Content
92%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
Excellent skill that efficiently maps Python constructs to Dafny equivalents with concrete, executable examples. The impedance mismatch framing is clever and actionable. Minor weakness is the dangling reference to 'invariant-inference' and the monolithic structure, though the content density justifies keeping it together.
Suggestions
Clarify the 'invariant-inference' reference—either link to an actual file or inline the technique
Consider splitting the translation tables into a separate REFERENCE.md if this skill grows
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Every section earns its place—tables compress translation rules efficiently, no explanation of what Dafny or Python are, assumes Claude knows both languages. The impedance mismatch table is dense with actionable mappings. | 3 / 3 |
Actionability | Provides executable Dafny code with complete worked example that verifies. Translation tables give concrete patterns, not vague descriptions. The output format template is copy-paste ready. | 3 / 3 |
Workflow Clarity | Clear 3-step process (extract spec → translate body → add invariants) with explicit guidance on what to do when stuck. The 'Do not' section provides validation checkpoints against common failure modes. | 3 / 3 |
Progressive Disclosure | Content is well-organized with clear sections, but references 'invariant-inference' without explaining what it is or where to find it. All content is inline rather than split, though the skill length may justify this. | 2 / 3 |
Total | 11 / 12 Passed |