Systematic Literature Review

The systematic-literature-review skill enables AI agents to execute rigorous academic research workflows. It automates the discovery, screening, and synthesis of scholarly articles based on specific inclusion criteria to generate structured review reports.

When to use it

• Conducting meta-analyses or narrative reviews across multiple domains of study.
• Identifying gaps in existing literature by systematically comparing conflicting findings.
• Preparing evidence-based summaries for policy decisions or clinical guidelines.
• Streamlining the initial data collection phase before deep-dive analysis.

Key capabilities

• Automated search and retrieval of relevant academic papers from major databases.
• Application of predefined inclusion/exclusion criteria to filter sources.
• Extraction of key data points, methodologies, and conclusions from selected texts.
• Synthesis of findings into a structured literature review document.

Example prompts

• "Perform a systematic literature review on the efficacy of AI in early cancer detection using studies published since 2020."
• "Identify methodological inconsistencies across recent climate change mitigation strategies by reviewing peer-reviewed journals."
• "Generate a summary of current research trends in quantum computing hardware, excluding theoretical papers only."

Tips & gotchas

Ensure you provide clear, unambiguous inclusion/exclusion criteria to avoid biased or incomplete results. This skill relies on access to indexed academic databases; it may not retrieve preprints or non-indexed grey literature unless explicitly configured with additional sources.