but the opening line and "When To Use" section partially restate the frontmatter description and could be trimmed.

4 / 5

Actionability

Fully executable, copy-paste-ready CLI examples (retention/cohort/funnel with text and JSON output) plus exact CSV input formats cover the common cases, and the instructional guidance is concrete (e.g., "Executive layer: 5-7 metrics").

5 / 5

Workflow Clarity

A clear, well-ordered 5-step workflow plus a separate 5-step cohort method; no explicit validation checkpoints, but the task is read-only analysis so the destructive/batch cap does not apply and the sequence stands on its own.

4 / 5

Progressive Disclosure

Good structure with real, one-level-deep references (metrics-frameworks.md, dashboard-templates.md) and a provided tooling script, but the references are signaled only in a two-line "See:" block placed mid-document rather than a dedicated navigation section.

4 / 5

Total

17

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, well-scoped description that explicitly pairs concrete capabilities with clear trigger phrases and a distinct product-analytics niche. Only minor gap is a few additional natural synonyms that could broaden trigger coverage.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — "defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends" — giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Explicitly answers both what (defining KPIs, building dashboards, cohort/retention analysis, interpreting adoption trends) and when ("Use when..." with concrete trigger phrases).

5 / 5

Trigger Term Quality

Strong natural terms (product KPIs, metric dashboards, cohort/retention analysis, feature adoption) that users would say, but a few common synonyms ("metrics", "funnel", "engagement") are missing.

4 / 5

Distinctiveness Conflict Risk

Clear product-analytics niche scoped to product stages; triggers (cohort analysis, retention, feature adoption) are distinct from adjacent skills like saas-metrics-coach, giving minimal conflict risk.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
alirezarezvani/claude-skills
Reviewed

Table of Contents

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