English homeMethodsWhat Are Research Skills? AI Skill Workflows for Writing, Reading, Revision, Peer Review, and Reproducibility
Method
What Are Research Skills? AI Skill Workflows for Writing, Reading, Revision, Peer Review, and Reproducibility
A practical workflow for using AI agents in verifiable literature search, critical reading, evidence tables, statistical checks, peer review, and code reproduction.
Before using this in research
The goal is not to adopt another tool. The goal is to reduce verified research time without weakening the evidence trail.
Best for
Biomedical, medical, and academic researchers who want to apply AI agents to literature review, manuscript preparation, peer review, statistical checking, and reproducibility tasks in a transparent and auditable way.
First step
Start with a clearly defined research question, target manuscript or dataset, and inclusion criteria. Ask the AI agent to document sources, assumptions, search terms, and outputs so each step can be checked by a human researcher.
A safer workflow
1
Search and screen the literature: define the research question, databases, keywords, eligibility criteria, and screening logic; use the AI agent to organize candidate papers while preserving citations and reasons for inclusion or exclusion.
2Read deeply and extract evidence: have the AI agent summarize study design, population, interventions, outcomes, effect estimates, limitations, and risk of bias; convert findings into structured evidence tables for human verification.
3Check analysis and writing: use the AI agent to review manuscript structure, terminology, reporting guideline alignment, statistical descriptions, tables, figures, and consistency between results, methods, and conclusions.
4Support peer review and reproducibility: ask the AI agent to draft review comments, identify missing methodological details, inspect code or notebooks, list required software and data dependencies, and flag steps that must be rerun or independently validated.
Watch-outs
Do not treat AI-generated summaries as evidence. Verify claims against the original article, protocol, dataset, or code repository.
Watch for citation errors, fabricated references, incorrect statistical interpretation, and overconfident conclusions that exceed the available data.
Protect confidential manuscripts, patient data, unpublished results, and reviewer materials according to institutional, journal, and regulatory requirements.
Evidence checks
Confirm that every key claim links to a traceable source, such as a PMID, DOI, trial registry entry, guideline, dataset, or code file.
Compare extracted outcomes, sample sizes, confidence intervals, p values, model specifications, and subgroup definitions with the original source documents.
Record prompts, search strategies, inclusion decisions, version numbers, and human reviewer decisions so the workflow can be audited and reproduced.
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