Content
65%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is highly actionable, providing five complete executable functions for a clean crosstab-to-heatmap-to-export pipeline. Its weaknesses are a lack of validation checkpoints in the batch workflow and a monolithic single-file structure with no overview or external references.
Suggestions
Add validation/checkpoint steps between batch stages (e.g., assert the crosstab is non-empty, verify export sheets before returning the FileLink).
Trim comments that merely restate obvious pandas operations to improve token efficiency.
Add a brief overview/quick-start section at the top showing how the five functions chain together, and consider moving the longer functions into a scripts/ bundle referenced from SKILL.md.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is code-heavy with practical gotcha tips (ffill for merged cells, Chinese font config) and no concept-explanation padding, but several comments restate obvious operations (e.g., '# 清洗字符串空格', '# 计算占比'), so it is 'mostly efficient but could be tightened'. | 2 / 3 |
Actionability | Each step ships a complete, executable Python function with imports (preprocess_pivot_data, create_cross_analysis, generate_analysis_heatmap, extract_performance_insights, export_results), copy-paste ready, matching the 'fully executable code' anchor. | 3 / 3 |
Workflow Clarity | A clear Step1–Step5 sequence exists, but this batch data-processing/export workflow has no validation checkpoints or error-recovery feedback loops, so workflow_clarity is capped at 2 per the batch-operations guideline. | 2 / 3 |
Progressive Disclosure | Content is organized into five labeled steps, but there is no overview/quick-start, no navigation, and all code is inline with no bundle files, matching 'some structure but content that should be separate is inline'. | 2 / 3 |
Total | 9 / 12 Passed |