A prompt-to-PRD generator that understands your codebase

Hacker News

Hey HN, I’ve been experimenting with coding agents for a while, mostly using o3 to plan tasks and Claude Code to execute them. That combo worked surprisingly well, but I realized most people don't have a great setup to make that kind of workflow usable. So I built a small tool that generates PRDs and feature specs from prompts, grounded in your codebase. It uses an agent (similar to Claude Code) to read your repo and write the docs. That's powered by a separate open-source agent SDK I built. It’s still early and a bit rough to self-host (you’ll need a DB and LLM proxy), but the repo is open-source if you want to explore or build on it. - GitHub: https://github.com/qckfx/compose - Hosted version: https://compose.qckfx.com - Agent SDK Github: https://github.com/qckfx/agent-sdk Would love feedback on what’s useful or what’s missing, or ideas for evolving this into a broader prototyping or background agent coordination tool.

Visit product View on Hacker NewsJun 19, 2025

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

97%97% 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 HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

19%19% 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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