Content
53%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 information-rich and gives concrete data paths, formulas, and output templates, but it is monolithic and verbose, inlining example reports and algorithm explanations that should live in separate reference files. Workflow sequencing and validation loops are also underspecified.
Suggestions
Move the full example report templates and the algorithm/formula reference into separate files under references/ and link to them from SKILL.md to fix progressive disclosure.
Replace the fabricated sample numbers in inline reports with brief structural sketches, and drop re-explanations of Pearson/regression that Claude already knows.
Add an explicit numbered analysis workflow (load data → quality check → analyze → validate findings → render report) with a validate/retry loop on data-quality failures.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly structured but padded: three full sample report templates with fabricated numbers and re-explanations of Pearson correlation / linear regression formulas Claude already knows could be trimmed. | 3 / 5 |
Actionability | Provides concrete data-source paths (data/fitness-logs/YYYY-MM/YYYY-MM-DD.json, data/fitness-tracker.json), a MET table, formulas, example commands (/fitness trend 3months) and output templates — mostly executable with only minor gaps. | 4 / 5 |
Workflow Clarity | Features and a data-quality checklist are listed, but there is no end-to-end sequenced analysis workflow with explicit validate→fix→retry checkpoints, fitting 'steps listed but validation gaps'. | 3 / 5 |
Progressive Disclosure | The 430-line body is monolithic with no bundle files and no external references; the full example report templates and algorithm reference clearly belong in separate files but are fully inlined. | 2 / 5 |
Total | 12 / 20 Passed |