literature-overview

name	Literature Overview
description	Quick landscape scan — discover papers on a topic without full-text reading
type	sop
layer	sop
agents	["alphaxiv","semantic-scholar"]
tools	{"alphaxiv":["discover_papers"],"semantic-scholar":["relevanceSearch"]}
input	query (string)
output	PaperList[] with title, authors, year, citationCount, abstract snippet

Literature Overview SOP

Layer Rules

• Layer: sop — wraps MCP tools directly
• Called by: Any tactic or strategy requiring a quick literature landscape scan
• Calls: alphaxiv MCP tools, semantic-scholar MCP tools (never calls other SOPs)

Purpose

Fast landscape scan. Understand what papers exist on a topic, who the key authors are, and rough citation counts. No full-text reading. This skill is for orientation — getting a bird's-eye view before committing to deeper reading.

Use this when you need to:

• Quickly assess how much literature exists on a topic
• Identify key papers and authors in a field
• Get citation counts to gauge paper impact
• Decide which papers deserve deeper reading (via literature-search or literature-research)

Tools

Tool	Purpose	Returns
alphaxiv.discover_papers	Semantic search for arXiv papers	Ranked paper list with title, abstract snippet, arXiv ID
ss.relevanceSearch	Keyword search across all venues	Title, abstract, authors, year, citationCount, paperId

Tool Roles

• alphaxiv.discover_papers = primary search for arXiv-covered fields (CS, math, physics, stats, EE, quant-bio/finance)
• ss.relevanceSearch = supplementary search for non-arXiv papers (biomedical, clinical, social science, humanities)

HARD-GATE

**This skill returns abstracts and metadata ONLY.**

Do NOT draw conclusions about:

• Methodology details
• Experimental results
• Specific contributions or findings
• Comparative analysis between papers

Abstracts are for ORIENTATION — identifying what exists and what looks promising.

For any substantive analysis, escalate to:

• literature-search — read AI-summarized reports (medium depth)
• literature-research — read raw full text (deep)

Treating abstracts as sufficient for research conclusions is PROHIBITED.

Workflow

Step 1: Search arXiv via alphaxiv

alphaxiv.discover_papers(
  keywords: ["keyword1", "keyword2", "keyword3"],
  question: "Detailed semantic description of desired papers",
  difficulty: 3
)

Parameters:

• keywords: 3-4 concise terms (method names, acronyms, authors)
• question: Detailed description of what papers you're looking for
• difficulty: 1-10 (use 3 for overview, higher = more retrieval effort)

Step 2: Supplement with semantic-scholar

ss.relevanceSearch(
  query: "search terms",
  limit: 20,
  year: "2022-2024",
  fields_of_study: "Computer Science"
)

Parameters:

• query: keyword search string
• limit: max results (default 10, max 100)
• year: year range filter (e.g., "2023-2024", "2020-")
• fields_of_study: field filter (optional)
• min_citation_count: citation threshold (optional)
• open_access_only: boolean (optional)

Step 3: Merge and Deduplicate

• Combine results from both sources
• Deduplicate by title similarity or matching arXiv IDs
• Sort by citation count (descending) as default ranking

Step 4: Return Structured Results

For each paper, present:

• Title
• Authors (first author + et al. for brevity)
• Year
• Citation Count (from ss if available)
• Abstract Snippet (first 2-3 sentences)
• Source (alphaxiv / semantic-scholar / both)

Tool-Specific Notes

alphaxiv.discover_papers

• Covers: computer science, mathematics, physics, statistics, quantitative biology/finance, electrical engineering
• Does NOT cover: biomedical, clinical, life science (PubMed, Cell, Nature)
• Returns: paper ID, title, authors, publication date, abstract snippet
• difficulty parameter: 1-3 for quick scans, 5-7 for thorough discovery, 8-10 for exhaustive

ss.relevanceSearch

• Covers: all academic venues (broader than arXiv)
• Returns: title, abstract, authors, year, citationCount, paperId, externalIds
• ID formats in results: S2 ID, arXiv ID, DOI, PMID
• Rate limit: 1 req/s without API key, 100 req/s with SS_API_KEY

Example

Quick scan: "graph neural networks for drug discovery"

# Step 1: arXiv search
alphaxiv.discover_papers(
  keywords: ["GNN", "drug discovery", "molecular"],
  question: "Papers applying graph neural networks to drug discovery and molecular property prediction",
  difficulty: 3
)

# Step 2: Supplement
ss.relevanceSearch(
  query: "graph neural network drug discovery",
  limit: 15,
  year: "2022-2024",
  min_citation_count: 50
)

# Step 3-4: Merge, deduplicate, return sorted list

Expected output: A list of 15-30 papers with titles, authors, years, and citation counts — enough to understand the landscape and pick papers for deeper reading.