Agentsmd – Local preferences and templates for AGENTS.md

Hacker News

Hi HN I built agentsmd for developers who use AGENTS.md but want a way to manage personal preferences and reusable templates on top of the canonical repo version. For example, I don’t want my agents to run npm run dev for Next.js. Another developer might want that step included. Those kinds of preferences should live in my local .agentsmd, not in the shared AGENTS.md. The standard only defines looking at AGENTS.md files, which are git-tracked, so this tool helps get around the problem. Ideally, the AGENTS.md standard should also look at local .agentsmd files to account for local preferences. I've already opened an issue: https://github.com/openai/agents.md/issues/72 I’d love feedback on: - How you separate personal vs. shared guidance in your projects - Whether templates like this would help in your workflow - What other features would make managing AGENTS.md easier Thanks for checking it out!

Visit product View on Hacker NewsSep 29, 2025

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

93%93% 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: para · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, way, para · Missing: mobile apps, ios, entrepreneurs

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

36%36% predicted probability of success on AppSumo, 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

31%31% 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

14%14% 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.

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