Content
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill is well-structured and token-efficient with executable code and a final validation step, but it delegates its core Parquet-conversion premise to another skill and leaves decision branches (sub-threshold files, missing metric) unhandled, weakening actionability and workflow clarity.
Suggestions
Add the actual Excel→Parquet conversion code instead of assuming another skill already produced converted_data.parquet, so the skill delivers its advertised capability end-to-end.
Provide an explicit branch for when total_rows < 10000 (e.g. process directly without Parquet) and a user-facing message when target_rows is empty, so the workflow has feedback at each decision point.
Replace the '目标指标名称' placeholder with guidance on how to identify the target metric from the data, or accept it as a parameter with a worked example.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean: brief step descriptions plus focused code, with comments that add non-obvious value (e.g. '仅读取一列以加快行数统计速度') rather than explaining concepts Claude already knows. | 3 / 3 |
Actionability | Real executable Python is shown, but the headline Excel→Parquet conversion is only assumed ('假设已通过大文件处理技能将Excel转换为Parquet') and the target metric is a placeholder ('目标指标名称'), leaving a key detail incomplete. | 2 / 3 |
Workflow Clarity | Steps are clearly sequenced and Step3 includes an output verification, but there is no feedback loop or explicit handling for the <10000-row branch or the metric-not-found case, leaving checkpoints implicit. | 2 / 3 |
Progressive Disclosure | It is a self-contained, single-purpose skill organized into three clear step sections with no need for external references, satisfying the well-organized-sections allowance for simple skills. | 3 / 3 |
Total | 10 / 12 Passed |