I adapted codex-plugin-cc's design for Gemini CLI's ACP

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This started as a protocol / adapter exercise. codex-plugin-cc made me want the same kind of Claude Code integration for Gemini, so I built one. https://github.com/abiswas97/gemini-plugin-cc The repo is derived from openai/codex-plugin-cc, but the runtime layer is different: this plugin talks directly to Gemini CLI in ACP mode instead of Codex app-server. In practice, the Gemini side here is much more session-oriented: - spawn `gemini --acp` (or `--experimental-acp` for older CLI versions) - initialize - create or load a session - set mode / model - send prompts - stream `session/update` - handle file-system / permission callbacks - cancel when needed That difference shows up in the plugin design too. Codex app-server has richer built-in concepts around review / turn lifecycles. In this repo, review is implemented in the plugin layer on top of the generic ACP task flow. Current commands are: `/gemini:review`, `/gemini:adversarial-review`, `/gemini:task`, `/gemini:rescue`, plus status / result / cancel. Built with Claude Code + Codex, which felt appropriately recursive.

Visit product View on Hacker NewsMar 31, 2026

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

91%91% 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

68%68% 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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

25%25% 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

21%21% 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.

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