Content
87%Weight 40%Scale 1-3Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is a lean, highly actionable reference with executable code, decision tables, and APA templates, well-organized into clear sections. Its main gap is workflow clarity: the worked example does not enforce assumption checks as explicit validation branches.
Suggestions
Turn the analysis flow into an explicit validation-gated workflow: after the normality/variance check, branch to the non-parametric alternative when assumptions fail, rather than printing the t-test unconditionally.
Add a brief "if p is non-significant, report effect size and CI; do not claim no effect" checkpoint in the worked example to close the implicit feedback loop.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean reference material — decision tables, executable code, effect-size thresholds, and APA templates — with no padding explaining basic concepts Claude already knows. | 3 / 3 |
Actionability | Provides copy-paste-ready executable Python (scipy, pingouin), concrete test-selection tables, effect-size cutoffs, and filled-in APA report templates — fully actionable rather than descriptive. | 3 / 3 |
Workflow Clarity | A sequence exists (descriptives → normality → test) and a checklist is present, but validation checkpoints are implicit: the example runs the t-test regardless of the normality result, with no explicit branch or feedback loop. | 2 / 3 |
Progressive Disclosure | Content is organized into eight clearly headed sections with no nested or multi-level references; as a self-contained reference skill with no bundle files, well-organized sections satisfy progressive disclosure. | 3 / 3 |
Total | 11 / 12 Passed |