Names the domain (sample size and power calculations) and lists some specific design types (survival analysis, clustered designs, multiple comparisons), but doesn't describe concrete actions like 'compute required sample size', 'generate power curves', or 'output effect size tables'.

Describes what the skill does but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per the rubric, a missing 'Use when...' clause caps completeness at 2, and since the 'what' is also only moderately detailed, this scores at 1.

Includes relevant terms like 'sample size', 'power calculations', 'survival analysis', 'clustered designs', and 'multiple comparisons', which are natural keywords. However, it misses common variations users might say such as 'power analysis', 'how many subjects', 'statistical power', 'RCT', 'clinical trial design', or 'effect size'.

The combination of 'sample size and power calculations' with specific complex study designs (survival analysis, clustered designs, multiple comparisons) creates a clear niche that is unlikely to conflict with other skills. This is a well-defined statistical specialty area.

Extremely verbose and repetitive. Multiple sections reference each other circularly ('See ## Prerequisites above', 'See ## Usage above', 'See ## Workflow above'). The 'When to Use' section repeats the description verbatim. Boilerplate sections like Risk Assessment, Security Checklist, Lifecycle Status, Evaluation Criteria, and Response Template add significant token overhead without providing actionable value for the actual task of sample size calculation. Much content is generic template filler rather than domain-specific guidance.

The Usage section provides concrete CLI commands with specific parameters, and the Parameters table is useful. However, the actual statistical computation logic is entirely delegated to an opaque `scripts/main.py` with no explanation of the underlying calculations, formulas, or how to handle the advertised complex designs (survival analysis, clustered designs, multiple comparisons). The workflow steps are generic process steps, not specific to power analysis.

The workflow section is entirely generic ('Confirm the user objective', 'Validate that the request matches the documented scope') with no steps specific to sample size calculation. There are no validation checkpoints for statistical correctness (e.g., verifying effect size is reasonable, checking assumptions of the statistical test). The 'Example run plan' is also generic. No feedback loops for verifying calculation results against known benchmarks or sanity checks.

There is some structure with sections and a reference to `references/audit-reference.md`, but the document is monolithic with many sections that could be consolidated or removed. The circular cross-references ('See ## Prerequisites above') are confusing rather than helpful. Content is not well-organized for discovery—the actual usage examples and parameters are buried among boilerplate sections.