clinic-sample-size

Unified tool for calculating sample sizes for Diagnostic, Efficacy, Etiology, and Prognosis clinical studies. Supports various statistical methods (Sensitivity/Specificity, Log-rank, Chi-square, EPV, etc.).

Quality

66%

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Securityby

Passed

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tessl review fix ./scientific-skills/Other/clinic-sample-size/SKILL.md

Quality

Content

67%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.

The content is highly actionable with executable examples for every supported study type and clear script references, but it is weighed down by substantial generic boilerplate that inflates the token budget without adding skill-specific guidance.

Suggestions

Remove or collapse the generic templated sections ('When to Use', 'Required Inputs', 'Output Contract', 'Validation and Safety Rules', 'Failure Handling', 'Deterministic Output Rules', 'Completion Checklist') that are not specific to clinic sample size calculation.

Consolidate the duplicate validation pointers (Validation Shortcut, Quick Validation, Example Usage) into a single verification section to reduce redundancy.

Make the validate-fix-retry loop explicit in the Recommended Workflow rather than leaving error recovery implicit.

Dimension	Reasoning	Score
Conciseness	The body is noticeably padded with generic templated sections ('When to Use', 'Required Inputs', 'Output Contract', 'Validation and Safety Rules', 'Failure Handling', 'Deterministic Output Rules', 'Completion Checklist') that add no skill-specific value, plus redundant pointer lines like 'See ## Usage above for related details.'	2 / 5
Actionability	Provides copy-paste-ready bash commands covering all four study types (e.g. 'python scripts/main.py ... diagnostic sens_spec --se 0.8 --sp 0.9 ...') plus compile and validation commands, with concrete flags and JSON input examples for the common cases.	5 / 5
Workflow Clarity	A clear sequence exists (Example run plan + Recommended Workflow) with validation checkpoints ('python scripts/validate_skill.py --help', final validation pass, Quick Validation), though the feedback loop is implicit rather than an explicit validate-fix-retry cycle.	4 / 5
Progressive Disclosure	Content is organized under clear headers with one-level-deep references to real bundle scripts (scripts/main.py, scripts/calculators.py, scripts/validate_skill.py) and no nested reference chains; minor organization gaps from inlined boilerplate.	4 / 5
	Total	15 / 20 Passed

Description

65%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.

The description clearly conveys a specialized clinical-research niche with strong trigger terms and low conflict risk, but it lacks any explicit 'when to use' guidance and names only one concrete action, limiting completeness and specificity.

Suggestions

Add an explicit 'Use when...' clause naming natural triggers, e.g. 'Use when planning Diagnostic, Efficacy, Etiology, or Prognosis clinical studies and the user needs sample size or power calculations.'

Broaden action coverage beyond a single verb to mention deliverables such as producing Markdown reports or attaching study metadata.

Dimension	Reasoning	Score
Specificity	Names the domain and a single concrete action ('calculating sample sizes') with comprehensive enumeration of study types and statistical methods, but offers only one action verb rather than several distinct actions.	3 / 5
Completeness	The 'what' is clear (sample size calculation for four clinical study types) but there is no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric guidelines.	3 / 5
Trigger Term Quality	Includes natural clinical-research terms a user would say ('sample sizes', 'Diagnostic', 'Efficacy', 'Etiology', 'Prognosis', 'Sensitivity/Specificity', 'Chi-square', 'EPV'), though common synonyms like 'power analysis' or 'RCT' are absent.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (clinical sample-size calculation across four named study types) with distinct, specific triggers and minimal overlap risk with other skills.	5 / 5
	Total	15 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aipoch/medical-research-skills
Path: scientific-skills/Other/clinic-sample-size/SKILL.md
Commit: 63c61d3

Reviewed: 4 hours ago

Table of Contents

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