Gemini Plugin for Claude Code

Hacker News

I built a plugin that lets Claude Code delegate work to Gemini CLI. I started this after finding myself reaching for Gemini more often on long context repo work. I have been especially liking Gemini’s codebaseinvestigator for long context. This is inspired by openai/codex-plugin-cc. Code Review, adversarial review. Under the hood its Gemini CLI over ACP Would love feedback from people using Claude Code, Gemini CLI, or ACP. I am especially curious whether this feels useful outside my own workflow. Its a great combo with Opus 4.7 + Gemini 3.1 workflows

Visit product View on Hacker NewsApr 22, 2026

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Actual performance

11points

7comments

Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, codex, context · Missing: mac, agents, macos

98%98% 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.

Indie HackersFits the IH revenue-focused audience · Strong signals: started, gemini · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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