Content
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body presents a well-sequenced, well-organized workflow with concrete data sources, but keeps all detail inline, describes core algorithms rather than implementing them, and references a missing script. Validation checkpoints and progressive disclosure to reference files would lift it further.
Suggestions
Provide executable implementations (or reference files) for the core algorithms rather than describing them at a high level, and remove or supply the missing scripts/generate_ai_report.py.
Add explicit validation checkpoints in the workflow (e.g., verify data files exist and are well-formed before analysis; confirm report output before recording history).
Move algorithm details, the data-source table, and safety rules into one-level-deep reference files to reduce inline length and improve navigation.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient and well-organized, but risk types and algorithms are restated across "核心功能", "执行步骤", and "算法说明", so it could be tightened rather than fully lean. | 2 / 3 |
Actionability | Concrete file paths and readFile/exists snippets are provided, but the core algorithms (CUSUM, Z-score, Framingham, ASCVD) are described rather than given as executable code, and the referenced scripts/generate_ai_report.py does not exist in the bundle. | 2 / 3 |
Workflow Clarity | A clear 9-step sequence is present, but there are no explicit validation checkpoints or validate-fix-retry feedback loops, which keeps it below the level-3 anchor. | 2 / 3 |
Progressive Disclosure | The single SKILL.md is well-sectioned, but at ~220 lines with all algorithm and data-source detail inline and a broken reference to a non-existent script, it is not split into one-level-deep reference files. | 2 / 3 |
Total | 8 / 12 Passed |