Package Manager for Agent Skills: Publish, Discover, Install Everywhere

Hacker News

Paks is: + A CLI to create, validate, install, and publish skills + A public registry to browse and discover skills + Semantic versioning to manage skill updates + Open source Skills are pakaged, versioned, and published so they can be reused across different AI coding agents like Claude Code, Cursor, Stakpak, GitHub Copilot, Goose, and more... Agent skills are often copied between tools or stored in your file system, which makes them hard to share. Paks extends agent skills by adding packaging and a registry. This project is an early open source project, so your feedback and contributions are highly appreciated! Repo: https://github.com/stakpak/paks

Visit product View on Hacker NewsDec 29, 2025

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

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.

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

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

39%39% 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

37%37% 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

34%34% 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

20%20% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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