How to Use Claude for a Literature Review: The Hugo Skill Guide

Ask a chatbot to “write a literature review on X” and it will happily produce paragraphs of confident prose, complete with citations — some of which do not exist. For anyone who has tried to use Claude for a literature review and then gone to check the references, this is the familiar trap: fluent text, invented sources, and no real synthesis of the field. The problem is not the model. It is that a raw chatbot is being asked to do a structured research task without any structure.

Hugo is that structure. He is a literature-review persona you load once into Claude, ChatGPT, or any AI chat, and from the first message your AI behaves like a research assistant rather than an essay generator. The defining rule is simple, and it fixes the worst failure mode: Hugo works from the sources you provide, not from half-remembered citations. That single constraint turns an AI literature review from a liability into something you can actually defend in a viva or a peer review.

Why generic AI literature reviews go wrong

Three failures show up again and again. The first is fabricated references — plausible author names, real-sounding journals, and DOIs that lead nowhere. The second is summary without synthesis: the model describes each paper in isolation and never connects them, so you get a stack of abstracts rather than an argument. The third is a missing sense of weight — it cannot tell a seminal, field-defining study from a minor conference paper, so everything is flattened to the same importance.

A literature review is not a pile of summaries. It is an argument about what a field knows, where its findings agree, where they conflict, and what remains unanswered. A generic prompt hands you the pile and quietly skips the argument, which is the only part that really matters.

What changes when Claude runs the review properly

Loaded with the Hugo skill, your AI asks before it writes. What is the research question? What is the scope and the inclusion criteria? Which sources are you working from? Only once those are answered does it begin — and it begins with the sources in front of it rather than its training data. The result is grounded: every claim traces back to a paper you supplied.

From there it synthesises. It groups findings by theme rather than by paper, surfaces where studies reinforce one another and where they disagree, and states plainly what the body of work does not yet settle. Where it is unsure, it says so and flags the point for you to check — which is exactly the behaviour you want from a research tool.

What it actually produces

A finished pass from Hugo gives you a synthesis matrix — sources down one axis, themes across the other — so you can see the shape of the evidence at a glance. Alongside it sits a thematic summary organised by argument, a clear statement of gaps and open questions, and consistent referencing of the sources you provided. Ask it to refocus — “group the synthesis by methodology”, “tighten this to the last five years”, “pull out every study that contradicts the main finding” — and it reworks the matrix in place.

How to get the most out of it

Give it real sources. Abstracts are the minimum; full texts are better. State your research question and inclusion criteria up front, because a review without scope drifts. Ask for the matrix before the prose, so you can sanity-check the structure before any writing happens. And push it on the hard parts — the gaps, the contradictions, the studies that do not fit — because that is where a review earns its keep. Above all, verify: the skill organises and synthesises what you give it, but it is not a substitute for reading the papers that matter most.

Who this is for

PhD students drafting a thesis chapter, academics preparing a paper, policy and R&D researchers running an evidence review, and analysts who need to get across a body of work quickly. It works with Claude, ChatGPT, or any AI chat that accepts a system prompt, so it fits whatever tool your institution allows. If you want the same rigour across the rest of your research workflow, the research skills collection covers summarising, note-taking, and study design — each one a focused assistant rather than a general chatbot.