Yet Another Markup Language Engineering Toolkit

Hacker News

Over the past few weeks I've grown tired of these super, all in one, agentic AI Skill plugins, MCP servers and whatnot that promise you the world. Of course, there is a world of vibes that survives off that, but there is also a world of more constraints and responsibilities. Hence I built Yamlet: A CLI tool with accompanying Claude Code skillset, that merges both deterministic checks with human like interface to help you define requirements - including acceptance criteria, which are then promptly used to generate adequate Gherking .feature test files. The details are stored in a minimized fashion as .yamlet.yaml files, which allow quick and easy review - without the usual multi page slop specs. - And that's it. Everything else, depends entirely on the system you're working on. I stopped here because for me, this can not only be used in a system of pure agentic coding, but also with a normal dev team.

Visit product View on Hacker NewsJul 31, 2026

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Launch Intel predictions

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

89%89% 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: including · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: io, including · 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.

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

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

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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