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review-adnlpproblems

Verify ADNLPModel implementations for correctness, type stability, and allocations.

90|48|Updated Oct 13, 2015
Search Tags:#static analysis#code review#optimization#dynamic analysis#type stability#ADNLPModel
One-click install
npx skills add https://github.com/JuliaSmoothOptimizers/OptimizationProblems.jl --skill review-adnlpproblems
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: review-adnlpproblems
Source: https://github.com/JuliaSmoothOptimizers/OptimizationProblems.jl/tree/main/.claude/skills/review-adnlpproblems
Command: npx skills add https://github.com/JuliaSmoothOptimizers/OptimizationProblems.jl --skill review-adnlpproblems

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ADNLPModels, NLPModels, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the verification of ADNLPModel correctness, type stability, constructor form, and allocation requirements, streamlining the process of ensuring robust optimization models.

Core Features & Use Cases

  • Automatic Code Review: Automates the review of src/ADNLPProblems/<name>.jl files for adherence to test suite rules, contributing guidelines, and ADNLPModels API conventions.
  • Problem Characteristics Detection: Identifies whether a problem is NLS, has nonlinear or linear constraints, variable bounds, scalability, and n-adjustment.
  • Static and Dynamic Analysis: Performs both static code analysis and dynamic checks using Julia to ensure type stability and allocation-free execution.
  • Error and Warning Reporting: Reports findings as Errors, Warnings, and Info, aiding in quick identification and resolution of issues.

Quick Start

Use the review-adnlpproblems skill to review the correctness of the ADNLPModel for the problem 'hs100' by running: /review-adnlpproblems hs100

Frequently Asked Questions about review-adnlpproblems

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I verify ADNLPModel correctness and type stability in Julia?▼

Automating ADNLPModel correctness verification involves analyzing source code for API convention adherence and performing dynamic checks for type stability and allocation-free execution in Julia.

How do I automate code review for ADNLPModels optimization problems?▼

Automating code review for ADNLPModels involves scanning source files to detect problem characteristics like nonlinear constraints, variable bounds, and scalability, while reporting violations as errors and warnings.

Do I need Julia and ADNLPModels installed to run automated optimization model checks?▼

Yes, you need Julia and ADNLPModels installed because the automated optimization model checks require executing dynamic analysis to validate type stability and allocation requirements.

What static analysis checks are performed on ADNLPModel source files?▼

Static analysis checks on ADNLPModel source files include reviewing adherence to test suite rules, contributing guidelines, and API conventions, alongside detecting NLS properties and linear constraints.

Why does my ADNLPModel fail the dynamic analysis allocation checks?▼

ADNLPModel dynamic analysis allocation checks fail when execution triggers memory allocations, indicating the optimization model code lacks the required allocation-free properties.

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