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Git hook and CI guardrails for AI-assisted software development

6 starsTypeScript

RipStop – Git guardrails to reduce impact if your code agent goes wild

by Jonverrier·May 12, 2026·2 points·1 comment

AI Analysis

●●SolidSolve My ProblemShip It

Git hooks that feed policy back into agent context via generated RIPSTOP.md files.

Strengths
  • Generates RIPSTOP.md to inform agents of active rules, closing feedback loops.
  • Blocks AI failures like history rewrites, PII commits, and test skips.
  • YAML rules apply across Cursor, Claude Code, and human commits alike.
Weaknesses
  • Early stage with eight commits and no visible community adoption yet.
  • Git hooks are easily bypassed locally without server-side enforcement integration.
Target Audience

Developers using AI coding agents like Cursor or Claude Code in team environments

Similar To

pre-commit · Husky · GitHub Advanced Security

Post Description

Hi all,

RipStop is a node package implementing a set of rules that consumers can use to protect their repos from wilder actions by LLM agents. A consumer needs only a few lines of code to configure the rules they wish to apply. Example rules: no PII in commits, no Git actions that interfere with the Git history and thus might impact your ability to recover from problems.

I built this for two main reasons: - in my own repos I had a few incidents - in one Claude Code erased some un-staged changes, and in another started trying to roll back some commits. - I was reflecting on why agentic development is slow on the uptake in corporate environments. One possibility is fear of the wilder actions LLMs take - so I thought it might be helpful to illustrate how to control these.

The security model is as follows: - Layer 1 - guidance to the LLM in AGENTS.md or CLAUDE.md. This is of course guidance, and gets forgotten if context rot sets in. - Layer 2 - Git controls, via hooks that get fired automatically on various actions. Higher hurdle - the LLM would need to generate very specific commands to get round these. This is the space I am aiming at. BTW we also generate a file to go into AGENTS.md to tell the agent the environment it is working in and the controls the consumer has chosen, so there is a bit of layer 1. - Layer 3 - containers with hard configuration. Not trying to go this far in this package. This is really the space the major labs are going after with cloud agents.

I think it might be useful in two domains: - People running local agents who want a little more safety - A POC for corporate controls

Feel free to look, provide commentary, and use.

Jon

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