In this repository

• Rules: .cursor/rules/ai-solution-quality.md (when to warn, ask, and not implement), .cursor/rules/imports.md (imports at top, ordered), .cursor/rules/code-quality.md, .cursor/rules/clean-code.md.
• Plan: docs/AI_CODING_STANDARDS.md — strategy and enforcement.
• After any code change: run make format and make lint from repo root.

AI solution quality — checklist and examples

Use this skill when implementing a feature, fixing a bug, or reviewing code so that AI-assisted output is optimal, non-workaround, and risks are communicated.

When to use this skill

• Implementing a new feature or endpoint
• Proposing a fix for a bug or regression
• Refactoring or changing architecture
• Reviewing or refining AI-generated code before merge
• When the user asks for something that might require a workaround or suboptimal approach