oh-my-taiyiforge

TaiyiForge is built on a specific diagnosis: the problem isn't that AI can write code, it's that agents take shortcuts — skipping requirement and design to jump straight to implementation — and that nobody can later reconstruct what was decided or who signed off. Its answer is a nine-stage state machine with a fixed artifact per stage and three hard human gates at change, design, and review. That structure is the product; the code generation itself comes from whichever AI terminal you already use. The strongest parts are the enforcement details. The dev stage requires TDD red-green before code can advance. The review gate requires an executable verification command behind every acceptance criterion rather than a written assertion that the tests pass. ChangeGraph tracks dependencies between changes so a multi-module feature doesn't quietly break itself. Long sessions produce a CONTEXT-COMPACT.md, which addresses the specific failure mode where the context window blows out mid-feature and the earlier decisions are lost. Ten profiles (full, lite, nano) exist precisely so you can dial process overhead down to match a small change instead of paying nine stages for a typo. The cross-terminal story is unusual and useful: the same command vocabulary works in Claude Code, Cursor, OpenCode, and Codex, via npx taiyi-forge-install --all. Teams that have members on different AI editors don't have to maintain four sets of process documentation. /taiyi:plan is the pragmatic entry point — it ingests a README, PRD, PDF, or URL and proposes a module breakdown and a profile, but defaults to semi-automatic so a human confirms before it batch-generates changes. Auto mode trades that confirmation for a one-shot full-stack skeleton. Where it does not fit: this is a plugin, not a standalone product, so there is no desktop or mobile app. It depends on external AI services for the actual generation, so availability and limits inherit from those services. As an open-source repository with no vendor support desk, you get community help and a GitHub issue tracker rather than SLAs. Teams that want fully autonomous agents will find the gates actively annoying, and teams without a review culture will simply work around them — at which point the pipeline is overhead with no payoff. The honest read: adopt it if auditability is a requirement, avoid it if you are optimizing for raw speed on greenfield code.