Conciseness

The body is efficient — a capabilities table, copy-paste commands, a parameter table, and an output example — with no explanations of regression concepts Claude already knows. Not score 5 because 'Detailed Usage' repeats material already shown: the '--type' examples and the '-f "sqft,bedrooms,bathrooms"' feature example duplicate Quick Start and the Parameters table ('Omit to automatically use all numeric columns' restates the '--features' default).

Actionability

Fully executable, copy-paste-ready commands covering the common cases: 'python3 scripts/regression_analyzer.py data.csv --target price', the logistic case with '--features "age,income,tenure"', forcing '--type linear/logistic', and '--output result.json'. A complete parameter table with defaults and a concrete JSON output structure leave no gaps on how to invoke the tool.

Workflow Clarity

This is a single-command tool skill where the single action is unambiguous: pick target, optionally pick features/type, run the script, read the JSON output — and auto-detection ('Automatically switches to logistic regression when the target is binary (0/1)') resolves the main decision point. Not score 5 because there are no checkpoints for the failure modes a user would hit (e.g., what to do about multicollinearity warnings or non-numeric targets) — though the operation is neither destructive nor batch, so the ≤3 cap does not apply.

Progressive Disclosure

Good structure with clear sections (Capabilities, Quick Start, Detailed Usage, Parameters, Output Structure, Dependencies), and the referenced bundle file 'scripts/regression_analyzer.py' exists as a single flat reference. All content is appropriately inline at this size with no nested or multi-level references. Not score 5 because the 'Detailed Usage' section partially duplicates Quick Start instead of consolidating — a minor organization gap.

Specificity

Quotes multiple concrete actions: 'Run regression analysis (OLS or logistic)', 'generating coefficients, R², p-values, VIF, and plain-language interpretation'. This is comprehensive coverage of the tool's outputs, matching the anchor 'Lists multiple specific concrete actions; comprehensive coverage' — not score 4, which expects minor gaps in coverage, and the description names both regression types and every key output.

Completeness

Explicitly answers both questions: 'what' via 'Run regression analysis (OLS or logistic)... generating coefficients, R², p-values, VIF, and plain-language interpretation' and 'when' via 'Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit...'. This matches the anchor 'Clearly and explicitly answers both what AND when with concrete trigger phrases'; it is not score 4 because the 'when' clause is already fully explicit, not merely present.

Trigger Term Quality

Good keyword coverage including synonyms: 'regression modeling, fitting data, testing significance, checking multicollinearity' plus 'OLS, logit, coefficient, p-value, or R-squared' (both 'R²' and 'R-squared' variants). Not score 5 because a few natural terms users would say are missing, e.g. 'linear regression', 'multiple regression', 'predict', and explicit file extensions like '.csv'/'.xlsx' — it only says 'CSV/Excel data'.

Distinctiveness Conflict Risk

The niche is clear with mostly distinct technical triggers (OLS, logit, VIF, multicollinearity) that a general data-analysis or visualization skill would not claim. Not score 5 because 'fitting data' and 'testing significance' are somewhat broad phrases with minor overlap risk against general statistics/data-analysis skills; not score 3 because the dominant triggers are highly specific to regression.