Systematic Review

• Writing a literature review, survey, or related-work section for a research paper or thesis.
• Conducting a comprehensive search across multiple academic databases (Semantic Scholar, arXiv, OpenAlex) to map a field.
• Screening a large candidate set of papers by title/abstract before committing to full-text review.
• Identifying research gaps and open opportunities rather than just summarizing existing work.

• You only need a quick one-off citation or a single reference lookup rather than a structured review.
• Your project is already past the literature search and screening stages and synthesis is complete.

Follows the PRISMA 2020 framework (Page et al., BMJ 2021) to ensure your literature review meets accepted reporting standards. Each step is sequenced to mirror the standard systematic review workflow.

Prompts you to define clear inclusion and exclusion criteria before any searching begins, reducing selection bias and keeping the review scope well-controlled.

Structures searches across multiple sources — Semantic Scholar, arXiv, and OpenAlex — using both broad and narrow queries to maximise recall and minimise coverage gaps.

Implements a title/abstract screen first, followed by full-text review, so you invest deep reading effort only on the most relevant papers.

Extracts a consistent set of fields from each paper — method, dataset, metrics, and key findings — making cross-paper comparison and table generation straightforward.

Guides synthesis toward identifying research gaps and future opportunities, not just cataloguing prior work, while prioritising recent high-citation papers and at least one seminal paper per sub-topic.

A researcher writing a paper needs a defensible, reproducible account of prior work. The skill walks through criteria-setting, multi-database search, and structured extraction to produce a coherent related-work narrative.

A PhD student conducting a formal literature review can follow the PRISMA-like steps to screen hundreds of candidates systematically, extract comparable metadata, and synthesise gaps that motivate their research questions.

Teams preparing a survey paper or meta-analysis use the structured extraction template to collect method, dataset, and metric information consistently across all included studies.

When exploring a new topic area, the synthesis stage surfaces under-studied problems and open opportunities, helping researchers identify where a novel contribution would have the most impact.