lasso-logistics-analysis

Use when building a binary classification model from an expression matrix or other omics feature matrix with LASSO logistic regression, cross-validation, and coefficient path visualization. NOT for: multiclass classification, survival/Cox models, or ordinary linear regression.

Quality

89%

Does it follow best practices?

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Securityby

Passed

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Quality

Content

86%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 highly actionable, well-organized skill body with executable commands, a clear sequenced workflow, and clean progressive disclosure into verified bundle files. The main improvement opportunity is reducing redundancy (duplicated navigation directives) and removing the meta 'Implementation Checklist'.

Suggestions

Remove or relocate the 'Implementation Checklist' — it is a development artifact and does not help Claude execute the skill.

Deduplicate the navigation guidance: keep either the top 'When to Read External Files' table or the per-step 'WHEN... READ' directives rather than repeating both, to tighten token use.

Add an explicit validation gate in the workflow (e.g., 'Only proceed to modeling when input validation passes') to strengthen the feedback loop and lift workflow clarity.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with compact tables and copy-paste commands rather than prose padding, but there is redundancy between the top navigation table and the per-step 'WHEN... READ' directives, and the 'Implementation Checklist' is a dev artifact that could be trimmed; not a 3 because the bulk is lean and accurate.	4 / 5
Actionability	Fully executable Rscript commands with all flags, plus four concrete example invocations (basic, feature panel, custom folds/seed, custom titles) and sample input CSVs — copy-paste ready and covering the common cases, matching the top anchor.	5 / 5
Workflow Clarity	A clear four-step sequence (validate → prepare → fit → save) with input validation as step 1 and an error-code-to-solution table plus troubleshooting fallback as a feedback loop, but it lacks an explicit 'only proceed when valid' gate inline, so it falls short of the top anchor.	4 / 5
Progressive Disclosure	A clear overview with a 'When to Read External Files' table signaling one-level-deep references (verified to exist in references/ and scripts/), with content appropriately split across modular files, matching the top anchor for easy navigation.	5 / 5
	Total	18 / 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-structured description that states concrete capabilities, gives an explicit 'Use when' trigger, and sharply scopes the skill with a 'NOT for' clause. The only minor gap is the absence of file-extension trigger terms.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — building a binary classification model, LASSO logistic regression, cross-validation, and coefficient path visualization — giving comprehensive coverage of the skill's capabilities, matching the anchor for several specific concrete actions.	5 / 5
Completeness	Explicitly answers both 'what' (LASSO logistic regression with cross-validation and coefficient path visualization) and 'when' via a concrete 'Use when...' clause, satisfying the top anchor; not a 4 because the trigger guidance is explicit and specific.	5 / 5
Trigger Term Quality	Good keyword coverage with natural domain terms ('binary classification model', 'expression matrix', 'LASSO logistic regression') and a synonym ('omics feature matrix'), but lacks file-extension triggers like .csv/.tsv that users might mention, so it sits just below comprehensive.	4 / 5
Distinctiveness Conflict Risk	A clear niche (LASSO logistic regression on omics matrices) reinforced by an explicit 'NOT for' exclusion list (multiclass, survival/Cox, ordinary linear regression) minimizes conflict with adjacent skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: aipoch/medical-research-skills
Path: awesome-med-research-skills/Data Analysis/lasso-logistics-analysis/SKILL.md
Commit: f5ef65b

Reviewed: ~11 hours ago

Table of Contents

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