--- name: lit-review description: Multi-paper literature review with gap analysis and citation graph allowed-tools: Read, Bash, Grep, Glob, WebFetch, WebSearch --- Synthesize knowledge across multiple papers on a research topic. This skill goes beyond individual paper reading to produce a coherent literature review -- comparing methodologies, synthesizing findings, identifying research gaps, building citation graphs, and generating publication-ready related work sections. It orchestrates parallel paper reading, cross-paper analysis, and structured output. - User says "literature review", "related work", "survey of", "research landscape" - User asks "what's known about X?", "compare papers on Y", "what are the approaches to Z?" - User needs a related work section for a paper or proposal - User wants to identify research gaps in a topic area - User wants to compare methodologies across multiple papers - User says "what has been done on since ?" - Reading a single specific paper -- use `paper-review` instead - Quick citation lookup ("who wrote X?") -- use research-assistant agent directly - Statistical analysis of data -- use `research-analysis` instead - General web research not about academic papers -- use `research` skill instead - Tracking experiment results -- use `experiment-log` instead 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. - Always define scope boundaries before starting (topic, time range, methodology focus) - Search existing memory first to avoid re-reading papers already in the knowledge graph - Read new papers in parallel (up to 5 concurrent paper-reader agents) - Use opus-tier agent for cross-paper synthesis (requires complex reasoning) - Build citation graph incrementally as papers are processed - Output must include gap analysis -- what is NOT covered is as important as what is - Default model routing: paper-reader at sonnet (parallel), literature-reviewer at opus (synthesis) 1. **Scope Definition**: Define the literature review boundaries - Research question: What specific question is being investigated? - Topic boundaries: What is in scope vs out of scope? - Temporal bounds: Papers from when to when? - Methodology focus: Any specific approaches to prioritize? - If scope is too broad ("tell me about AI"), ask user to narrow down 2. **Gather Existing Knowledge**: Search memory for papers already reviewed ``` sc_memory_search(query="", category="paper") sc_memory_graph_query(query="papers about ") ``` - Collect all previously extracted papers on the topic - Note which sub-areas are already covered vs gaps in coverage 3. **Discover New Papers**: Search for papers not yet in the knowledge base ``` WebSearch(query=" research paper ") WebSearch(query=" survey ") WebSearch(query=" arxiv") ``` - Prioritize: surveys/reviews first, then seminal papers, then recent work - Target 10-30 papers depending on scope breadth - Filter by venue quality and citation count when possible 4. **Read Papers in Parallel**: Extract structured info from new papers - Fire up to 5 paper-reader agents simultaneously - Each extracts: [PAPER], [METHOD], [FINDING], [LIMITATION], [CONTRIBUTION] - Store each in knowledge graph via `paper-review` workflow ``` # Parallel extraction Agent 1: paper-reader(sonnet) -> paper A Agent 2: paper-reader(sonnet) -> paper B Agent 3: paper-reader(sonnet) -> paper C Agent 4: paper-reader(sonnet) -> paper D Agent 5: paper-reader(sonnet) -> paper E ``` 5. **Cross-Paper Synthesis**: Analyze across all papers (opus tier) - **Methodology comparison**: Which approaches are used? How do they differ? - **Findings synthesis**: Where do results agree? Where do they conflict? - **Trend analysis**: How has the field evolved over time? - **Strength assessment**: Which methods have strongest evidence? ``` literature-reviewer(opus) analyzes all extracted papers together ``` 6. **Gap Analysis**: Identify what is NOT covered - Missing methodologies: What approaches have NOT been tried? - Missing datasets: What data domains are underrepresented? - Missing evaluations: What metrics are not measured? - Conflicting results: Where do papers disagree and why? - Open questions: What do authors consistently list as future work? 7. **Build Citation Graph**: Create relationship map in knowledge store ``` sc_memory_add_relation(from="paper_A", to="paper_B", type="cites") sc_memory_add_relation(from="paper_C", to="paper_A", type="extends") sc_memory_add_relation(from="paper_D", to="paper_B", type="contradicts") ``` - Track: cites, builds_on, extends, contradicts, replicates, same_topic 8. **Generate Output**: Produce structured literature review - Executive summary (1 paragraph) - Methodology comparison table - Findings synthesis with evidence quality ratings - Gap analysis with research opportunities - Citation graph summary - Optional: Publication-ready related work section - `sc_memory_search` -- Find existing papers on the topic in memory - `sc_memory_graph_query` -- Explore citation relationships and paper clusters - `sc_memory_add_entity` -- Create paper and topic entities - `sc_memory_add_relation` -- Build citation graph with typed relationships - `sc_memory_store` -- Store the literature review itself as a knowledge entry - `WebSearch` -- Discover papers not yet in the knowledge base - `WebFetch` -- Access paper abstracts and open-access content - `Read` -- Load local PDFs and previously