literature-review

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

---
name: literature-review
description: Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
allowed-tools: Read Write Edit Bash
license: MIT license
metadata:
  version: "1.7"
  skill-author: K-Dense Inc.
  openclaw:
    primaryEnv: OPENROUTER_API_KEY
    envVars:
    - name: OPENROUTER_API_KEY
      required: false
      description: OpenRouter API key for the skill's LLM-powered steps.
---

# 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 uses the **parallel-web skill** (`parallel-cli search`) as the primary web search tool for broad academic literature discovery, supplemented by specialized database access skills (gget, bioservices, datacommons-client). It 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

## Visual Enhancement with Scientific Schematics

**⚠️ MANDATORY: Every literature review MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.**

This is not optional. Literature reviews without visual elements are incomplete. Before finalizing any document:
1. Generate at minimum ONE schematic or diagram (e.g., PRISMA flow diagram for systematic reviews)
2. Prefer 2-3 figures for comprehensive reviews (search strategy flowchart, thematic synthesis diagram, conceptual framework)

**How to generate figures:**
- Use the **scientific-schematics** skill to generate AI-powered publication-quality diagrams
- Simply describe your desired diagram in natural language
- Nano Banana Pro will automatically generate, review, and refine the schematic

**How to generate schematics:**
```bash
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
```

The AI will automatically:
- Create publication-quality images with proper formatting
- Review and refine through multiple iterations
- Ensure accessibility (colorblind-friendly, high contrast)
- Save outputs in the figures/ directory

**When to add schematics:**
- PRISMA flow diagrams for systematic reviews
- Literature search strategy flowcharts
- Thematic synthesis diagrams
- Research gap visualization maps
- Citation network diagrams
- Conceptual framework illustrations
- Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

---

## Core Workflow

A literature review runs in seven phases, documented in full with commands and templates
in [references/core_workflow.md](references/core_workflow.md):

1. **Planning and scoping** — the question, inclusion and exclusion criteria, and scope.
2. **Systematic literature search** — multi-database searching with recorded queries.
3. **Screening and selection** — title/abstract then full-text screening with counts kept
   for the PRISMA flow.
4. **Data extraction and quality ass