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 rich in domain-specific, actionable detail — data sources, scoring algorithms, thresholds, and a complete report template — and the analysis workflow is clearly sequenced. However, it is a monolithic ~775-line document with substantial duplication and padding, no validation checkpoints, and no use of progressive disclosure to split the report template and algorithms into reference files.
Suggestions
Move the full sample report and algorithm implementations into reference files (e.g. references/report-template.md, references/algorithms.md) and keep SKILL.md as a concise overview with clearly signaled one-level-deep links — this addresses both conciseness and progressive disclosure.
Remove duplicated content: the '触发条件' section repeats the frontmatter description, and medical-safety advice appears both inside the sample report and as the standalone '医学安全声明' section.
Add validation checkpoints to the execution steps, e.g. verify 'data-example/sleep-tracker.json' exists and covers the requested 时间范围 before analysis, and require a minimum record count before computing correlations.
Make the code executable by defining or removing helper functions ('time_to_minutes_std', 'align_data_with_lag', 'is_weekday', 'avg') and aligning field access (e.g. 'record[\"awakenings\"][\"count\"]', 'r[\"bedtime\"]') with the documented JSON structure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body runs ~775 lines with a ~330-line fully-populated sample report inlined in '输出格式' and ~155 lines of algorithms in '算法说明'; the '触发条件' section duplicates the frontmatter description, and medical-safety advice appears both inside the sample report ('医学安全提醒') and again as a standalone '医学安全声明' section. This is noticeably verbose with several padded/duplicated sections — matching anchor 2 — though not level 1 since it never explains concepts Claude already knows like 'what a PDF is'. | 2 / 5 |
Actionability | Concrete guidance throughout: exact data paths ('data-example/sleep-tracker.json'), executable analysis with 'from scipy import stats... stats.pearsonr', specific thresholds ('sleep latency >30分钟', '18-22℃'), and a complete report template. Falls short of level 5 because helper functions ('time_to_minutes_std', 'align_data_with_lag', 'is_weekday', 'avg') are undefined and the code accesses fields (e.g. 'record[\"awakenings\"][\"count\"]') absent from the documented data structure. | 4 / 5 |
Workflow Clarity | A clear four-step sequence exists ('步骤 1: 确定分析范围' → '步骤 2: 读取数据' → '步骤 3: 数据分析' → '步骤 4: 生成报告') but validation checkpoints are entirely missing — no verification that data files exist, that the requested date range is covered, or that record counts suffice for correlation significance. This matches anchor 3 (steps listed but checkpoints missing); the destructive/batch cap does not apply since this is a read-analyze-report skill. | 3 / 5 |
Progressive Disclosure | Section headers are well-organized (功能, 使用说明, 输出格式, 数据结构, 算法说明, 医学安全声明), but the ~330-line report template and ~155 lines of algorithms clearly belong in separate reference files and are fully inlined, with no bundle files or references at all. This matches anchor 3 ('content that should be separate is inline') rather than 2, since structure is present and navigable. | 3 / 5 |
Total | 12 / 20 Passed |