Prefab: Reusable folder templates for Mac with variables and automation

Hacker News

Prefab is a native macOS app that lets you turn any folder structure into a reusable template. You define the structure, add {{variables}} to folder names, file names, and file contents, and optionally run terminal commands after deployment. I built it because I got tired of the repetitive setup when starting new projects - not just the folders but the boilerplate files, the git init, the dependency installs, etc. Existing solutions were either CLI-only scaffolding tools locked to a specific framework, or way too heavy for what's fundamentally a simple problem. What it does: - Drag any existing folder in to create a template, or build one visually - Variables support text, dates, drop-downs, and toggles with validation - Post-creation actions run shell commands via NSUserUnixTask (fully sandboxed, no entitlement hacks) - Deploy from the app, a menu bar icon, or a Finder right-click extension - Set Finder tag colours and SF Symbol icons on folders in the template Technical details for anyone curious: it's Swift/SwiftUI, App Sandbox compliant, uses NSUserUnixTask for shell execution which means scripts run from ~/Library/Application Scripts/ - Apple's sanctioned approach for sandboxed apps that need to run user-defined commands. No temporary exceptions needed. It started as a developer tool but I ended up using it for all kinds of project organisation, so there's a persona system that adapts the UI language and starter templates depending on your use case. Free, no account, no tracking, no IAP. It's available fore free on the Mac App Store. Happy to answer any questions about the implementation or take feature suggestions. Still actively developing it.

Visit product View on Hacker NewsJan 31, 2026

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

95%95% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

39%39% 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 · Strong signals: apps, way · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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.

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