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Conduct literature research, peer review, and synthesize grounded reports with multiple agents.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "Literature Review Council" MCP server from askskill: Run: claude mcp add 'io-github-ravicha2-lit-review-council' -- uvx lit-review-council
Create a multi-agent literature review on "applications of generative AI in education." First research the literature, then run peer review, and finally produce a grounded synthesized report.
A literature review report that includes research, review, and synthesized conclusions.
Gather research on "the impact of remote work on productivity," run a multi-agent review of differing viewpoints, and output a synthesized analysis report.
A grounded report summarizing key viewpoints and providing synthesized analysis.
I am writing an article about "climate tech investment trends." Please research the literature, perform peer review, and organize the results into a synthesized report for writing.
A literature research and synthesis report that can support writing.
Researchers or students can use it before defining a topic to research literature, review findings, and synthesize them into a grounded overview. The output is useful as an early-stage review reference.
Writers can use it before drafting articles or reports to gather relevant research and run multi-agent review, producing a more grounded synthesis. This helps organize key sources and perspectives faster.
It is a multi-agent literature review tool that performs research, peer review, and synthesized report generation. Its core capability is turning literature findings into more grounded outputs.
The provided material does not include installation or integration steps. See the source repository for details.
Based on the description, it does more than retrieve information: it also includes peer review and synthesized report generation. In other words, it emphasizes a multi-agent literature review workflow rather than just search results.
Retrieve citable technical context and coordinate evidence-backed work across AI agents.
Conduct systematic literature reviews with search planning, screening, synthesis, and citation checks.
Analyze academic manuscripts and generate tough, professional peer review reports.
Break down complex research questions and produce structured, credibility-scored reports.
Search, ingest, and query academic papers with arXiv and Semantic Scholar.
Review contract code with multi-agent AI and get verdict-driven quality insights.