Content
35%Scale 1-3Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill reads more like a product specification or feature overview document than an actionable skill for Claude. It spends significant tokens describing capabilities, algorithms, and trigger phrases rather than providing concrete implementation guidance. The workflow lacks validation checkpoints critical for health-related data processing, and the pseudocode is not executable.
Suggestions
Remove feature marketing content (sections 1-5 under '核心功能') and replace with a 2-3 line summary; Claude doesn't need algorithm names explained—it needs to know which algorithm to apply when.
Replace pseudocode with actual executable code or precise tool invocations (e.g., exact Read tool calls with file paths, actual Python/JS for analysis calculations).
Add explicit validation checkpoints: verify file existence before reading, validate JSON schema after parsing, verify risk scores are within expected ranges before generating reports.
Move the data source table, algorithm explanations, and trigger examples to separate reference files, keeping SKILL.md focused on the execution workflow.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose, spending extensive space describing features, algorithms, and capabilities that Claude already understands (correlation analysis, Z-scores, risk scoring models). The feature list sections read like marketing documentation rather than actionable instructions. Much of the content (algorithm explanations, data source tables) could be drastically condensed or moved to reference files. | 1 / 3 |
Actionability | The execution steps provide some structure with JavaScript-like pseudocode for file reading, but the code is not truly executable (readFile/exists are not real functions without context). Steps 4-7 are abstract descriptions ('perform data cleaning, time alignment') rather than concrete, copy-paste-ready implementations. The actual analysis logic is never shown. | 2 / 3 |
Workflow Clarity | The 9-step workflow provides a clear sequence, but lacks validation checkpoints entirely. There's no verification after data reading (what if files are missing or malformed?), no validation after analysis, and no error recovery paths. For a system dealing with health data and risk predictions, the absence of validation steps is a significant gap. | 2 / 3 |
Progressive Disclosure | The content is a monolithic document with no references to external files for detailed content. Algorithm explanations, data source tables, and feature descriptions are all inline when they could be split into reference files. The structure uses headers well, but the sheer volume of inline content (algorithm details, full data source table, trigger examples) makes it unwieldy. | 2 / 3 |
Total | 7 / 12 Passed |