
Can an AI Agent Run Your Mass Spec Pipeline? Benchmarking the PyOpenMS Skill
A reproducible 250-run study of feature detection, adduct grouping, quantification, and identification, run by an AI agent with and without the pyOpenMS skill.
Blog
Explore tutorials, product updates, and insights on AI-powered research automation from the K-Dense team.

A reproducible 250-run study of feature detection, adduct grouping, quantification, and identification, run by an AI agent with and without the pyOpenMS skill.

Fable 5 and GPT-Rosalind show frontier AI moving into scientific workflows. The next bottleneck is not intelligence, but evidence.

A reproducible study of pKa, logD, tautomers, ADME, and docking run by an AI agent with the Rowan skill, measured against RDKit and experimental ground truth.

Anthropic showed a general model can rival ChemDraw at NMR. The real story for scientists: the bottleneck has moved from the model to the workflow around it.

AI agents are moving into real scientific workflows. The question for working scientists is not whether they are powerful, but whether they are verifiable.

Scientific Agents: 503 open-source AGENTS.md profiles that teach any AI agent to reason like a senior practitioner in a specific science or engineering field.

One Agent Skill gives a research agent 78 public scientific and economic databases. Here is the design argument, with real token and routing benchmarks.

Our new open source methodology makes AI research agents pre-register hypotheses, work reproducibly, and verify before they claim. Pre-registration over TDD.

Exa is now integrated into K-Dense's Scientific Agent Skills library, bringing neural web search and URL extraction to AI-driven scientific research.