Content
66%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 skill delivers concrete, mostly executable code in a clear three-step sequence and is appropriately concise for a self-contained skill. The main gap is the absence of validation/verification steps around the Excel write and the placeholder column names in the analysis step.
Suggestions
Add a validation checkpoint after saving the report (e.g. re-open the workbook to confirm the highlighted row and headers rendered correctly) to lift workflow clarity above 3.
Replace the placeholder column names in Step 2 with a concrete example dataset and note how to detect the target columns generically, so the code is copy-paste ready.
Guard the Parquet branch for the pyarrow dependency and the multi-sheet case (currently only sheet 0 is converted even when total_rows spans all sheets).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient: each step opens with a terse instruction and follows with focused code, with only minor padding such as the placeholder comments in Step 2 that could be tightened. | 4 / 5 |
Actionability | Provides concrete, largely executable pandas and openpyxl code with real style definitions and output paths, but Step 2 uses placeholder column names ('分类列名', '目标数值列') so it is not fully copy-paste ready for a given dataset. | 4 / 5 |
Workflow Clarity | Steps are sequenced (read/convert, group-by analysis, write/format), but there are no validation checkpoints for the batch write operation and no error-recovery feedback loop, which caps the score at 3 per the rubric. | 3 / 5 |
Progressive Disclosure | A single self-contained SKILL.md with clear Step1/Step2/Step3 headers and no bundle files; for a focused single-purpose skill this is well organized, though the all-inline code blocks could be slightly better sectioned. | 4 / 5 |
Total | 15 / 20 Passed |