Content
68%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 executable and well-structured into a clear four-step pipeline, but it over-claims multi-sheet handling while only processing the first sheet, and lacks a validation checkpoint for the batch report output. Tightening these would raise the lower dimensions.
Suggestions
Add a validation checkpoint before export, e.g. assert summary_df is non-empty and that total_val > 0, and surface a clear error otherwise.
Actually iterate over all sheets for the aggregation, or explicitly document that only the first sheet is analyzed, to reconcile the '多Sheet' claim with the code.
Either use is_large_file to branch the processing strategy or remove it to avoid dead code.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean step-plus-code structure with no padding about what pandas or pie charts are; a few inline comments could be trimmed but overall efficient. | 4 / 5 |
Actionability | Provides fully executable Python across all four steps, but is_large_file is computed and never used and only sheet_names[0] is analyzed despite the '多Sheet' claim, leaving minor gaps. | 4 / 5 |
Workflow Clarity | Four steps are clearly sequenced, but this batch report-generation workflow has no explicit validation/verification checkpoint before producing the downloadable output, which caps workflow clarity at 3. | 3 / 5 |
Progressive Disclosure | Self-contained SKILL.md with no bundle files and no nested references; the four labeled steps provide good organization, with only minor header-formatting gaps. | 4 / 5 |
Total | 15 / 20 Passed |