Content
23%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 severely over-engineered for a SKILL.md file, containing ~400+ lines of mostly descriptive content with no executable code. It explains basic concepts Claude already understands (moving averages, linear regression, percentage calculations), repeats usage examples in multiple sections, and inlines massive output templates that should be in separate reference files. The medical safety boundaries section is a strength, but the overall content fails to be actionable or token-efficient.
Suggestions
Reduce content to under 100 lines by removing explanations of basic concepts (moving averages, linear regression, compliance formulas) and trusting Claude to implement these from brief specifications.
Move output format templates, algorithm details, and cross-module correlation analysis into separate reference files (e.g., REPORT_TEMPLATES.md, ALGORITHMS.md, CORRELATIONS.md) and link to them from the main skill.
Replace pseudocode blocks with actual executable Python code for data reading, analysis calculations, and report generation—or at minimum provide concrete file-parsing examples with real JSON structures.
Consolidate the two duplicate '使用示例' sections into a single concise section with one representative example showing input → processing → output.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose at ~400+ lines. Extensively explains concepts Claude already knows (what linear regression is, what moving averages are, basic compliance calculations). Massive amounts of template text, repeated examples, and redundant sections (e.g., '使用示例' appears twice with overlapping content). The pseudocode blocks are not executable and add bulk without value. | 1 / 5 |
Actionability | Despite the length, there is no executable code—only pseudocode descriptions and formulas written in plain text. The skill describes what to analyze but never provides actual implementation (no Python scripts, no concrete data parsing logic, no real commands). The '/rehab' commands are described but not implemented. | 2 / 5 |
Workflow Clarity | Steps 1-7 provide a reasonable sequence for the analysis workflow, and there is basic data validation mentioned in Step 1 (check file existence, verify structure, confirm sufficient data points). However, there are no explicit feedback loops for error recovery during the analysis process itself, and the validation steps are described abstractly rather than with concrete checkpoints. | 3 / 5 |
Progressive Disclosure | The entire skill is a monolithic wall of content with no references to external files. The output format templates, algorithm details, cross-module correlation analysis, and extensive examples should clearly be split into separate reference files. No bundle files exist to support progressive disclosure, and the content that should be in separate files is all inlined. | 2 / 5 |
Total | 8 / 20 Passed |