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literature-review

literature-review

github.com/lingzhi227Productivity4404.0k installsUpdated 35w ago
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About literature-review

A research skill for producing structured literature reviews through multi-perspective dialogue and systematic paper searches. It guides researchers from topic exploration to cited synthesis, paper summaries, and research gaps.

Key features

  1. 01Generates diverse expert perspectives
  2. 02Conducts multi-turn research dialogue
  3. 03Searches Semantic Scholar, OpenAlex, and arXiv
  4. 04Synthesizes findings with inline citations
  5. 05Produces paper summaries and knowledge gaps

Use cases

  1. 01Survey research on a focused topic
  2. 02Compare literature from multiple perspectives
  3. 03Build a cited review and paper database
  4. 04Identify gaps for future research

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/lingzhi227/agent-research-skills --skill literature-review

After installing, type/literature-reviewin Claude Code to confirm it loaded.

Bundled filesreferences/dialogue-prompts.mdreferences/review-workflow.md

Details

From the project README

Literature Review

Conduct deep literature reviews through multi-perspective dialogue and systematic search.

Input

  • $0 — Research topic or question
  • $1 — Optional: specific focus or angle

References

  • Multi-perspective dialogue prompts (STORM): ~/.claude/skills/literature-review/references/dialogue-prompts.md
  • Literature review workflow (AgentLaboratory): ~/.claude/skills/literature-review/references/review-workflow.md

Scripts (from literature-search skill)

# Search Semantic Scholar
python ~/.claude/skills/deep-research/scripts/search_semantic_scholar.py --query "topic" --max-results 20

# Search OpenAlex
python ~/.claude/skills/literature-search/scripts/search_openalex.py --query "topic" --max-results 20

# Search arXiv
python ~/.claude/skills/deep-research/scripts/search_arxiv.py --query "topic" --max-results 10

Workflow

Step 1: Generate Expert Personas (from STORM)

Given the topic, create 3-5 diverse expert personas:

  • Each represents a different perspective, role, or research angle
  • Example: "ML systems researcher focused on efficiency", "Theoretical statistician concerned with guarantees"
  • Use the persona generation prompts from references
Step 2: Multi-Perspective Dialogue

For each persona, simulate a multi-turn Q&A conversation:

  1. Persona asks a question from their unique angle
  2. Generate search queries from the question
  3. Search literature using the search scripts
  4. Synthesize an answer grounded in retrieved papers with inline citations
  5. Record the dialogue turn with search results
  6. Repeat for 3-5 turns per persona
  7. End when persona says "Thank you so much for your help!"
Step 3: Synthesize Knowledge
  • Combine all persona conversations into a unified knowledge base
  • Remove redundancy across personas
  • Organize by theme/subtopic
  • Generate an outline based on the collected information
Step 4: Generate Literature Review
  • Write a structured review organized by the generated outline
  • Every claim must be supported by a citation
  • Include a summary table of key papers (method, contribution, limitations)

Output

A structured literature review with:

  1. Outline — Hierarchical topic structure
  2. Per-section summaries — Each grounded in retrieved papers
  3. Paper database — Structured entries for all reviewed papers
  4. Knowledge gaps — Identified areas needing further investigation

Rules

  • Every sentence in the review must be supported by gathered information
  • If information is not found, explicitly state the gap
  • Cite broadly — cover diverse approaches, not just the most popular
  • Include recent papers (last 2-3 years) alongside foundational work
  • Use inline citations: "Smith et al. [1] propose..."

Related Skills

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