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Your Data Schemas Are Ready for Humans. Are They Ready for AI Too? If AI agents are going to write SQL, analyze data, and answer business questions, “somewhat documented” is no longer enough. This post explores how to assess whether a schema is understandable not just for humans, but for AI too — and proposes a practical rubric to improve it....
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Le pedimos a los agentes de IA que escriban SQL, detecten patrones y razonen sobre nuestros datos. Pero si el esquema tiene conocimiento implícito, el agente improvisa: el SQL compila, corre y devuelve números plausibles... que no son correctos. Te propongo una rúbrica con cinco dimensiones y dos herramientas para evaluar y mejorar la legibilidad de tus esquemas antes de que el agente se ponga a tirarse triples.
From Claude Code Skill to LibreChat Agent in a jiffy
A colleague recently published an exploration of a Claude Code skill called Linear Issue Enricher. The skill basically scores Linear issues for agent-readiness (to be read and developed by an agent) and enriches them with codebase context when needed. I’m a LibreChat fan, so my question was if I could port this to a LibreChat Agent without much pain. Spoiler: yes. And it’s a good proof of…
