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 a well-sequenced, code-forward workflow with concrete executable examples and efficient use of tokens. Its main weakness is the absence of validation/verification checkpoints in a batch export pipeline, which caps workflow clarity.
Suggestions
Add validation checkpoints between steps — e.g., assert df is non-empty after dropna, verify output_excel_path and output_img_path exist after export — to support error recovery.
Replace placeholder column names ('分类字段', '数值字段', '分组字段') with a short note on how to bind them to the user's actual columns, or show the binding explicitly.
Clarify cross-step variable continuity (counts, proportions, cross_analysis, stats) so a reader knows which artifacts each later step depends on.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is code-forward with brief per-step Chinese instructions and minimal prose; it assumes Claude's competence and avoids explaining concepts Claude already knows, with only minor inline comments that could be trimmed. | 4 / 5 |
Actionability | Each step ships concrete, mostly executable pandas/matplotlib code with specific calls (ffill, crosstab, subplots, savefig), but placeholder column names ('分类字段', '数值字段') and cross-step variable reuse keep it just short of copy-paste ready. | 4 / 5 |
Workflow Clarity | Step1–Step5 are clearly sequenced, but this batch data-processing/export workflow has no validation checkpoints (e.g., verifying df is non-empty after dropna, confirming export succeeded), which caps workflow clarity at 3 per the batch-operation rule. | 3 / 5 |
Progressive Disclosure | The single self-contained file is well-organized into clearly delineated Step1–Step5 sections with no nested references; structure is good though all content lives inline with no separate reference files. | 4 / 5 |
Total | 15 / 20 Passed |