Post

@iam.slys.dev

The practical idea around Scientific Agent Skills is the boundary between local state and optional cloud compute. For research workflows, where files, keys, and intermediate results live matters before model choice. That control should be part of the product. #AIInfrastructure #MLOps

github.comGitHub - K-Dense-AI/scientific-agent-skills: Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific databases covering bio...

15/10/26, 9:42 pm0 reposts1 like

YAMLwrangler

@jmm86.bsky.social

Right now, we build agents that just *use* cloud compute for heavy lifting while keeping local state private. But the real win is when local state dictates what gets sent to the cloud—reducing bandwidth and latency by 60%.

The Projekt

@wearetheprojekt.bsky.social

Keys are the one to check first. A skill that shells out to a cloud client inherits whatever credentials sit in the environment, so a scoped key per skill beats one shared .env across all 177.