Content
72%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 lean, actionable, and well-structured with a clear parent-reference, but the two-step workflow lacks an explicit validation checkpoint and error-recovery loop before writing the batch output file.
Suggestions
Add an explicit validation checkpoint between Step1 and Step2 (e.g., confirm result_df is non-empty and the unit-consistency pass rate is acceptable before saving) plus an error-recovery loop, since this writes a batch output file.
Make the code copy-paste ready by including `import pandas as pd` and showing how `data` is obtained, or state explicitly that these are supplied by the parent workflow.
Replace the placeholder column names with one concrete worked example (real columns and sample values) so the executable path is unambiguous.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean code with a brief orienting note and no padding about Parquet/pandas; minor redundant labeling comments ('# 确定最终输出的数据框', '# 生成下载链接') could be trimmed. | 4 / 5 |
Actionability | Concrete pandas code with real methods (sort_values, to_excel), but it uses placeholder columns ('numerator_col', 'group_col') and assumes `data`/`pd` already exist, leaving minor gaps for copy-paste use. | 4 / 5 |
Workflow Clarity | A clear Step1→Step2 sequence exists and Step1 has an internal unit-consistency check, but there is no workflow-level validation checkpoint or error-recovery loop before the batch file write, which caps workflow clarity at 3. | 3 / 5 |
Progressive Disclosure | Under ~50 lines and single-purpose, with well-organized Step1/Step2 sections and a clearly signaled one-level reference to 'the parent workflow SKILL.md'; no external bundle files are needed. | 5 / 5 |
Total | 16 / 20 Passed |