saved paper extractions - `Grep` -- Search across stored paper extractions for specific claims or methods - `Glob` -- Find paper extraction files in the data directory User: "literature review on attention mechanisms in NLP since 2020" Action: 1. Scope: attention mechanisms, NLP domain, 2020-present, all methodology types 2. Search memory: found 4 papers already reviewed on attention 3. WebSearch: discovered 12 additional relevant papers 4. Parallel read: 5 agents extract from new papers simultaneously (3 batches) 5. Synthesis (opus): Compare self-attention, cross-attention, sparse attention, linear attention approaches across all 16 papers 6. Gaps: No papers on attention in low-resource languages, conflicting efficiency claims between sparse and linear approaches 7. Citation graph: 47 relations mapped across 16 papers 8. Output: 3-page structured review with methodology table, findings matrix, gap analysis Why good: Clear scope, leverages existing knowledge, parallel execution, deep synthesis, explicit gaps. User: "what's known about federated learning for healthcare?" Action: 1. Scope clarification: "federated learning", healthcare domain, no time restriction, all approaches 2. Search existing memory + web for papers 3. Systematic extraction and comparison 4. Output includes privacy-specific considerations unique to healthcare domain Why good: Recognizes domain-specific aspects, comprehensive coverage. User: "tell me about AI" Action: Starting literature review. Why bad: Scope is impossibly broad. Should ask user to narrow: "Which aspect of AI? NLP, computer vision, reinforcement learning, generative models? And what time period?" User: "read this paper https://arxiv.org/abs/2301.12345" Action: Running lit-review skill. Why bad: This is a single paper. Use `paper-review` skill instead. - If scope is too broad (would require 50+ papers), ask user to narrow the topic or time range - If fewer than 3 papers are found on the topic, report that the area may be too niche and suggest broadening scope - If paper access is restricted (paywalls), note which papers could not be fully analyzed and how this limits the review - If conflicting findings cannot be resolved, document the conflict explicitly with evidence from both sides - If the review exceeds reasonable length (>5000 words), offer to split into sub-topic sections - After 3 failed attempts to access a specific paper source, skip it and note in limitations - [ ] Scope clearly defined with topic, time range, and boundaries - [ ] Existing knowledge in memory leveraged (no redundant re-reading) - [ ] Sufficient papers covered (minimum 5 for meaningful synthesis) - [ ] Methodology comparison table generated - [ ] Findings synthesized with agreement/conflict analysis - [ ] Gap analysis identifies what is NOT covered - [ ] Citation graph built in knowledge store - [ ] Research opportunities derived from gaps - [ ] Output formatted as structured literature review - [ ] Review itself stored in memory for future reference ## Citation Graph Visualization The citation graph stored in the knowledge graph can be queried for: - **Clusters**: Groups of papers that cite each other heavily (research communities) - **Bridges**: Papers that connect different research communities - **Seminal papers**: Nodes with high in-degree (many papers cite them) - **Recent frontiers**: Recent papers with low in-degree (newest work) Query examples: ``` sc_memory_graph_query(query="papers cited by more than 3 papers in ") sc_memory_graph_query(query="papers that contradict each other on ") ``` ## BibTeX Export Generate BibTeX entries from reviewed papers: ```bibtex @article{smith2024attention, title={Efficient Attention Mechanisms for Long Documents}, author={Smith, J. and Jones, M.}, journal={NeurIPS}, year={2024} } ``` Export to `~/superclaw/data/reviews/.bib` ## Related Work Section Generation For paper writing, generate a publication-ready related work section: - Organized by methodology category, not chronologically - Each paragraph covers one approach family with key papers - Transitions explain how approaches relate to each other - Final paragraph positions the user's work relative to existing literature ## Gap-to-Experiment Pipeline When gaps are identified, automatically suggest experiments: 1. Gap: "No evaluation of method X on dataset Y" 2. Suggested experiment: parameters from method X paper + dataset Y 3. Link to `experiment-log` skill for tracking ## Review Update Strategy When new papers are published: 1. Search for papers published after the last review date 2. Extract and compare against existing review findings 3. Update synthesis with new evidence 4. Flag if any gaps have been filled or new gaps emerged ## Troubleshooting **Too many papers found?** - Narrow by venue quality (top-tier conferences/journals) - Narrow by citation count threshold - Focus on surveys first, then follow their references selectively **Conflicting synthesis?** - Check if papers use different evaluation metrics - Check if papers use different datasets - Check if methodology descriptions use different terminology for similar approaches **Citation graph disconnected?** - Some papers may be from different research communities - Add `same_topic` relations even without direct citations - Check if terminology differences hide connections

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