Content
80%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill is concise and highly actionable with executable code and a clear step sequence, but it lacks validation checkpoints and keeps all content inline with no progressive disclosure.
Suggestions
Add explicit validation/verification steps — e.g., assert expected columns exist after loading, check for NaNs after percentage conversion, and confirm the PNG was written before reporting.
Split the long inline report-generation and visualization code into a referenced script (e.g. scripts/generate_report.py) and keep SKILL.md as an overview that points to it.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean — one-line step descriptions plus executable code with only purposeful comments, no padding or explanation of concepts Claude already knows. | 3 / 3 |
Actionability | Each step provides fully executable Python (pandas/numpy/matplotlib/seaborn) that is copy-paste ready; placeholder column names are explicitly flagged as 占位示例. | 3 / 3 |
Workflow Clarity | Five steps are clearly sequenced (Step1–Step5), but there are no explicit validation checkpoints or error-recovery feedback loops — Step1's shape/columns print is only an implicit check. | 2 / 3 |
Progressive Disclosure | Content is organized into stepped sections, but it is a single ~150-line monolithic file with no bundle references; material that could live in separate reference files is all inline. | 2 / 3 |
Total | 10 / 12 Passed |