review-purejump

Automate PureJuMP code review by running the review skill with a problem name, which checks signature conventions, n-adjustment, start-value coverage, and ADNLPProblems cross-compatibility dynamically.

PureJuMP file analysis checks correctness, signature conventions, n-adjustment, start-value coverage, and cross-compatibility with ADNLPProblems siblings by executing Julia code for dynamic validation.

Yes, you can validate PureJuMP compatibility with ADNLPProblems automatically by running the review skill, which dynamically executes Julia code to check cross-compatibility and adherence to established guidelines.

Code review findings are reported as Errors for test-suite failures, Warnings for likely issues requiring judgment, and Info for notable but valid observations in your PureJuMP optimization implementation.

Yes, reviewing PureJuMP files requires the NLPModels, NLPModelsJuMP, and ADNLPModels dependencies to dynamically execute Julia code and validate optimization problem implementations.

Automated PureJuMP code review limitations include flagging Warnings that require human judgment and relying on dynamic Julia code execution, meaning complex logic errors may need manual inspection beyond standard checks.