Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is lean, executable, and well-structured, but the export workflow lacks validation checkpoints that would catch an empty filter result or a failed export.
Suggestions
Add a verification checkpoint after filtering — e.g. assert or print len(df_filtered) and warn if it is 0 — so an empty filter does not silently produce a blank Excel file.
Add a post-export check that the output file exists and report its row count (e.g. re-read filtered_result_output.xlsx and print its shape) to confirm the export succeeded.
Surface the key placeholders (target_sheet, filter_col, target_value, selected_cols) in a short upfront list so Claude knows which values must be substituted before running either step.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean — two labeled steps, a one-line navigation note, and executable code with minimal comments — with no padding explaining concepts Claude already knows, so every token earns its place. | 3 / 3 |
Actionability | Both steps give real executable pandas code with column-existence guards, and the placeholder values are explicitly flagged for substitution (e.g. '# 替换为实际sheet名'), which is justified flexibility rather than pseudocode. | 3 / 3 |
Workflow Clarity | The two steps are clearly sequenced, but there are no validation checkpoints — the filter could silently yield zero rows and the export is never verified — which per the rubric caps an export/batch workflow at 2. | 2 / 3 |
Progressive Disclosure | It is a simple, well-organized two-step skill with clearly signaled one-level navigation to the parent workflow SKILL.md, and no bundle files are needed, so the structure is clean and easy to navigate. | 3 / 3 |
Total | 11 / 12 Passed |