Content
57%Reviews 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-organized, mostly executable six-step pipeline, but it relies on placeholder values, includes commented-out scaffolding, and lacks the validation checkpoints expected for batch data operations.
Suggestions
Replace placeholder thresholds and column names with concrete, runnable examples (or document explicitly how the user should parameterize them) so the code is copy-paste ready.
Add validation checkpoints (e.g. assert the sheet loaded with expected columns, check for empty results after cleaning, verify the output file was written) with a fix-and-retry loop for the batch operations.
Remove commented-out scaffolding lines or move them into a clearly labeled optional/advanced section to tighten token efficiency.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly dense code with brief step headers and no concept explanations, but it carries placeholder scaffolding (commented-out logic like '# df[\'group_col\'] = ...' and '# df[\'score\'] = ...') that could be tightened, so it is not the fully lean level 3. | 2 / 3 |
Actionability | Real executable pandas/matplotlib/seaborn functions are provided, but key parts use placeholders the user must substitute (target_col/group_col) and example thresholds marked '占位示例', so it is incomplete rather than copy-paste ready level 3. | 2 / 3 |
Workflow Clarity | The six steps are clearly sequenced Step1→Step6, but the batch/data workflow has no validation checkpoints or error-recovery loops; per the guideline missing validation for batch operations caps workflow clarity at 2. | 2 / 3 |
Progressive Disclosure | The skill is a single concise SKILL.md with no bundle files and is well-organized into six labeled steps; per the guideline a short single-file skill with no external-reference need can score 3 on well-organized sections alone. | 3 / 3 |
Total | 9 / 12 Passed |