Content
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is an exemplary skill that demonstrates expert-level content design. It provides a comprehensive smell catalog with concrete detection heuristics, prioritization methodology tied to real metrics (git churn), false-positive suppression rules, and a worked example that ties everything together. The skill respects Claude's intelligence while adding genuine domain expertise about when smells matter and when they don't.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Every section earns its place. The catalog table is dense with information, the worked example demonstrates rather than explains, and there's no padding about what code smells are or basic programming concepts Claude already knows. | 3 / 3 |
Actionability | Provides concrete detection heuristics ('>40 lines, >3 levels of nesting'), specific git commands for churn analysis, a complete worked example with real Python code, and a precise output format template. Copy-paste ready throughout. | 3 / 3 |
Workflow Clarity | Clear workflow: detect smells using catalog → rank by churn × severity using git command → apply false-positive suppression checks → output in specified format → hand off to code-refactoring-assistant. The prioritization logic is explicit and the handoff to another skill provides a validation checkpoint. | 3 / 3 |
Progressive Disclosure | Well-structured with clear sections (catalog, ranking, false-positives, worked example, do-nots, output format). References external skill (code-refactoring-assistant) appropriately. Content is appropriately sized for a single file without needing external references. | 3 / 3 |
Total | 12 / 12 Passed |