Release-gate – a CLI that checks AI-agent PRs for risky changes

Hacker News

I built this after noticing that AI-agent PRs are hard to review. There are many tools available but it does not show the reason why the code can be problematic in case of agent. A normal diff can show code changes, but it may not make it obvious that a prompt changed, a model changed, a tool config changed, or model output now reaches an eval/exec path. release-gate is a Python CLI that compares a PR branch against a base ref and reports only what the PR introduced. Example: pip install release-gate release-gate pr --base origin/main It can also create a lockfile for prompts/model/tool config: release-gate lock . release-gate audit . --lock This is early. I’m mainly looking for feedback on whether PR-scoped gating is useful, and where the static analysis is still too noisy.

Visit product View on Hacker NewsJul 27, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, code · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

16%16% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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