Paper analysis (single or multi) with structured extraction, gap analysis, and citation graph
Single-paper mode: Read and extract structured information (methodology, findings, limitations, contributions) from a specific paper. Store as queryable knowledge in memory.
Multi-paper mode: Synthesize knowledge across multiple papers -- comparing methodologies, synthesizing findings, identifying research gaps, building citation graphs, and generating publication-ready related work sections. </Purpose>
<Use_When>
<Do_Not_Use_When>
research skill instead<Why_This_Exists> Individual paper reviews miss the forest for the trees. Researchers need to understand how papers relate to each other -- which methods build on which, where findings agree or conflict, and what has NOT been studied. Manual literature reviews take days and are biased by reading order and recency. This skill systematically covers the landscape, identifies gaps, and produces structured synthesis that would take a human researcher significantly longer. </Why_This_Exists>
<Execution_Policy>
When user provides a specific paper (URL, DOI, PDF, or title):
Identify Source: Parse the paper reference
Check Existing: Search memory for this paper
sc_memory_search(query="<paper title or ID>", category="paper")
Extract Structure: Extract structured sections:
Store in Memory: sc_memory_store(content="<full extraction>", category="paper", confidence=0.9)
Connect to Existing Papers: Query memory for related work and add relations
Report: One-paragraph overview, key findings table, methodology summary, limitations, connections
When user requests a review across multiple papers:
Scope Definition: Define the literature review boundaries
Gather Existing Knowledge: Search memory for papers already reviewed
sc_memory_search(query="<topic keywords>", category="paper")
Discover New Papers: Search for papers not yet in the knowledge base
WebSearch(query="<topic> research paper <year range>")
WebSearch(query="<topic> survey <year>")
WebSearch(query="<specific methodology> <topic> arxiv")
Read Papers in Parallel: Extract structured info from new papers
research-reviewer workflow# Parallel extraction
Agent 1: research-reviewer(sonnet) -> paper A
Agent 2: research-reviewer(sonnet) -> paper B
Agent 3: research-reviewer(sonnet) -> paper C
Agent 4: research-reviewer(sonnet) -> paper D
Agent 5: research-reviewer(sonnet) -> paper E
Cross-Paper Synthesis: Analyze across all papers (opus tier)
literature-reviewer(opus) analyzes all extracted papers together
Gap Analysis: Identify what is NOT covered
Build Citation Map: Document relationships between papers in memory
Generate Output: Produce structured literature review
<Tool_Usage>
sc_memory_search -- Find existing papers on the topic in memorysc_memory_store -- Store the literature review itself as a knowledge entryWebSearch -- Discover papers not yet in the knowledge baseWebFetch -- Access paper abstracts and open-access contentRead -- Load local PDFs and previously saved paper extractionsGrep -- Search across stored paper extractions for specific claims or methodsGlob -- Find paper extraction files in the data directory
</Tool_Usage><Escalation_And_Stop_Conditions>
<Final_Checklist>
Citation relationships stored in memory can be queried for:
Query examples:
sc_memory_search(query="papers cited <topic>")
sc_memory_search(query="contradicts <claim>")
Generate BibTeX entries from reviewed papers:
@article{smith2024attention,
title={Efficient Attention Mechanisms for Long Documents},
author={Smith, J. and Jones, M.},
journal={NeurIPS},
year={2024}
}
Export to ~/superclaw/data/reviews/<review-id>.bib
For paper writing, generate a publication-ready related work section:
When gaps are identified, automatically suggest experiments:
When new papers are published:
Too many papers found?
Conflicting synthesis?
Citation graph disconnected?
same_topic relations even without direct citations