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 provides mostly executable, reasonably concise code for each step in a clear sequence, but it lacks validation/verification checkpoints for a batch data operation, which caps workflow clarity.
Suggestions
Add validation checkpoints after conversion and cleaning (e.g., verify row counts before/after Parquet conversion, assert the cleaned column is non-empty) and a retry-on-failure loop.
Define the row-count threshold for '数据量过大' so the Parquet-conversion branch is unambiguous instead of left to inference.
Replace the 'target_column' placeholder with guidance on detecting or accepting the target column, and consolidate the repeated pandas imports across steps.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Code blocks are tight with brief inline comments and no padding about pandas/regex basics, though repeated imports and a few comments that restate the step title keep it just below a 5. | 4 / 5 |
Actionability | Each step ships concrete, executable pandas/regex code that is largely copy-paste ready, but placeholders like 'target_column' and an undefined row-count threshold leave minor gaps. | 4 / 5 |
Workflow Clarity | Three steps are clearly sequenced (convert -> clean -> export), but this batch file-writing operation has no validation or verification checkpoints, which caps workflow clarity at 3 per the rubric. | 3 / 5 |
Progressive Disclosure | As a short single-purpose skill with no bundle files, the well-organized header, parent-workflow pointer, and three labeled steps satisfy the simple-skill exception for a top score. | 5 / 5 |
Total | 16 / 20 Passed |