Content
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is highly actionable with complete, runnable code and a clearly sequenced workflow that includes a data-validation checkpoint. Its main weakness is conciseness (some comments restate basic library behavior) and a monolithic structure with no progressive disclosure.
Suggestions
Trim comments that explain well-known matplotlib/pandas behavior (e.g., how the bottom parameter accumulates) to improve token efficiency.
Split the lengthy inline tutorial into a concise overview in SKILL.md with detailed code moved to a referenced script or reference file for better progressive disclosure.
Make the Step3 validation an explicit checkpoint with guidance on what to do if totals do not sum to ~100.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient code with task-relevant comments, but some comments re-explain basic matplotlib knowledge (e.g., '核心堆叠逻辑：每一层的 bottom 是前几层高度的总和'), fitting the 'mostly efficient but includes some unnecessary explanation' anchor. | 2 / 3 |
Actionability | It provides complete, executable Python with imports, sample data, conversion logic, plotting via the bottom parameter, and high-resolution save — copy-paste ready, matching the 'fully executable code' anchor. | 3 / 3 |
Workflow Clarity | The Step1–Step5 sequence is clearly ordered and Step3 includes an explicit data-integrity validation ('验证数据完整性：确保所有维度相加接近 100' via final_check), satisfying the 'clear sequence with explicit validation steps' anchor for this non-destructive skill. | 3 / 3 |
Progressive Disclosure | No bundle files exist and the skill is a single ~85-line monolithic tutorial with everything inline; though well-organized into steps, it exceeds the simple-skill under-50-lines allowance for a top score, fitting the 'content that should be separate is inline' anchor. | 2 / 3 |
Total | 10 / 12 Passed |