A prompt directory directly integrated into your LLM

Hacker News

I built Minnas: a prompt and resource directory directly integrated into your local LLM. With it you can discover popular prompt/resource collections on our [directory]( https://minnas.io/directory ), add them to your account, and connect it to your LLM using the MCP protocol so that they instantly become available in your workflow. You can also publish your own prompt collections, or share them directly with your team to give your teammates instant access to your handcrafted prompts. I hope you find value in it! Also, please don't hesitate in letting me know if you spot any bugs or areas for improvement. Feel free to let me know if you're missing any prompt collections from the directory, and I'll do my best to add them.

Visit product View on Hacker NewsAug 21, 2025

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Launch Intel predictions

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

65%65% 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.

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

45%45% 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

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

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

40%40% 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

37%37% 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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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