veeduzyl

We’re building MindForge Guard — a runtime governance layer for AI-assisted development. The core idea: as AI moves from generation to execution, governance needs to happen at runtime, not only after the fact. The upgraded version is now live, focused on single-agent verifiable governance and practical AI-assisted development workflows. Welcome to try it out — your feedback and suggestions would be highly appreciated.

We’ve published the first installable GitHub Release of MindForge Guard (v1.0.1).

This release is distributed directly via GitHub (no npm registry dependency).

What’s included:

Policy init schema alignment

Legacy v=1 auto-migration

Kernel bundled inside Guard

Installable .tgz

One-step installer script

Install:

npm install -g ./veeduzyl-mindforge-guard-1.0.1.tgz

Verify:

guard --version guard init guard validate-policy guard status guard drift status

This version focuses on stable distribution and deterministic install flow.

Release page: https://github.com/veeduzyl-hue/mindforge-guard/releases/tag...

Feedback welcome.

I’m experimenting with a cold guardrail for refactor risk. If it blocks you (or fails to), feedback is here: GitHub Discussion: “Feedback: tell me when it interrupts you”

I’m experimenting with a cold guardrail for refactor risk. If it blocks you (or fails to), feedback is here: GitHub Discussion: “Feedback: tell me when it interrupts you”

If you try it and it interrupts you, I’m actively collecting feedback here:

GitHub Discussion: “Feedback: tell me when it interrupts you”

There’s no analytics or tracking. Just paste the output of a local telemetry_report (anonymous, local-only).

I’m mostly interested in when people override the guardrail and why.

This tool intentionally has no usage analytics. Feedback happens via a pinned GitHub Discussion instead: paste a local telemetry summary, nothing else.