Content
70%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, actionable skill body with a clear workflow and validation loop. The main gaps are minor redundancy around the core principle and a lack of executable example code in the Python pattern section.
Suggestions
Consolidate the 'restrained/clarity over fancy' guidance into the Core principle section and remove the restatements in Chart selection and the 'Do not…' lines to tighten conciseness.
Replace the descriptive 'Suggested Python pattern' with a minimal executable snippet (plt.style.use with the asset path, figure size, and both vector+png export) so it is copy-paste ready.
Consider moving the surface-class definitions and practical figure-size table into a referenced reference file so SKILL.md stays a lean overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is directive and free of concept-explanation padding, but the 'restrained/not fancy' principle is restated across Core principle, Chart selection, and several 'Do not…' lines, so it could be tightened. | 2 / 3 |
Actionability | Concrete rules, exact figure sizes, and export formats are given, but the 'Suggested Python pattern' is descriptive ('prefer: plt.style.use…, explicit figure size') with no executable copy-paste code block. | 2 / 3 |
Workflow Clarity | The numbered render-inspect-revise sequence and the self-review checklist with an explicit feedback loop ('If any answer is negative, revise before calling complete') provide clear validation checkpoints. | 3 / 3 |
Progressive Disclosure | Sections are well organized, the referenced style asset (assets/deepscientist-academic.mplstyle) is a real one-level bundle file, and the internal-policy references are clearly signaled with no nested indirection. | 3 / 3 |
Total | 10 / 12 Passed |