I built a self-improving and healing workflow for Antigravity 2.0 where your AI review prompts update themselves based on your actual codebase. No more stale checklists that still reference packages you removed months ago.
The core idea: meta-prompts — instructions that tell the AI to scan your package.json, GEMINI.md, skills, and MCP config, then generate hyper-specific review prompts from scratch. Every time.
What's in the repo:
🧠 Self-optimizing prompts for code review, spring cleaning, and architecture review
🔄 Dual-model implementation review (Gemini drafts the plan, Claude stress-tests it)
🛠️ Modular .skills/ the AI loads on-demand instead of bloating every conversation
📊 Timestamped review reports with retention policies — resolved issues stay resolved
📱 A script that consolidates all your docs into a single .md for Gemini Gem so you can query your repo from your phone with Gemini.
GitHub: antigravity-self-evolving-reviews The README has a TLDR — you can be running self-evolving reviews on your own project in about 10 minutes. Download, unzip into your workspace, run /run-meta-prompts, then run /spring-cleaning or /code-review. That's it.
If you try it, I'd really like to know: does /run-meta-prompts generate prompts that actually match your stack and produce meaningfull code-review, spring-cleaning, ... or does it miss things? That's the part I'm least sure about on projects different from mine.
Feedback, suggestions, and PRs welcome!