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 skill provides concrete, executable code across a clear three-step sequence but lacks validation checkpoints and has a variable-continuity gap between Step1 and Step2. Structure is reasonably well organized for a single-file sub-skill.
Suggestions
Add validation/verification checkpoints, e.g., assert the output file exists or handle empty target rows, to lift workflow clarity above 3.
Fix the data-flow gap: Step2 uses `df` but Step1 only assigns `df_temp`; explicitly carry the relevant DataFrame forward or reload it.
Remove a few redundant inline code comments (e.g., '# 计算平均值') to tighten conciseness.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly executable code with brief step introductions and avoids explaining concepts Claude already knows, though a few inline comments like '# 计算平均值' and '# 定义样式' could be trimmed. | 4 / 5 |
Actionability | Three complete, mostly copy-paste-ready Python blocks cover the task, but Step2 references an undefined `df` (Step1 only defines `df_temp`) and uses a placeholder target entity, leaving minor gaps. | 4 / 5 |
Workflow Clarity | Step1→Step2→Step3 are clearly sequenced, but there are no validation or verification checkpoints (e.g., confirming the output file or checking for empty data), which caps this dimension. | 3 / 5 |
Progressive Disclosure | Content is organized into a Skill Steps section with three labeled steps and a clear note pointing to the parent workflow; no bundle files exist, and the inline structure is appropriate with only minor organization gaps. | 4 / 5 |
Total | 15 / 20 Passed |