Content
78%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-structured, actionable tutorial-style skill with an exemplary progressive-disclosure split: the body teaches core usage while the real reference file carries the catalog tables, settings, and anti-patterns. The residual weaknesses are small — a few missing imports in examples and light over-explanation of concepts Claude already knows.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is code-first and efficient, but contains minor over-explanation of concepts Claude already knows ('a strategy names the shape of the input', 'The draw() call requests a value from a strategy') and repeats the filter-vs-assume guidance twice in Step 5. | 4 / 5 |
Actionability | Nearly all examples are executable, but minor gaps remain: Step 4 uses date without importing it, Step 8 lacks import pytest and has a skeletal fixture body, and Step 9 uses a literal @given(...) placeholder. | 4 / 5 |
Workflow Clarity | A clear 9-step sequence from install to CI with a failure feedback loop (falsifying example copied into an @example regression test) and a diagnostic warning against heavy filtering, but validation checkpoints are implicit rather than the explicit validate-then-proceed structure of the top anchor. | 4 / 5 |
Progressive Disclosure | Scored against the actual bundle: references/hypothesis-reference.md exists, matches its advertised contents, is signaled three times with clear descriptions of what it holds, and is one level deep with no nesting; bulk lookup tables are properly deferred while core how-to stays inline. | 5 / 5 |
Total | 17 / 20 Passed |