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 body provides concrete, mostly-executable code organized into a clear four-step pipeline with lean, practical comments. Its main weakness is the absence of validation/verification checkpoints in a data-modifying workflow, which caps workflow clarity. Structure and progressive disclosure are solid for a focused single-file sub-skill.
Suggestions
Insert validation checkpoints between steps — e.g., after cleaning, assert row-count sanity / print df_clean.shape and non-null counts before analyzing; after regression, verify r_value and p_value are finite.
Add a minimal data-loading/setup snippet (read_excel + column selection) so the code blocks are fully copy-paste runnable rather than assuming a pre-existing df.
Optionally move the longer visualization code into a referenced scripts/ file and keep SKILL.md as a tighter overview to approach the top progressive-disclosure anchor.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is efficient: concise Chinese step descriptions plus focused code, with comments that are practical tips (font config, utf-8-sig, jitter) rather than explanations of basic concepts; a few comments like '# 假设 target_col_x...' are minor padding, keeping it just below the lean 'every token earns its place' anchor. | 4 / 5 |
Actionability | Each step ships concrete, executable pandas/matplotlib/scipy code with real calls (groupby/agg, pivot_table, linregress, savefig), but it assumes df and named columns already exist with no data-loading setup, so it is 'mostly executable with minor gaps' rather than fully copy-paste ready. | 4 / 5 |
Workflow Clarity | Four steps are clearly sequenced (clean → analyze → visualize → export), but there are no validation checkpoints (e.g., verifying the cleaned dataset or sanity-checking stats), and the dropna/ffill cleaning is a data-modifying batch operation — per the rubric this caps workflow_clarity at 3. | 3 / 5 |
Progressive Disclosure | Content is well organized into four labeled steps with a clearly signaled, one-level-deep reference to the parent workflow SKILL.md and no nested references; with no bundle files present and a focused single-file scope this is good structure, though it lacks the explicit overview/references split of the top anchor. | 4 / 5 |
Total | 15 / 20 Passed |