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 presents a well-sequenced nine-step workflow with concrete data-source mapping, but the guidance is undermined by pseudocode tool calls, abstract analysis steps, a dangling reference to a nonexistent script, and repetition of the risk-model material across three sections. It reads as a feature overview more than an executable procedure.
Suggestions
Replace the pseudocode readFile()/exists() blocks with the actual tool invocations (e.g., Read on the concrete file paths), and give steps 4-6 a concrete method or reference implementation instead of one-line summaries.
Add validation checkpoints: after preprocessing, verify required data is present and time-aligned before analyzing; after report generation, verify the HTML report renders before updating data/ai-history.json.
Either include scripts/generate_ai_report.py in the bundle or remove the reference, and move 算法说明 details and the trigger catalog to reference files to cut duplication and shrink SKILL.md.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient — it documents project-specific data sources and steps Claude would not know — but risk models (Framingham/ADA/ASCVD, CUSUM/Z-score) and the risk-type list are repeated nearly verbatim across 核心功能, 执行步骤, and 算法说明, and the ✅-padded trigger catalog duplicates the description's job. Matches 'mostly efficient but could be tightened'; not 4 because the duplication is substantive, not minor. | 3 / 5 |
Actionability | There is real concrete guidance (exact data file paths, a data-source table, the generate_ai_report.py invocation), but the JavaScript snippets are pseudocode — readFile()/exists() are not executable tool calls — and steps 4-6 are abstract directions ("进行数据清洗、时间对齐和缺失值处理") with no method or code. Matches 'some concrete guidance but incomplete; pseudocode instead of executable code'; not 4 because core analysis steps lack executable detail. | 3 / 5 |
Workflow Clarity | The nine execution steps are clearly sequenced, and step 1 validates the AI config while step 3 guards optional files with exists(), giving a partial checkpoint. But there is no validation of analysis outputs or the generated report before step 9 writes to ai-history.json, so checkpoints are largely missing — the score-3 anchor fits. Not 4 because explicit checkpoints beyond the initial config check are absent. | 3 / 5 |
Progressive Disclosure | Sections are well-headed and easy to scan, but the ~220-line body inlines material that belongs in reference files (算法说明 details, the full data-source schema), and its only bundle reference — scripts/generate_ai_report.py — does not exist in the skill (no references/, scripts/, or assets/ directories). Matches 'some structure but could be better organized'; not 4 because a signaled reference points to a missing file and content is not split out. | 3 / 5 |
Total | 12 / 20 Passed |