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 and actionable with a clean three-step structure, but it lacks validation checkpoints for a batch data operation and relies on a fragile assumed Parquet path.
Suggestions
Add validation checkpoints (e.g. verify df is non-empty after read, warn if the filter matches zero rows) to lift workflow clarity above the batch-operation cap of 3.
Replace the assumed 'auto_converted_data.parquet' path with explicit discovery of the generated Parquet file, or document the contract with the parent skill.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient code with brief, useful comments; minor verbosity from print statements like '已使用 Parquet 格式加速读取大文件。' that could be trimmed. | 4 / 5 |
Actionability | Provides concrete, mostly executable Python for each step; minor gaps are the placeholder values for target_col/group_col/target_value and the assumed 'auto_converted_data.parquet' path. | 4 / 5 |
Workflow Clarity | Three steps are clearly sequenced, but this batch data-processing workflow has no validation/verification checkpoints (e.g. confirming the dataframe loaded or the filter matched), capping workflow clarity at 3. | 3 / 5 |
Progressive Disclosure | A short (<50 line) single-purpose skill with well-organized Step1/Step2/Step3 sections and a clear pointer to the parent workflow; no external references are needed. | 5 / 5 |
Total | 16 / 20 Passed |