Use Claude Code to Automate Systematic Literature Reviews

Systematic literature reviews are tedious, time-consuming, and prone to human error. A new YouTube walkthrough shows how Claude Code can automate the entire pipeline — from scraping paper metadata to extracting key themes and generating structured summaries.

Here's the workflow that works today.

The Technique — Automating the Literature Review Pipeline

The core idea: Use Claude Code as an agent that:

1. Scrapes paper metadata (title, authors, abstract, DOI) from sources like arXiv or Google Scholar
2. Extracts key findings, methodologies, and limitations from each paper
3. Groups papers by theme, methodology, or research question
4. Generates a structured literature review with citations

Instead of manually reading 50+ papers and copying notes into a spreadsheet, you give Claude Code a search query and let it build the review.

Why It Works — Context Window + File System Access

Claude Code's advantage over a web UI or a simple prompt:

• Multi-file output: It can write a structured review document, a citation file (BibTeX/JSON), and a summary table — all in one session
• File system access: It reads PDFs (via text extraction), writes markdown, and can even manage git commits for versioning your review
• MCP support: You can connect it to MCP servers for web scraping (e.g., @anthropic/mcp-web-search) or database queries (e.g., Semantic Scholar API)

This is a pattern we've seen before: Claude Code excels at research-to-report workflows where the output is a structured document, not just code. (See our coverage on Agent Harnessing for more on infrastructure patterns.)

How To Apply It — Step-by-Step

1. Set up your CLAUDE.md for literature review

# CLAUDE.md
## Literature Review Rules
- Output all findings as `review.md` with sections: Abstract, Methodology, Key Findings, Limitations, Relevance
- Save paper metadata to `papers.json` with fields: title, authors, year, DOI, url
- Use BibTeX format for citations in `references.bib`
- Never fabricate paper content — if you can't access the full text, note "Summary based on abstract only"

2. Run the review

claude code "Conduct a systematic literature review on transformer-based code generation models. 
Search arXiv and Semantic Scholar for papers from 2023-2026. 
For each paper, extract: model name, training data, evaluation benchmarks, and reported performance. 
Group papers by architecture (encoder-only, decoder-only, encoder-decoder) and generate a comparison table. 
Save everything to ./lit-review/"

3. Iterate with targeted prompts

After the initial run, refine:

claude code "Focus on papers that report HumanEval or MBPP scores. 
Create a table with columns: Paper, Model, HumanEval Pass@1, MBPP Pass@1, Year. 
Highlight the top-3 performing models and note any reproducibility concerns."

MCP Integration for Deeper Searches

For more comprehensive reviews, connect Claude Code to:

• Semantic Scholar API (via MCP) — get citation counts, influential citations, and TLDRs
• arXiv API — fetch full paper PDFs and extract text
• Google Scholar (via @anthropic/mcp-web-search) — find papers not indexed elsewhere

Example MCP config snippet for your .claude/servers.json:

{
  "mcpServers": {
    "semantic-scholar": {
      "command": "npx",
      "args": ["@anthropic/mcp-semantic-scholar"]
    }
  }
}

Caveats

• Paywalled papers: Claude Code can only access open-access or arXiv versions. For paywalled content, you'll need to provide PDFs manually
• Hallucination risk: Always verify citations and key claims — Claude may invent paper details if it can't access the full text
• Scope management: Without careful prompting, Claude may try to review 200+ papers. Limit to 20-30 papers per session

The Bottom Line

This workflow won't replace the final human judgment in a literature review — but it can compress 2 days of manual work into 2 hours. Use it for the grunt work: discovery, extraction, and first-draft organization. Then apply your expertise to validate and refine.