Summary

This skill provides guidance on organizational practices and team processes for AI-assisted software development. It outlines how teams should shift planning priorities, architecture decisions, code review practices, and testing standards when working with AI-generated code. The skill is process-oriented rather than operational—it does not execute code, access filesystems, or perform automated tasks.

Trigger Keywords

Phrases that agents use to match this skill to user intent.

Use Cases

• Establishing code review practices for AI-generated code
• Designing testing strategies for agent-assisted development
• Setting team processes and ownership models with AI tooling
• Architecting systems optimized for AI code generation
• Evaluating engineer competencies in AI-first workflows

Quality Notes

• Skill provides high-level organizational guidance without actionable implementation steps or examples
• No concrete checklists, templates, or runbooks for code review, testing, or hiring processes
• Lacks reference to specific tools, frameworks, or CI/CD systems teams should use
• Does not address common pain points: how to integrate AI tools into existing workflows, handling edge cases in generated code, or managing technical debt from rapid generation
• Architecture requirements section is generic ('explicit boundaries', 'stable contracts') without concrete patterns or anti-patterns specific to agent-generated code
• Testing standard raises the bar but does not provide concrete examples of what 'high-signal prompts and evals' look like in practice
• No guidance on rollout risk, A/B testing, or monitoring for systems with significant AI-generated components