Content
35%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 a comprehensive but overly verbose specification document rather than an actionable skill. It describes many capabilities at a conceptual level but provides almost no executable code, relying heavily on pseudocode and descriptive bullet points. The content would benefit greatly from being condensed to essential instructions with actual implementation code, while moving reference material to the bundle files it already names but doesn't provide.
Suggestions
Replace all pseudocode snippets with actual executable JavaScript/Python code, especially for data reading, filtering, correlation calculation, and HTML report generation
Cut the content by at least 50%: remove the trigger conditions examples, the algorithm theory section, the feature list descriptions, and the performance/extensibility sections that Claude doesn't need
Actually create the referenced bundle files (data-sources.md, algorithms.md, examples.md) and move the detailed data source tables, algorithm descriptions, and full output examples into them
Add validation checkpoints in the workflow: verify data files exist before analysis, validate data format/integrity, and confirm report generation succeeded before presenting results
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is severely verbose at ~350+ lines. It extensively describes concepts Claude already understands (what correlation analysis is, what Pearson coefficients are, what CUSUM algorithms do), lists every possible data source exhaustively, explains basic statistical concepts, and includes large sections of pseudocode that don't add actionable value. The trigger conditions section with 12+ examples of user queries is unnecessary padding. | 2 / 5 |
Actionability | Despite its length, the skill provides almost no executable code. The JavaScript snippets are pseudocode with comments like '// 计算相关系数' instead of actual implementations. The 8-step workflow describes what to do at a high level but lacks concrete, copy-paste-ready code for any step. The HTML report generation—arguably the most complex part—has zero implementation details. | 2 / 5 |
Workflow Clarity | The 8-step sequence is clearly listed and logically ordered (determine range → read data → filter → analyze → correlate → detect changes → generate insights → visualize). However, there are no validation checkpoints between steps, no feedback loops for error recovery during analysis, and the error handling section is separate rather than integrated into the workflow. For a skill that generates health reports, missing validation of data integrity before analysis is a notable gap. | 3 / 5 |
Progressive Disclosure | The skill references external files (data-sources.md, algorithms.md, examples.md) which is good progressive disclosure in principle, but none of these bundle files actually exist. Meanwhile, the main file inlines extensive content that should be in those referenced files—the full data source tables, algorithm descriptions, and detailed output format examples are all in the main SKILL.md, making it bloated despite having references. | 3 / 5 |
Total | 10 / 20 Passed |