Content
75%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.
The body is highly actionable with executable examples and a clear sequenced workflow that references a real bundle script, but it carries some redundancy between the Step 2 and Step 3 output interpretation and would benefit from an explicit verification checkpoint. Tightening the repeated sat/unsat/timeout language and adding a concrete validate step would raise the weaker dimensions.
Suggestions
Consolidate the sat/unsat/unknown/timeout explanation into one place to remove the duplication between Step 2 and Step 3.
Add an explicit verification step (e.g. re-run after editing constraints, or assert the reported objective value matches the model) to strengthen the feedback loop.
Consider trimming the Action/Expectation/Result framework where two of the three fields merely restate each other.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient and assumes Claude's competence, but the sat/unsat/unknown/timeout explanation is restated across Step 2 and Step 3 and the Action/Expectation/Result framework triple-states each step, so it could be tightened. | 3 / 5 |
Actionability | Provides a fully executable SMT-LIB2 example with (minimize ...), concrete bash invocation commands, and a complete parameters table covering the common cases. | 5 / 5 |
Workflow Clarity | A clear three-step sequence (Formulate, Run, Interpret) with explicit decision branches on sat/unsat/unknown/timeout; the error-recovery guidance ('check constraints for contradictions or simplify') is present but somewhat vague rather than a validate-fix-retry loop. | 4 / 5 |
Progressive Disclosure | Well-organized into clearly headed steps plus a Parameters table, with a single one-level-deep, clearly signaled reference to the real scripts/optimize.py bundle file; minor organization gaps only. | 4 / 5 |
Total | 16 / 20 Passed |