Dbt-skillz compiles your dbt project into a Claude Code skill

Hacker News

Coding agents working on a dbt project are like a bull in a china shop. They don't know - What tables exist and what they contain - What each column means - How tables relate to each other - Which grain to use for aggregation - What business logic is embedded in transformations ... So they make frequent mistakes. dbt-skillz distills this information into a compact skill with multiple sub-skills. It's useful across four use cases: 1. help "data consumers" get more reliable answers when querying data via an agent 2. help "data producers" keep the agent on track while developing a dbt project. 3. run automatically on PRs and merged in CI/CD to keep the skill fresh 4. in review agents to more accurately review downstream dashboards, PRs, and other dbt-related code.

Visit product View on Hacker NewsMar 25, 2026

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

96%96% 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: mistakes · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% 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: answers · Missing: mobile apps, ios, personal

41%41% 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

33%33% 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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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