Content
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is a tight, fully executable four-step pandas workflow with good organization and no concept-explanation padding. Its main weakness is the absence of validation/verification checkpoints for a batch file-processing operation.
Suggestions
Add validation checkpoints after risky batch steps — e.g., assert `extracted_data` is non-empty before dedup, and confirm the exported files exist after `to_excel`.
Remove the duplicated `import pandas as pd` in Steps 2 and 4 and trim obvious restating-the-obvious comments to improve token efficiency.
Add minimal error handling around `pd.read_excel` (missing file / wrong sheet name) so the workflow fails fast with a clear message.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient with short step labels and executable code, but `import pandas as pd` is repeated in Steps 2 and 4 and a few comments restate the obvious ('# 使用 set 进行高效去重', '# 转换为排序后的列表'). | 2 / 3 |
Actionability | All four steps provide fully executable, copy-paste-ready pandas code with concrete column indices, keyword lists, and output paths — no pseudocode or missing details. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced (Step1–Step4) but this batch read/process/write operation has no validation or verification checkpoints (no check that exports succeeded, no empty-result guard), capping clarity at 2. | 2 / 3 |
Progressive Disclosure | A simple under-50-line single-task skill with no external bundle files; content is cleanly organized into labeled steps with only a one-level pointer to the parent workflow SKILL.md. | 3 / 3 |
Total | 10 / 12 Passed |