AgentCheck – Local AI-powered code review agents for Claude Code

Hacker News

I just want code reviews to be useful. They should be about trade-offs, sharing knowledge, and holding each other accountable, not on policing style, naming quirks, or catching trivial regressions and security issues. Tools similar to Cursor Bugbot flood PRs with noise, run in a black box, and bill you per seat for the privilege... they try to solve problems too late, in the wrong place, and without your developer environment tools. That turned into AgentCheck: an open-source subagent that runs locally with five focused reviewers - logic, security, style, guidelines, and product. It bakes in a lot of internal experience in building enterprise products, working in agentic-native SDLC, and solving Claude Code shenanigans. If you’re curious about the internals or have ideas for new reviewers, feel free to ask questions or contribute.

Visit product View on Hacker NewsAug 28, 2025

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

97%97% 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.

Hacker NewsStrong engagement from HN community · Strong signals: code review, ide, io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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 · Strong signals: reviews · Missing: plus, platform, intuitive

29%29% 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

16%16% 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.

Incorrect prediction on native model

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