Claude Playbooks – isolated, shareable Claude Code instances

Hacker News

Sometimes I want to change my CC configuration by installing some skills, plugins or having some MCP connections. However the more I customize my CC the more is gets convoluted because it is hard to keep it clean. Wanna try an experimental hook or new CLAUDE.md behavior? You have to change your current setup, then if you didn't like, you have to revert it back. Over time, it became a nuisance to deal with all of the changes, then I wanted to use 2 different accounts, that's where I have discovered that we can actually have isolated CC instances, which is a spectacular way to have multiple CC setups. By default, CC utilized CLAUDE_CONFIG_DIR which points to ~/.claude, if you want to have a fresh CC, just run this: mkdir ~/.claude-personal CLAUDE_CONFIG_DIR=~/.claude-personal claude an you'll have separate CC instance, you may configure however you want and even have an alias for this: alias claude-personal='CLAUDE_CONFIG_DIR=~/.claude-personal claude' (put this into your ~/.bashrc or ~/.zshrc) and you are good to go! I have created several CLAUDE_CONFIG_DIR and being able to use them in parallel feels great. For example, I have experimental CC setup that is loaded with community plugins and skills (caveman, i-have-adhd, context-mode, etc) and I can access this setup just by typing "claude-experimental". And another setup where I even use different account! They both work isolated. To make things easy, I've created a small tool to manage and *share* isolated claude-code setups: https://github.com/ramazanpolat/claude-playbooks/ Single static binary, no dependencies, MIT licensed. Feedbacks are welcome!

Visit product View on Hacker NewsJul 27, 2026

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

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.

TrustMRRFits verified-revenue profile · Strong signals: personal, way, para · Missing: mobile apps, ios, entrepreneurs

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

50%50% 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: adhd, 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.

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

27%27% 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

16%16% 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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