Content
48%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-structured with a genuinely strong safety frame (evidence boundaries, model-verification gates, privacy rules, honest disclosure that scripts and data files are not bundled). Its main weaknesses are redundancy across 核心功能/执行步骤/算法说明 sections and pseudocode-level guidance that cannot be executed as written. Splitting reference material into bundle files and de-duplicating the risk-model and algorithm listings would improve it significantly.
Suggestions
De-duplicate: 核心功能 restates the algorithms and risk models that 执行步骤 and 算法说明 already cover; merge these into one section and remove explanations of concepts Claude already knows (Pearson/Spearman use-cases, IQR basics) to fix the conciseness score.
Move 算法说明 details, the 触发条件 example list, and the /ai command catalog into reference files (e.g., references/algorithms.md, references/triggers.md) so SKILL.md stays a lean overview — this also improves progressive_disclosure.
Provide at least one executable snippet (e.g., a concrete Z-score/CUSUM computation in Python over the listed JSON schemas, or the actual report template) so the analysis steps are actionable rather than pseudocode.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Substantial duplication: CUSUM/Z-score appear in 核心功能, 步骤5, and 算法说明; Framingham/ADA/ASCVD risk models are likewise listed three times; 算法说明 explains concepts Claude already knows (e.g., "皮尔逊相关系数: 连续变量", "IQR方法: 四分位数异常值检测") and the 16 ✅ trigger examples largely restate 'When to Use'. Roughly a third of the ~240 lines could be cut, matching anchor 2 rather than 3. | 2 / 5 |
Actionability | Concrete elements exist (data-source table with exact file paths, 9-step sequence, three-tier advice taxonomy), but the code is explicitly pseudocode ("readFile/exists片段是伪代码") and the HTML report step depends on an unbundled script (scripts/generate_ai_report.py), so nothing is directly executable — anchor 3, not 4. | 3 / 5 |
Workflow Clarity | A clear 9-step sequence with most checkpoints present: 步骤1 checks whether AI features are enabled, 步骤6 requires verifying model version/inputs/applicable population before computing risk, 步骤9 gates history-writing on explicit user consent, and the Limitations section mandates stopping for clarification. Minor gaps (no feedback loop after report generation) keep it at anchor 4 rather than 5. | 4 / 5 |
Progressive Disclosure | No bundle files exist; this is a single ~240-line file with clear section headers and one clearly-signaled external prerequisite at the top. However, reference-style material (算法说明, 触发条件 example lists, command catalog) is inlined where a multi-file bundle would be leaner, matching anchor 3 ('content that should be separate is inline'). | 3 / 5 |
Total | 12 / 20 Passed |