Content
68%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 content is concrete, executable, and well-structured into five sequenced steps with efficient code. Its main weakness is the absence of validation/verification checkpoints for a batch file-writing workflow, which caps workflow clarity.
Suggestions
Add validation checkpoints: verify the input file/sheets exist before processing, assert total_count > 0 before computing ratios, and confirm the output Excel/PNG files were written successfully.
Fix the Step 2 gap where `df` is referenced but never defined in the preceding snippet — load the target sheet into `df` explicitly so each step is independently runnable.
Replace placeholder values ('category_a_column', exclude_val='代码') with a brief note on how to identify the correct columns and which header placeholders to exclude, improving actionability.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly lean code with task-relevant Chinese comments and no padding about concepts Claude already knows; a few comments and the hardcoded exclude_val could be trimmed, so not a 5. | 4 / 5 |
Actionability | Provides concrete, mostly copy-paste-ready pandas/matplotlib code across all five steps; minor gaps (df is undefined in Step 2, placeholder column names like 'category_a_column') keep it below 5. | 4 / 5 |
Workflow Clarity | Five steps are clearly sequenced, but this batch/file-writing operation has no validation or verification checkpoints (e.g. confirming the file exists, checking output), capping workflow clarity at 3 per the rubric. | 3 / 5 |
Progressive Disclosure | No bundle files exist and none are needed; the body is well-organized into five labeled steps with clear structure, fitting the 'good structure, minor gaps' anchor rather than 5 because it is not a minimal <50-line single-purpose skill. | 4 / 5 |
Total | 15 / 20 Passed |