Evals Skills

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Evals Skills

Hello HN I'm Rogerio, co-founder of LangWatch This past month we've completely changed the way we onboard new customers now on LangWatch, instead of giving them instructions on how to instrument, cookbooks or UIs to build evals, or docs on how to write Scenario agent simulation tests, we simply give them skills now, or ready to copy-and-paste prompts. This has reduced our onboarding time to only a few minutes, no more postponing evals because other priorities gets in the way. We have now skills for everything for managing your agent lifecycle: "Instrument my agent with open telemetry" "Write evaluations for my agent" "Write scenario tests and a CI pipeline for my agent" "Version my prompts" and even more targeted recipes "Check my agent doesn't give prescriptive advice" "Generate an evaluation dataset from my RAG knowledge base" "Test my CLI is well usable by other AI agents" Check out more on LangWatch Skills directory above and lmk what you think

Visit product View on Hacker NewsMar 23, 2026

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

95%95% 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 · Missing: supports, reddit linkedin, podcasting

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: lua, pipe, io · Missing: https docs, excited, just released

52%52% 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: month, way · Missing: mobile apps, ios, personal

49%49% 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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

22%22% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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