Content
21%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is extremely verbose and poorly structured, reading more like a comprehensive nutritional science reference document than an actionable skill for Claude. The massive inline example report (~200 lines), exhaustive nutrient listings, and detailed algorithm code bloat the file enormously while providing little actionable guidance on how Claude should actually perform the analysis. The content would benefit dramatically from being split into separate reference files and condensed to focus on decision-making logic and workflow steps.
Suggestions
Reduce the content by 70-80%: remove exhaustive nutrient lists Claude already knows, trim the example report to one representative section, and move algorithm code and data schemas to separate bundle files (e.g., ALGORITHMS.md, DATA_SCHEMA.md, EXAMPLE_REPORT.md).
Add concrete validation steps to the workflow: verify data file existence, validate data completeness before analysis, check for anomalous values, and include error recovery paths.
Replace the high-level workflow steps with specific, actionable instructions: instead of '读取数据' and '数据分析', specify exactly what Claude should do at each step, what tools to use, and what decisions to make.
Create bundle files for the referenced data paths (data-example/*.json) and split the safety guidelines, algorithms, and example outputs into separate referenced files with clear navigation links.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~500+ lines. Extensively explains nutritional concepts Claude already knows (what RDA is, what vitamins are, what macronutrients are). The massive example report template, exhaustive lists of every vitamin and mineral, and detailed algorithm explanations are heavily padded. Most of this content could be reduced to 20% of its current size. | 1 / 5 |
Actionability | Contains some executable Python code for RDA calculations and nutrient density scoring, but much of the content is descriptive rather than instructive. The code examples are illustrative pseudocode-like snippets (referencing undefined variables like RDA[v], daily_data fields) rather than truly executable code. The workflow steps are high-level ('读取数据', '数据分析') without concrete implementation. | 3 / 5 |
Workflow Clarity | The 4-step workflow (确定分析范围 → 读取数据 → 数据分析 → 生成报告) is extremely high-level with no validation checkpoints. There's no verification that data files exist or are valid, no error handling for missing nutrients, no feedback loops for data quality issues. For a skill involving health data analysis, the absence of data validation steps is a significant gap. | 2 / 5 |
Progressive Disclosure | Monolithic wall of text with everything inlined - massive example reports, full algorithm implementations, complete data schemas, exhaustive nutrient lists, and reference resources all in one file. No bundle files are provided despite references to data-example/ paths. Content that clearly belongs in separate files (algorithms, example reports, data schemas, safety guidelines) is all crammed into the main SKILL.md. | 1 / 5 |
Total | 7 / 20 Passed |