Content
50%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 is well-organized at the section level with a clear 9-step pipeline and a genuinely useful data-source table, but the middle analysis steps are described rather than instructed, the code snippets are pseudocode, and one referenced script is missing from the bundle. Repetition of the risk models and inline algorithm explainers inflate token cost without adding guidance Claude doesn't already have.
Suggestions
Consolidate the risk-model details (Framingham/ADA/ASCVD) into one section and drop the Pearson/Spearman/Z-score primer — Claude knows these algorithms; only the skill-specific thresholds (|z| > 2, 10-year probability windows) earn their tokens.
Replace the `readFile()` pseudocode with actual executable invocations of the allowed tools (Read/Grep/Glob on the exact data paths), and specify concrete methods for steps 4–7 (e.g., which window for time alignment, what counts as a change point).
Either ship `scripts/generate_ai_report.py` in a scripts/ directory or remove the dangling reference, and move the trigger-example lists and algorithm details into a references/ file so SKILL.md stays a lean overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly dense and spec-like, but the Framingham/ADA/ASCVD risk models are repeated across three sections (核心功能, 步骤 6, 算法说明), and 算法说明 re-explains Pearson/Spearman/Z-score concepts Claude already knows. More than minor trim (4), but not heavily padded tutorial prose (2). | 3 / 5 |
Actionability | Concrete elements exist — the data-source table with exact file paths, `exists()` guards for optional files — but the code snippets are pseudocode (`readFile(...)` is an undefined function), steps 4–7 give only high-level direction ('数据清洗、时间对齐和缺失值处理'), and step 8 invokes `scripts/generate_ai_report.py`, which does not exist in the bundle. | 3 / 5 |
Workflow Clarity | A clear, coherent 9-step sequence with a step-1 config check and `exists()` guards for missing files, but checkpoints for the analysis steps are implicit and there is no output validation or error-recovery loop. More than a rough sequence (2); short of 'most checkpoints present' (4). | 3 / 5 |
Progressive Disclosure | Section headers make the ~230-line body navigable, but everything is inlined monolithically — algorithm details, trigger examples, and report-generation specifics that belong in separate reference files — and the single referenced path (`scripts/generate_ai_report.py`) is dangling since no scripts/ directory exists in the bundle. | 3 / 5 |
Total | 12 / 20 Passed |