alterlab-literature-review

The saved excerpt is a snapshot from review. The external source remains the complete and most current version.

---
name: alterlab-literature-review
description: Conducts comprehensive, systematic literature reviews across multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar), with PRISMA flow tracking, study screening (title/abstract and full-text), evidence-table extraction, and risk-of-bias assessment, producing professionally formatted markdown documents and PDFs with verified citations in multiple styles (APA, Nature, Vancouver). Use when running a systematic literature review, meta-analysis, research synthesis, or broad literature search, building a PRISMA flow diagram, screening studies, extracting an evidence table, or assessing risk-of-bias across biomedical, scientific, and technical domains. Part of the AlterLab Academic Skills suite.
allowed-tools: Read Write Edit Bash
license: MIT
compatibility: Needs network access to PubMed, arXiv, bioRxiv, and Semantic Scholar (biopython/arxiv clients); PDF output requires pandoc and xelatex (check with --check-deps)
metadata:
    skill-author: AlterLab
    version: "1.0.0"
---

# Literature Review

## Overview

Conduct systematic, comprehensive literature reviews following rigorous academic methodology. Search multiple literature databases, synthesize findings thematically, verify all citations for accuracy, and generate professional output documents in markdown and PDF formats.

This skill integrates with multiple scientific skills for database access (gget, bioservices, datacommons-client) and provides specialized tools for citation verification, result aggregation, and document generation.

## When to Use This Skill

Use this skill when:
- Conducting a systematic literature review for research or publication
- Synthesizing current knowledge on a specific topic across multiple sources
- Performing meta-analysis or scoping reviews
- Writing the literature review section of a research paper or thesis
- Investigating the state of the art in a research domain
- Identifying research gaps and future directions
- Requiring verified citations and professional formatting

## Core Workflow

Literature reviews follow a structured, multi-phase workflow:

### Phase 1: Planning and Scoping

1. **Define Research Question**: Use PICO framework (Population, Intervention, Comparison, Outcome) for clinical/biomedical reviews
   - Example: "What is the efficacy of CRISPR-Cas9 (I) for treating sickle cell disease (P) compared to standard care (C)?"

2. **Establish Scope and Objectives**:
   - Define clear, specific research questions
   - Determine review type (narrative, systematic, scoping, meta-analysis)
   - Set boundaries (time period, geographic scope, study types)

3. **Develop Search Strategy**:
   - Identify 2-4 main concepts from research question
   - List synonyms, abbreviations, and related terms for each concept
   - Plan Boolean operators (AND, OR, NOT) to combine terms
   - Select minimum 3 complementary databases

4. **Set Inclusion/Exclusion Criteria**:
   - Date range (e.g., last 10 years: 2015-2024)
   - Language (typically English, or specify multilingual)
   - Publication types (peer-reviewed, preprints, reviews)
   - Study designs (RCTs, observational, in vitro, etc.)
   - Document all criteria clearly

### Phase 2: Systematic Literature Search

1. **Multi-Database Search**:

   Select databases appropriate for the domain:

   **Biomedical & Life Sciences:**
   - Search PubMed/PMC via NCBI E-utilities (Entrez esearch/efetch) — see scripts/search_databases.py / direct Entrez API
   - Search bioRxiv/medRxiv via the bioRxiv API (api.biorxiv.org) or Europe PMC
   - Use `bioservices` skill for ChEMBL, KEGG, UniProt, etc.

   **General Scientific Literature:**
   - Search arXiv via direct API (preprints in physics, math, CS, q-bio)
   - Search Semantic Scholar via API (200M+ papers, cross-disciplinary)
   - Use Google Scholar for comprehensive coverage (manual or careful scraping)

   **Specialized Databases:**
   - Use `gget alphafold` for protein structures
   - Use `gg