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 excellent skill that efficiently teaches multi-version differential testing with concrete, executable guidance. The tables for observables and normalization are particularly effective at conveying dense information concisely. The worked example with real divergence findings and majority voting explanation transforms abstract concepts into actionable patterns.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, assuming Claude's competence with concepts like differential testing, property-based testing, and normalization. Every section earns its place with actionable tables and concrete examples rather than explanatory padding. | 3 / 3 |
Actionability | Provides fully executable Python code with Hypothesis integration, concrete normalization strategies in table format, and a complete worked example showing real divergence findings. The harness code is copy-paste ready. | 3 / 3 |
Workflow Clarity | The workflow is clear: decide observables → generate inputs → normalize → compare → cluster divergences. The worked example demonstrates the full sequence with explicit validation (majority voting) and the output format provides a structured checklist for results. | 3 / 3 |
Progressive Disclosure | Well-organized with clear sections (observables table, input generation hierarchy, normalization table, worked example, do-not list, output format). References the related skill appropriately. Content is appropriately scoped for a single file without needing external references. | 3 / 3 |
Total | 12 / 12 Passed |