elicit-research-assistant skill

Overview

This skill provides AI-assisted literature review and evidence synthesis for question-answering over research papers. It helps extract claims, assess evidence strength, compare methodologies, and synthesize findings across collections of papers. The goal is faster, more reliable literature insights to support research decisions and writing.

How this skill works

The assistant ingests paper metadata, text, and supplemental files, indexes them in a vector store, and runs targeted retrieval for each user query. It extracts candidate claims, links them to supporting passages, rates evidence strength, and produces concise syntheses or comparative tables. Outputs include claim citations, confidence scores, and suggested research gaps for follow-up.

When to use it

• Preparing a literature review or related work section
• Validating specific claims against the research corpus
• Comparing methods, datasets, or results across studies
• Identifying consensus, disagreement, or research gaps
• Summarizing evidence for grant proposals or policy briefs

Best practices

• Formulate narrow, answerable questions (who/what/when/how much) to improve retrieval precision
• Supply a curated set of papers or define search filters before deep synthesis
• Request claim-level citations and confidence statements for transparency
• Iterate: review extracted claims and prompt refinements or scope changes
• Cross-check automated assessments with domain experts for high-stakes conclusions

Example use cases

• Answer whether intervention X reduces outcome Y across randomized trials, with per-study citations
• Extract and verify all claims about a biomarker’s predictive value from a corpus
• Compare training protocols and reported performance across machine learning papers
• Synthesize methodological limitations common to a body of literature and propose gaps
• Produce a concise evidence summary for a policy brief with confidence ratings

FAQ