regression-modeler

Run regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating coefficients, R², p-values, VIF, and plain-language interpretation. Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit, coefficient, p-value, or R-squared.

Quality

100%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

Quality

Content

100%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A tight, well-structured CLI skill body that documents parameters, output, and dependencies with executable examples and no concept padding. The single bundled script is referenced cleanly at one level of depth.

Dimension	Reasoning	Score
Conciseness	Lean reference-style body (capabilities table, quick-start, parameters, output schema, dependencies) with no explanation of concepts Claude already knows; every section earns its place. Not the level below because there is no padded prose or redundant tutorializing.	3 / 3
Actionability	Copy-paste-ready commands like "python3 scripts/regression_analyzer.py data.csv --target price" plus a full parameters table and a concrete JSON output schema provide fully executable guidance.	3 / 3
Workflow Clarity	This is a simple single-command skill whose single action (run the analyzer with --target and options) is unambiguous, so workflow clarity qualifies for 3 per the simple-skills note; the operation is read-only analysis, so no destructive/batch validation checkpoint is required.	3 / 3
Progressive Disclosure	Well-organized sections form a clear overview, and the one bundle file (scripts/regression_analyzer.py) is referenced one level deep and confirmed present on disk, with no nested reference chains.	3 / 3
	Total	12 / 12 Passed

Description

100%

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 concise, third-person description that clearly states concrete capabilities and provides explicit trigger guidance with strong keyword coverage. It cleanly answers both what the skill does and when to invoke it.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — "generating coefficients, R², p-values, VIF, and plain-language interpretation" — and names both regression flavors (OLS/logistic), matching the score-3 anchor of multiple specific concrete actions.	3 / 3
Completeness	Explicitly answers both what ("Run regression analysis... generating coefficients, R², p-values, VIF...") and when ("Triggered by requests for regression modeling... or keywords like..."), with an explicit trigger clause.	3 / 3
Trigger Term Quality	Covers natural user phrasing — "regression modeling, fitting data, testing significance, checking multicollinearity" plus keywords "OLS, logit, coefficient, p-value, or R-squared" — giving good coverage of terms a user would actually say.	3 / 3
Distinctiveness Conflict Risk	A clear regression-analysis niche with distinct technical triggers (OLS, logit, VIF, p-value) makes it unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 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: zebbern/claude-code-guide
Path: skills/regression-modeler/SKILL.md
Commit: af7d491

Reviewed: about 7 hours ago

Table of Contents

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