Restructure codebases into vertical slices with agent instruction files.
npx skills add https://github.com/tswr/engineering-mastery-plugin --skill ai-native
Please help me install this Agent Skill. Skill: ai-native Source: https://github.com/tswr/engineering-mastery-plugin/tree/main/skills/ai-native Command: npx skills add https://github.com/tswr/engineering-mastery-plugin --skill ai-native
Poorly structured codebases with implicit conventions, deep inheritance hierarchies, and slow feedback loops produce low-quality, defect-prone output from AI coding agents, while well-structured code amplifies agent performance and reduces generated defects by 30% or more.
Apply the ai-native skill to restructure your existing codebase into vertical feature slices and create a root agent instruction file with repo-wide coding conventions for your AI coding assistant.
High-intent search queries and answers about installing and using this skill.
Optimize codebases for AI-assisted development by implementing vertical slice architecture, colocating tests, and minimizing dependencies to reduce comprehension barriers and fit within LLM context limits. This structured approach amplifies AI agent performance and reduces generated defects by 30% or more.
AI coding agents produce defect-prone output when working with poorly structured codebases that have implicit conventions, deep inheritance hierarchies, and slow feedback loops. Restructuring your codebase into small, self-contained modules with clear context organization resolves this low-quality generation issue.
Design tiered agent instruction files, such as CLAUDE.md or AGENTS.md, to optimize context window usage and deliver high-signal information to AI agents. This context engineering process ensures AI coding assistants receive explicit, repo-wide coding conventions.
The best way to structure a monorepo for AI coding agents is applying vertical slice architecture to create small, self-contained modules with colocated tests. This architecture fits within LLM context limits and minimizes comprehension barriers for the agent.
Vertical slice architecture works seamlessly with spec-driven development by enabling fast TDD feedback loops and commit checkpoints. This combination facilitates reliable, low-bug human-agent collaboration and speeds up development cycles.
Set up fast test suites by colocating tests within small, self-contained vertical slices to provide immediate feedback loops. This fast TDD setup acts as a commit checkpoint for human-agent collaboration, reliably minimizing AI-generated defects.
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