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Users need AI coding assistants to not only generate code but also to enforce predefined coding standards, patterns, and organizational quality, potentially through self-validation mechanisms. This would help maintain code quality while leveraging AI for productivity.
AI coding assistants generate code faster than I can review it. That's not a flex, it's a problem. In the last few weeks I've been building what I'm calling a self-healing AI coding workflow. The idea is simple: instead of manually reviewing a bunch of AI slop, you give the coding agent a carefully structured framework for validating its own work so it fixes most bugs before you see them. I packaged it into a single Claude Code skill. It kicks off three parallel sub-agents that research your codebase, understand the database schema, and do a code review. Then it spins up the dev server, defines user journeys based on what it learned, and tests each one by navigating the actual UI with browser automation, querying the database to verify records, and taking screenshots along the way. When it finds a blocker, it fixes the code, retests, and moves on. When it finds smaller issues, it logs them for you to address later. At the end you get a structured report with everything it found, everything it fixed, and screenshots of every step. The point isn't perfection. It's reducing the mental drag of validation so that by the time control passes back to you, the big stuff is already handled. I just posted a full breakdown on YouTube showing the entire workflow in action, including how to plug it directly into your feature development process: https://lnkd.in/g86HuxYf