Content
65%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 skill provides concise, concrete, mostly-executable Python across a clear five-step pipeline, but it lacks validation/verification checkpoints for a batch analysis workflow and does not use any progressive-disclosure file structure, capping workflow_clarity and progressive_disclosure at 3.
Suggestions
Add validation checkpoints: guard that required columns (time_col, target_cols, group_col, value_col) exist before use, and verify the report PNG was saved successfully before declaring completion.
Add a brief error-recovery feedback loop (e.g., if columns are missing, inspect df.columns and adapt; if total_current is 0, skip the growth-rate print) to lift workflow_clarity above the batch-operation cap.
Replace placeholder column names ('时间列', '指标1占比') with either an explicit 'replace these with your column names' instruction or a small auto-detection snippet so the code is copy-paste ready.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient: short step descriptions plus directly executable code with minimal conceptual padding, though inline comments like '# 设置中文字体，兼容不同操作系统' and the placeholder '# 占位示例' notes add slight over-explanation that could be trimmed. | 4 / 5 |
Actionability | Each step provides concrete, mostly executable Python with real pandas/matplotlib calls and specific thresholds; minor gaps (placeholder column names '时间列'/'指标1占比' that the user must replace, no input validation) keep it just below fully copy-paste-ready. | 4 / 5 |
Workflow Clarity | The five steps are clearly sequenced, but this is a batch analysis pipeline with no validation checkpoints — no checks that required columns exist, no error handling, no 'verify the output/report' feedback loop — so per the batch-operation cap, workflow_clarity cannot exceed 3 despite the clean sequence. | 3 / 5 |
Progressive Disclosure | Content is organized into clearly labeled steps rather than a monolithic wall, but there is no SKILL.md-as-overview structure pointing to separate references, no bundle files, and all detail is inlined; it is well-sectioned but not structured across files or navigation. | 3 / 5 |
Total | 14 / 20 Passed |