study-design-scale-selector

Determines the appropriate Risk of Bias assessment scale for a medical study based on its design (RCT, Cohort, etc.), using PubMed metadata lookup or text analysis. Use when the user wants to know which quality assessment tool to use for a specific paper (given PMID or abstract).

Quality

67%

Does it follow best practices?

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Securityby

Low

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tessl review fix ./scientific-skills/Data Analysis/study-design-scale-selector/SKILL.md

Quality

Content

52%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 core workflow is sound and well-sequenced with a sensible PMID-to-text fallback and an appropriately separated reference file. The main weakness is token efficiency: a large fraction of the body is generic templated boilerplate that does not teach Claude anything skill-specific, and the PDF extraction section is incomplete.

Suggestions

Remove or collapse the generic boilerplate sections ('When to Use', 'Key Features', 'Implementation Details', 'Output Contract', 'Validation and Safety Rules', 'Failure Handling') that restate generic skill guidance and add no study-design-specific value, keeping only the Workflow, Dependencies, and scale_rules reference.

Complete the 'PDF Text Extraction' section with an actual executable command (e.g. 'python scripts/extract_pdf.py paper.pdf --output extracted.txt') instead of a header followed by no example.

Replace the stale date-stamped path in 'Example Usage' (cd "20260316/scientific-skills/...") with a path-agnostic invocation, and tighten the text-analysis fallback with a more concrete identification procedure rather than only a keyword list.

Dimension	Reasoning	Score
Conciseness	The body is noticeably padded with generic boilerplate sections ('When to Use', 'Key Features', 'Implementation Details' which only says 'See ## Workflow above', 'Output Contract', 'Validation and Safety Rules') that add no skill-specific value, fitting the score-2 anchor of several unnecessary padded sections rather than the mostly-efficient score-3.	2 / 5
Actionability	The main path is executable ('python scripts/selector.py "<PMID>"', a concrete scale_rules.md table, a JSON output schema), but the text-analysis fallback is only keyword hints and the PDF extraction section header promises a command yet provides none, leaving key details incomplete per the score-3 anchor.	3 / 5
Workflow Clarity	Steps 1-4 are clearly sequenced with a conditional branch (PMID -> selector -> skip to step 3) and an explicit fallback when the script returns empty JSON or fails, matching the score-4 anchor of a clear sequence with most checkpoints present; the skill is read-only so the destructive/batch cap does not apply.	4 / 5
Progressive Disclosure	scale_rules.md is a real, one-level-deep reference clearly signaled via a markdown link, and both scripts are referenced from the overview, giving good structure; however the inlined generic boilerplate keeps it just below the cleanly-split score-5 anchor.	4 / 5
	Total	13 / 20 Passed

Description

82%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 is well-constructed: it states a specific capability and pairs it with an explicit, concrete 'Use when...' trigger clause anchored to real inputs (PMID, abstract). It is distinctive and unlikely to fire for unrelated skills. The only mild gap is action breadth, which is adequate rather than comprehensive.

Dimension	Reasoning	Score
Specificity	Names the domain and a couple concrete actions ('Determines the appropriate Risk of Bias assessment scale', 'using PubMed metadata lookup or text analysis') but the action set is not comprehensive, matching the score-3 anchor rather than the multi-action score-4 anchor.	3 / 5
Completeness	Explicitly answers both 'what' (determines the appropriate RoB scale for a medical study based on design) and 'when' ('Use when the user wants to know which quality assessment tool to use for a specific paper (given PMID or abstract)') with concrete trigger phrases, matching the score-5 anchor.	5 / 5
Trigger Term Quality	Good natural keyword coverage a researcher would actually say ('quality assessment tool', 'risk of bias', 'PMID', 'abstract', 'RCT', 'Cohort'), though a few synonyms/variants are missing, fitting score 4 rather than the fully comprehensive score 5.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (risk-of-bias scale selection for medical studies) with distinct, specific triggers that minimize overlap with other skills, matching the score-5 anchor.	5 / 5
	Total	17 / 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/Data Analysis/study-design-scale-selector/SKILL.md
Commit: f5ef65b

Reviewed: ~9 hours ago

Table of Contents

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