ArteSync – A package manager for AI coding agent skills

Hacker News

I built ArteSync, a CLI package manager for managing "agent skills"—prompt instruction files that extend AI coding assistants like Claude Code, Cursor, or Gemini CLI. The problem: as I accumulated skills across projects, I found myself manually copying files between repos with no way to track versions or share updates reliably. ArteSync treats any public GitHub path as an installable source and locks it to a commit hash, similar to how npm/Cargo handle dependencies: arsync install myorg/myrepo/skills/my-skill#main This fetches the files, writes a skills.arsync manifest, and generates a skills-lock.arsync lockfile (commit-pinned). Running arsync install with no arguments does a hard checkout to the exact hashes in the lockfile—so teams get reproducible skill environments. arsync update checks for upstream changes and rewrites the lock. It's early and experimental (breaking changes possible), but it's usable: npm install -g artesync Happy to answer questions.

Visit product View on Hacker NewsFeb 26, 2026

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

95%95% 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.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

31%31% 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

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

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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