Pr

Prawduct, a product development framework for Claude Code

Hacker News

Prawduct, a product development framework for Claude Code

Claude Code is amazing at writing code, but it will happily build from under-specified requirements, implement the same thing different ways, and fail to write great tests unless you specifically ask it to. Prawduct is a set of prompts, skills, hooks, and artifact templates that help focus Claude Code on product development rather than code development. You can start from something a simple as "make a website with a scientific calculator" or as complex as "create a MMO with clients for iOS, Android, and web". You can specify as much or as little arch standards or implementation details as you want. Specialized skills like /critic and /janitor are run automatically and apply context-less reviews to catch drift, hacks, and violations of best practices. I've been using Prawduct myself for a couple of months, developing my own projects and also iterating on Prawduct itself (which is of course self-hosted on its own framework). I'd love to hear feedback.

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Actual performance

6points
6comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, context, using · 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios, month, way · Missing: mobile apps, personal, entrepreneurs
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, host · Missing: plus, platform, intuitive
42%42% 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
25%25% 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.

Incorrect prediction on native model

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