Content
63%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is well-organized and actionably written, with executable outlier-detection code, a practical test-selection table, and business-oriented reporting guidance. Its main weaknesses are re-teaching statistics Claude already knows (hurting token efficiency) and keeping everything in one long file with no progressive disclosure via reference files.
Suggestions
Trim or drop the definitional explanations of concepts Claude already knows (what standard deviation/IQR are, the null-hypothesis framework, alpha=0.05) and keep only the applied decision guidance — e.g., replace the hypothesis-testing framework walkthrough with just the test-selection table and interpretation rules.
Split the self-contained sections into one-level-deep reference files (e.g., references/outlier-detection.md, references/hypothesis-testing.md, references/statistical-pitfalls.md) and keep SKILL.md as a concise overview with clearly signaled links.
Add small runnable snippets for the trend/forecasting guidance (e.g., a 3-line seasonal-naive or linear-trend forecast in pandas) so those sections are as executable as the outlier-detection code.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body spends tokens re-teaching textbook statistics Claude already knows ("Standard deviation: How far values typically fall from the mean", "Null hypothesis (H0): There is no difference", "Choose significance level (alpha): Typically 0.05"), alongside genuinely valuable applied guidance ("Always report mean and median together for business metrics"). This fits 'mostly efficient but includes some unnecessary explanation or could be tightened' — not level 2 because the majority is application/reporting guidance rather than padding. | 3 / 5 |
Actionability | Concrete, executable Python snippets are provided for outlier detection (z-score, IQR, percentile) and moving averages ("df['ma_7d'] = df['metric'].rolling(window=7, min_periods=1).mean()"), plus a concrete test-selection table. Anchor 4 fits: mostly executable with minor gaps, since the forecasting and hypothesis-testing sections give prose guidance rather than runnable code. | 4 / 5 |
Workflow Clarity | Multi-step processes are clearly sequenced ("1. Compute expected value... 4. Distinguish between point anomalies and change points"; the outlier handling decision tree "Investigate: Is this a data error, a genuine extreme value, or a different population?") with reporting checkpoints ("Report what you did"). Not level 5 because there are no explicit validate-and-recover feedback loops, though nothing destructive/batch applies the level-3 cap. | 4 / 5 |
Progressive Disclosure | The skill has no bundle files — all ~245 lines live inline in SKILL.md with good section headers. It exceeds the <50-line simple-skill exception, and substantial content (hypothesis testing basics, the caution section on statistical claims) clearly belongs in separate reference files, matching anchor 3: 'some structure... content that should be separate is inline'. | 3 / 5 |
Total | 14 / 20 Passed |