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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

87%

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Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

81%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-structured CLI skill: executable commands everywhere, a validated four-step workflow with error-recovery loops, and strong progressive-disclosure navigation via the 'When to Read' table and WHEN pointers. The deductions come from padding (dev checklist, version footer, textbook Methods section) and a dangling tests/data reference that would fail if a user followed the Testing section verbatim.

Suggestions

Remove the 'Implementation Checklist' and the 'Last updated: 2026-04-17 | Version: 1.0.0' footer — they are development bookkeeping and time-sensitive metadata with no operational value for using the skill.

Trim the 'Methods' section — LASSO L1-penalty mechanics and cv.glmnet behavior are knowledge Claude already has; keep only the pointer to references/algorithm.md.

Fix the dangling `tests/data/` references — the bundle contains no tests directory, so either ship the sample data files or remove that table row and the test commands in the Testing section; consider also moving the 15-row error table into references/troubleshooting.md to reduce duplication.

DimensionReasoningScore

Conciseness

The core sections (arguments, input/output formats, examples) are tight and table-driven, but there are several padded sections: the 13-line "Implementation Checklist" is developer bookkeeping, the "Last updated: 2026-04-17 | Version: 1.0.0" footer is time-sensitive info outside any deprecated section, the "Methods" section re-explains textbook LASSO/L1-penalty mechanics Claude already knows, and the 15-row error table duplicates references/troubleshooting.md. Matches the 'mostly efficient but includes some unnecessary explanation' anchor rather than 4, where over-explanation would be only minor.

3 / 5

Actionability

Fully executable throughout: a complete copy-paste-ready `Rscript scripts/main.R` usage block plus four runnable example commands, an arguments table with types/defaults/required flags, concrete CSV input-format samples, an output-files table, and test/validation commands. Matches the top anchor — commands cover the common cases (basic, feature panel, custom folds/seed, custom titles).

5 / 5

Workflow Clarity

A clear 4-step sequence (Validate Input → Prepare Matrix → Fit → Save Results) where Step 1 is explicit validation, a cause→solution error table, an explicit recovery loop ("IF error persists, READ: references/troubleshooting.md"), and post-run verification commands in the Testing section. This is not a destructive/batch operation, so no cap applies, and it matches the anchor with explicit validation steps and feedback loops rather than the 4 anchor with minor gaps.

5 / 5

Progressive Disclosure

The 'When to Read External Files' table up front plus WHEN-signaled pointers throughout give clear one-level-deep navigation, and the bundle confirms every referenced references/ and scripts/ file exists. Not a 5 because scored against the actual bundle: the `tests/data/` paths referenced in the table and Testing section do not exist (no tests directory in the bundle), and the inline 15-row error table duplicates content that belongs in references/troubleshooting.md. Not a 3 because structure, signaling, and file placement are otherwise good.

4 / 5

Total

17

/

20

Passed

Description

87%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 description: it pairs an explicit 'Use when' trigger with a precise statement of what the skill does, and the 'NOT for' clause sharply reduces mis-triggering against adjacent statistical skills. Only minor gaps — unmentioned feature selection/outputs and a few missing natural synonyms like 'glmnet' or 'feature selection'.

DimensionReasoningScore

Specificity

Names the domain ("expression matrix or other omics feature matrix") and several concrete actions ("building a binary classification model", "LASSO logistic regression, cross-validation, and coefficient path visualization"), which matches the anchor for several specific actions. Not a 5 because capability coverage is not comprehensive — feature selection and saved outputs are unmentioned; not a 3 because it lists more than 1-2 concrete actions with a precise input specification.

4 / 5

Completeness

Explicitly answers both: what ("building a binary classification model ... with LASSO logistic regression, cross-validation, and coefficient path visualization") and when ("Use when building a binary classification model from an expression matrix or other omics feature matrix"), with concrete trigger phrases. Matches the top anchor exactly; a 4 would require the 'when' to be less explicit, which it is not.

5 / 5

Trigger Term Quality

Good natural keyword coverage: "binary classification", "LASSO logistic regression", "cross-validation", "expression matrix", "omics", "feature matrix" — terms a bioinformatics user would actually say. Not a 5 because a few natural terms/synonyms are missing (e.g., "feature selection", "glmnet", file extensions like .csv); not a 3 because coverage goes well beyond some relevant keywords.

4 / 5

Distinctiveness Conflict Risk

Clear niche with explicit exclusion boundaries ("NOT for: multiclass classification, survival/Cox models, or ordinary linear regression") that disambiguate the closest neighboring skills. Minimal conflict risk; matches the top anchor, and no neighboring anchor fits better.

5 / 5

Total

18

/

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.

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

Reviewed

Table of Contents

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