DevSquad – Claude Code Plugin That Works with to Gemini CLI and Codex

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A year ago, I couldn't code. I still can't. But AI coding tools got me here. If you use Claude Code on anything bigger than a small script, you've hit the token limits faster than you'd like, or experienced context rot mid-session. The agent loses track of files, forgets architecture decisions, starts making things up. I was hitting this every single day. DevSquad is a Claude Code plugin, not a framework, not a new CLI. You install it, and it hooks into Claude Code's execution to delegate subtasks to Gemini and Codex. When context gets heavy, specific work gets offloaded: tests to one model, docs to another, refactoring to a third. For a quick proof of concept, I initially made a Claude.md that did the job, but I had to often remind it and it wasn't predictable or reliable. Hence, hook-enforced delegation, not polite suggestions via Claude.md. The plugin intercepts at defined trigger points and routes work through structured handoffs. Each agent operates in a focused context window instead of everyone fighting over one. I know what you're thinking: there have been a dozen agent orchestrators on Show HN this month. Here's why I built another one: all of those require you to set up a new environment. Docker, new CLI, YAML configs. DevSquad hooks into the tool you already use. How I built it: I'm a product person, not a developer. I led product at an AdTech company for years but never wrote production code. DevSquad was built entirely with AI coding tools — Claude Code, Google Antigravity, and yes, DevSquad itself once the early version worked. Make of that what you will. Two questions I'd genuinely love HN's take on: How are you handling Claude Code's token limits on large projects? And — right now this is built for vibe coders. Does it matter if the person who built a tool can't write the code by hand, as long as the tool works?

Visit product View on Hacker NewsFeb 27, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, model · 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: gemini · Missing: supports, reddit linkedin, podcasting

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, google · Missing: mobile apps, ios, personal

43%43% 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

27%27% predicted probability of success on AppSumo, 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

24%24% 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

23%23% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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