Nanopm – PM automation for Claude Code (audit → strategy → roadmap)

Hacker News

Garry Tan's gstack proved you can give Claude Code a full engineering team via the SKILL.md standard. I asked: what about the PM layer? One command (/pm-run) runs the full planning cycle inside your terminal — audit → objectives → strategy → roadmap → PRD. Each skill writes a markdown artifact, the next one reads it. Context compounds across the whole pipeline. The part I find most useful: it builds persistent memory of your product in ~/.nanopm/memory/. Re-run /pm-audit six months later and it knows what you tried before. No other PM tool does this because no other PM tool lives in your editor. /pm-breakdown creates tickets directly in Linear or GitHub Issues from the PRD. https://github.com/nmrtn/nanopm Early days, would love to know: does running PM work inside your editor feel right, or does it belong in a separate tool?

Visit product View on Hacker NewsMar 28, 2026

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

92%92% 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.

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

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

26%26% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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