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Homecrew – Share agent skills across your team and keep them in sync

Hacker News

Homecrew – Share agent skills across your team and keep them in sync

Hi HN, we share a ton of agent skills at my startup¹ and keeping them in sync was a pain, especially the full cross-product of every team member and every agent they use. We built Homecrew to solve it for us and open-sourced it. We use it to share skills, make them accessible to every agents, and keep everything in sync. It works for managing and finding skills outside of your team as well, but its primary motivation was sharing private internal skills. It's git-based, so if your team has a git repo of skills, Homecrew should "just work". Hope you find it helpful. Would love any feedback. ¹ https://logic.inc

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, open · Missing: mac, macos, cursor
94%94% 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 · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, 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
47%47% 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
30%30% 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
18%18% 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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