systematic-literature-review

Name: systematic-literature-review
Availability: InStock
Author: Chanw-research

Plan literature searches, deduplicate results, score papers, and draft LaTeX reports.

38|3|Updated May 7, 2026

Search Tags:#bibtex #dedupe #literature-review #openalex #ai-scoring #systematic-literature-review

Ready to run

npx skills add https://github.com/Chanw-research/claude-code-paper-writing --skill systematic-literature-review-chanw-research

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: systematic-literature-review
Source: https://github.com/Chanw-research/claude-code-paper-writing/tree/main/skills/literature-review/systematic-literature-review
Command: npx skills add https://github.com/Chanw-research/claude-code-paper-writing --skill systematic-literature-review-chanw-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, requests, numpy, scikit-learn, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Systematic literature review work is time-consuming, error-prone, and hard to reproduce; this skill automates discovery, scoring, and writing workflows to improve rigor and speed.

Core Features & Use Cases

AI-driven search planning
Deduplication, scoring, and topic modeling
Automated drafting and validation
Use Case: Researchers producing a premium literature review in days instead of weeks

Quick Start

Use the systematic-literature-review skill to orchestrate a complete AI-assisted literature review workflow from topic to final draft.

Frequently Asked Questions about systematic-literature-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate a systematic literature review from multiple databases?▼

To automate a systematic literature review, use an AI-assisted workflow to plan multi-source searches, deduplicate results, score papers, and output ready-to-use LaTeX and BibTeX artifacts with provenance and validation checks.

What is AI-driven scoring in a literature review workflow?▼

AI-driven scoring applies unified algorithms to evaluate discovered papers, helping researchers select high-quality references and generate structured reports with reproducible word budgets and QA checks.

How do I deduplicate BibTeX entries when compiling references from multiple searches?▼

Deduplicating BibTeX entries requires a workflow that ingests multi-source search results, removes redundant records, and outputs clean BibTeX artifacts with provenance tracking and QA validation checks.

Does this literature review skill support exporting to LaTeX and BibTeX formats?▼

Yes, the literature review workflow outputs ready-to-use LaTeX and BibTeX artifacts, ensuring selected references and structured reports include provenance and QA validation checks for reproducible research.

Can I use OpenAlex for multi-source literature searches and topic modeling?▼

Yes, you can use OpenAlex to plan and execute multi-source literature searches, followed by deduplication, AI-driven scoring, and topic modeling to select high-quality references for a structured report.

What is the best way to ensure reproducibility in automated literature reviews?▼

The best way to ensure reproducibility in automated literature reviews is to use a unified AI-driven workflow with validation steps, provenance tracking, and reproducible word budgets when drafting the final structured report.