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Ocupaciones relacionadas SOC
Basado en la clasificación ocupacional SOC
name literature-search description Medium-depth literature search — read AI-summarized reports for every paper analyzed type sop layer sop agents ["alphaxiv","semantic-scholar"] tools {"alphaxiv":["discover_papers","get_paper_content"],"semantic-scholar":["relevanceSearch","paper","paperBatch","citations","references"]} input query (string), scope (survey | gap-analysis | background) output PaperAnalysis[] with metadata + AI summary content dependencies {"sops":["literature-research"]}
Literature Search SOP
Layer Rules
Layer : sop — wraps MCP tools directlyCalled by : Any tactic or strategy requiring literature survey with paper content readingCalls : alphaxiv MCP tools, semantic-scholar MCP tools (never calls other SOPs)
Purpose
Medium-depth reading. Understand methods, contributions, and findings via AI-generated summary reports. Suitable for literature surveys, gap analysis, and building background knowledge.
Use this when you need to:
Conduct a literature survey on a topic
Understand what methods exist and how they compare
Identify gaps in current research
Build background knowledge for a research project
This skill reads AI-summarized reports — not raw full text. For rigorous analysis requiring raw text, use literature-research.
Tools
Tool Purpose Returns alphaxiv.discover_papersPrimary search — arXiv semantic search Ranked paper list with metadata ss.relevanceSearchSupplementary search — non-arXiv papers Title, abstract, authors, citationCount ss.paper / ss.paperBatchMetadata enrichment Citation count, DOI, S2 ID, externalIds ss.citationsPapers that cite this paper (incoming) Citing paper list with context ss.referencesPapers this paper cites (outgoing) Referenced paper list alphaxiv.get_paper_contentAI summary report (fullText: false) Structured AI-generated paper report
HARD-GATE
**Do NOT base analysis on abstracts or discover_papers snippets alone.**
For EVERY paper selected for analysis, you MUST call:
alphaxiv.get_paper_content(url: arxiv_url, fullText: false)
This returns an AI-generated summary report optimized for LLM consumption.
PROHIBITED:
Completing a research task without reading paper content
Using only abstracts from ss.relevanceSearch as your evidence
Using only discover_papers snippets as your evidence
Claiming to understand a paper's methodology from its abstract alone
Call get_paper_content for every paper you analyze
Base your analysis on the AI summary report content
Minimum 5 papers read via get_paper_content for any survey task
Workflow
Step 1: Search alphaxiv.discover_papers(
keywords: ["keyword1", "keyword2", "keyword3"],
question: "Detailed description of papers needed",
difficulty: 5
)
Supplementary (non-arXiv):
ss.relevanceSearch(
query: "search terms",
limit: 20,
year: "2022-2024"
)
Step 2: Enrich Metadata For papers found via alphaxiv, enrich with citation data:
ss.paperBatch(
paper_ids: ["ARXIV:2301.xxxxx", "ARXIV:2302.xxxxx", ...]
)
Returns: citationCount, DOI, S2 ID for each paper.
Step 3: Select Papers Choose top N papers (typically 5-15) based on:
Relevance to research question
Citation count (impact indicator)
Recency (for fast-moving fields)
Diversity of approaches (avoid reading only one school of thought)
Step 4: Read AI Summary Reports alphaxiv.get_paper_content(
url: "https://arxiv.org/abs/XXXX.XXXXX",
fullText: false
)
fullText: false (default) returns an AI-generated intermediate report:
Structured summary of contributions
Key methods and techniques
Main results and findings
Optimized for LLM consumption (faster than raw text)
Step 5: Citation Graph Expansion (Optional) To find related work not caught by keyword search:
ss.citations(paper_id: "ARXIV:XXXX.XXXXX", limit: 50)
ss.references(paper_id: "ARXIV:XXXX.XXXXX", limit: 50)
Filter results by year and citation count, then repeat Steps 3-4 for promising papers.
Tool-Specific Notes
alphaxiv.get_paper_content
fullText: false (default) — AI-generated report, faster, structuredfullText: true — raw extracted text, slower, complete (use in literature-research, not here)Accepts: arXiv URL (https://arxiv.org/abs/XXXX.XXXXX), PDF URL, alphaXiv URL
Only works for arXiv papers — non-arXiv papers cannot be read via this tool
ss.paperBatch
Max 500 papers per call
Auto-prefixes bare arXiv IDs (e.g., 2301.12345 → ARXIV:2301.12345)
Returns null for papers not found
ss.citations / ss.references
Max 1000 results per call
Use offset and limit for pagination
Includes citation context, intent, and influence flags
Examples
Literature survey: "attention mechanisms in vision transformers" # Step 1: Search
alphaxiv.discover_papers(
keywords: ["vision transformer", "attention", "ViT"],
question: "Papers proposing or analyzing attention mechanisms in vision transformers",
difficulty: 5
)
ss.relevanceSearch(query: "vision transformer attention mechanism", limit: 15, year: "2022-2024")
# Step 2: Enrich
ss.paperBatch(paper_ids: ["ARXIV:2010.11929", "ARXIV:2103.14030", ...])
# Step 3: Select top 8 by citation count + relevance
# Step 4: Read each
alphaxiv.get_paper_content(url: "https://arxiv.org/abs/2010.11929") # ViT
alphaxiv.get_paper_content(url: "https://arxiv.org/abs/2103.14030") # Swin
# ... repeat for all 8
# Step 5: Expand via citations of ViT
ss.citations(paper_id: "ARXIV:2010.11929", limit: 30)
Gap analysis: "efficient inference for large language models" # Step 1: Broad search
alphaxiv.discover_papers(
keywords: ["LLM", "efficient inference", "quantization", "pruning"],
question: "Methods for making large language model inference faster or cheaper",
difficulty: 6
)
# Step 2-4: Enrich, select 10, read AI summaries
# Step 5: Check what recent papers cite the seminal works
ss.citations(paper_id: "ARXIV:2210.17323", limit: 50) # GPTQ
ss.citations(paper_id: "ARXIV:2306.00978", limit: 50) # AWQ
Available SOPs Optional, no fixed order; the final leaf is always a sop.
SOP When to use literature-research Deep literature research — raw full text reading and targeted PDF queries for rigorous analysis
