Ccoutputstyles – Shareable output styles for Claude Code

Hacker News

Built a way to easily share and install output styles for Claude Code: npx ccoutputstyles *Demo:* https://asciinema.org/a/dJLG9t7nNy3QtDM4r5H87y5Jr *Website:* https://ccoutputstyles.vercel.app *GitHub:* https://github.com/viveknair/ccoutputstyles Creating your own output styles is easy, but there's no dead simple way to share and install them. This is just a tool that makes installation one command, plus a small collection to browse. - One command to install any style - Browse and preview templates from our GH repo - Share via simple URLs - Works with user-level and project-level configs npx ccoutputstyles npx ccoutputstyles --url https://ccoutputstyles.vercel.app/templates/critical-code-re... Templates are just markdown files in `~/.claude/output-styles/`. Adding new ones is a one-file PR. Started with a handful of templates (critical-reviewer, educator, security-auditor) but the real point is making it easy for anyone to share theirs. What style would you want to see? Would love to add a few.

Visit product View on Hacker NewsAug 23, 2025

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

75%75% 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.

AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

31%31% predicted probability of success on TrustMRR, 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

27%27% 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

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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