✓ Do

• Always review and test AI-generated code before merging
• Provide clear context: language, framework, coding standards, constraints
• Use for boilerplate, tests, docs—areas where mistakes are easily caught
• Iterate on prompts: start broad, refine with specific requirements
• Combine AI suggestions with human judgment and domain expertise
• Document successful prompt patterns for team reuse
• Keep version control so you can rollback if needed
• Use skill for learning and exploration, not production-critical features initially

✗ Don't

• Don't commit AI code without thorough testing and review
• Don't expose sensitive code, credentials, or proprietary algorithms
• Don't use for security-critical code (auth, crypto, payments) without expert review
• Don't skip peer review process just because AI generated it
• Don't assume code follows your team's conventions—verify
• Don't let junior developers skip learning fundamentals by relying solely on AI
• Don't ignore compiler warnings or test failures in generated code

✓ Use when

Use coding skills for boilerplate generation, code reviews, refactoring legacy code, writing tests, learning new frameworks, and debugging non-critical issues. Best for repetitive tasks where errors are easy to catch.

✗ Avoid when

Avoid for production security features (auth, encryption, payment processing), complex business logic requiring deep domain knowledge, performance-critical algorithms, or when learning fundamentals is more valuable than speed.