Here’s a trick I don’t see enough people using:

Add .diff to the end of any PR URL and copy&paste into a LLM for a quick initial review.

You can get an instant feedback on any GitHub PR.

No Copilot Enterprise. No browser extensions. No special tooling.
Example

PR Link: https://github.com/RahulPrabha/oldmanrahul.com/pull/11
Add .diff to the end: https://github.com/RahulPrabha/oldmanrahul.com/pull/11.diff
Copy the raw diff
Paste it into Claude, ChatGPT, or any LLM (Maybe add a short instuction like: please review.)

So no more human reviewers?

This isn’t a replacement for a real code review by a peer, but it’s a great way to get a first pass in short order.

You’ll catch obvious issues, get suggestions for edge cases you missed, and show up to the real review with cleaner code.

It’ll shorten your cycle times and be a courtesy to others.

Cheers!

AMSTAR stands for A MeaSurement Tool to Assess systematic Reviews. There has been a proliferation of systematic reviews as one of the key tools for evidence-based health care. This has presented both opportunities and risks. [...] The risks include variation in quality and empirical validation.

Exemple de collection de références pour une revue systématique

This dataset contains a collection of papers retrieved by using a PRISMA systematic review of Open Data and Public Domain data in Agriculture. This collection of papers uses, creates, or discusses about Open Data and Public Domain.
The dataset uses the Zotero RDF format and is classified according the source and the topic of the paper

Exemple de fichier RDF rendant (partiellement) compte des traitements successifs des références au cours d'une scoping review

Outil développé par Michael Gusenbauer permettant de comparer les bases de données pour cibler les bonnes ressources (ou simplement avoir une meilleure vision d'ensemble de ce que nous avons à disposition)

A free tool to give you the confidence to search the best databases available, so you find all you need, faster.

découvert grâce à Frédérique

Padlet contenant les slides, les exercices et de la documentation en lien avec le workshop "Can we use OpenAlex for systematic searches?" donné par Claire Stansfield à EAHIL 2025