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 content delivers executable, code-first guidance across a clear three-step sequence and stays concise, but lacks validation/verification checkpoints for its batch and file-writing operations and leans on a few undefined variables and placeholders.
Suggestions
Add validation checkpoints between steps, e.g. verify category_counts is non-empty before plotting and confirm output files exist after savefig/to_excel.
Define or note the assumed context variables (combined_df, output_dir) and import os so the code blocks are copy-paste runnable.
Add a brief error-recovery note for Step2 (e.g., what to do when '限值' parsing fails or target_val cannot be coerced).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is code-forward and assumes Claude knows pandas/matplotlib, with only brief functional comments; minor placeholder comments ('占位示例', '替换为实际列名') keep it just shy of fully lean. | 4 / 5 |
Actionability | Three concrete, mostly-executable Python blocks cover the common cases, but they rely on assumed context (combined_df, output_dir, unimported os) and placeholders that the user must replace, leaving minor gaps. | 4 / 5 |
Workflow Clarity | Steps are clearly sequenced (Step1/Step2/Step3), but there are no validation checkpoints for batch row-iteration and file writing, which per the batch-operation cap limits workflow clarity to 3. | 3 / 5 |
Progressive Disclosure | The skill is self-contained in one file with a clearly signaled one-level reference to the parent workflow SKILL.md and three organized step sections; no bundle files exist to require deeper splitting. | 4 / 5 |
Total | 15 / 20 Passed |