Content
82%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.
A high-quality, code-dense skill body that is executable and well-structured with useful validation/error-recovery reference tables. It is not a destructive or batch operation, so the workflow-clarity cap does not apply; main improvement room is in trimming minor redundant prose and optionally splitting advanced workflows into reference files.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Efficient, mostly code with brief section intros and assumes Claude's competence; only minor instances of restating capabilities in the Overview that could be trimmed. | 4 / 5 |
Actionability | Fully executable, copy-paste-ready code across four workflows (scipy curve_fit, lmfit with bounds/CI, AIC/BIC comparison, Jacobian error propagation) covering the common physics-fitting cases. | 5 / 5 |
Workflow Clarity | Four numbered core workflows give a clear sequence of approaches, and the Goodness-of-Fit table plus Common Pitfalls table provide interpretation checkpoints and error-recovery guidance; minor gaps in explicit validate-then-proceed sequencing. | 4 / 5 |
Progressive Disclosure | Well-organized single-file structure (Overview, When/Not to Use, Core Workflows, reference tables) with clear section headers and no nested references; the large code blocks could arguably be split into reference files but placement is reasonable. | 4 / 5 |
Total | 17 / 20 Passed |