Asmora(@asmora_mcp):From day one, we have integrated K-Dense Scientific Agent Skills into our platform as composable, interoperable building blocks for agentic scientific computing. These domain-specific skills enable multi-agent workflows across computational biology, chemistry, and life sciences, allowing complex reasoning and analysis tasks to be orchestrated into scalable scientific pipelines. @k_dense_ai is collaborating with @NVIDIAAI to benchmark NVIDIA BioNeMo Agent Toolkit Skills on NVIDIA NIM microservices. This work evaluates the performance and deployment characteristics of scientific agent skills exposed through standardized NIM inference endpoints, helping establish reference benchmarks for production-grade agentic science. The K-Dense ecosystem continues to gain strong community adoption, with approximately 30,000 GitHub stars, making it one of the leading open-source repositories in agentic science. If you have not yet explored the latest K-Dense Scientific Agent Skills available on our platform, you can learn more here: https://t.co/W0dh0dDZwP

2026.06.26 07:30

From day one, we have integrated K-Dense Scientific Agent Skills into our platform as composable, interoperable building blocks for agentic scientific computing. These domain-specific skills enable multi-agent workflows across computational biology, chemistry, and life sciences, allowing complex reasoning and analysis tasks to be orchestrated into scalable scientific pipelines. @k_dense_ai is collaborating with @NVIDIAAI to benchmark NVIDIA BioNeMo Agent Toolkit Skills on NVIDIA NIM microservices. This work evaluates the performance and deployment characteristics of scientific agent skills exposed through standardized NIM inference endpoints, helping establish reference benchmarks for production-grade agentic science. The K-Dense ecosystem continues to gain strong community adoption, with approximately 30,000 GitHub stars, making it one of the leading open-source repositories in agentic science. If you have not yet explored the latest K-Dense Scientific Agent Skills available on our platform, you can learn more here:

K-Dense@k_dense_ai

2026.06.23 14:12

Excited to share new work with @NVIDIAAI: we benchmarked 10 BioNeMo NIM skills across three @AnthropicAI Claude models, ~830 controlled runs. The result: skills don't make the models smarter, they make delivery reliable. On hard calls, a model ~5x cheaper became more reliable than the frontier baseline.

Forward to community