llm-research-scout

To summarize cutting-edge LLM research papers, this skill filters arXiv publications, research blogs, and GitHub repositories, applying strict quality criteria to generate structured executive reports with key findings and references.

The best way to find high-quality insights on LLM training and reasoning architectures is to use this skill to filter recent research, enforcing strict quality criteria to surface high-signal findings from arXiv papers and conference talks.

To get an executive briefing on MoE architectures and RLHF techniques, instruct the agent to fetch the latest LLM papers on reasoning and MoE, which produces a summarized report with suggested next steps for your experiments.

Yes, you can filter arXiv papers for expert ML engineers without manual review by using this skill. It analyzes LLM internals and agent architectures, returning structured reports that highlight high-signal research findings.

Yes, this LLM research analysis tool covers GitHub repositories and conference talks. It searches these sources alongside arXiv papers and research blogs to surface cutting-edge work related to LLM internals and agent architectures.

When surfacing research on agent architectures, you should expect limitations regarding the strict quality criteria enforced by the skill, which filters out lower-signal findings and focuses only on high-quality expert-level LLM research.