# Literature Synthesis > Literature Synthesis - Skill: `dnyoussef/literature-synthesis` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add dnyoussef/literature-synthesis` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/literature-synthesis/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/literature-synthesis --- /*============================================================================*/ /* SKILL SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: SKILL version: 1.0.0 description: | [assert|neutral] SKILL skill for research workflows [ground:given] [conf:0.95] [state:confirmed] category: research tags: - general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute SKILL workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic research processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "SKILL", category: "research", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed] ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. /*----------------------------------------------------------------------------*/ /* S2 TRIGGER CONDITIONS */ /*----------------------------------------------------------------------------*/ [define|neutral] TRIGGER_POSITIVE := { keywords: ["SKILL", "research", "workflow"], context: "user needs SKILL capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ name: literature-synthesis description: Systematic literature review and synthesis for Deep Research SOP Pipeline A. Use when starting research projects, conducting SOTA analysis, identifying research gaps, or preparing academic papers. Implements PRISMA-compliant systematic review methodology with automated search, screening, and synthesis across ArXiv, Semantic Scholar, and Papers with Code. version: 1.0.0 category: research tags: - research - analysis - planning author: ruv --- # Literature Synthesis ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. Conduct systematic literature reviews following PRISMA guidelines, synthesizing state-of-the-art research to identify gaps and opportunities for Deep Research SOP Phase 1. ## Overview **Purpose**: Systematic literature review identifying SOTA methods, research gaps, and opportunities **When to Use**: - Starting new research projects (Phase 1 of Deep Research SOP) - Conducting state-of-the-art (SOTA) analysis - Identifying research gaps and opportunities - Preparing related work sections for papers - Validating novelty claims for proposed methods - Quality Gate 1 requirement **Quality Gate**: Required for Quality Gate 1 (minimum 50 papers) **Prerequisites**: - Research question formulated - Search databases accessible (ArXiv, Semantic Scholar, Papers with Code) - Reference management tool available (Zotero, Mendeley, BibTeX) **Outputs**: - Literature review document (50-100+ papers) - SOTA performance benchmarks table - Research gap analysis - Hypothesis formulation - Citation database (BibTeX) - PRISMA flow diagram (if systematic review) **Time Estimate**: 1-2 weeks - Database search: 2-4 hours - Screening: 1-2 days - Full-text review: 3-5 days - Synthesis: 2-3 days - Writing: 1-2 days **Agents Used**: researcher --- ## Quick Start ### 1. Define Search Query ```bash # Store research question in memory npx claude-flow@alpha memory store \ --key "sop/literature-review/research-question" \ --value "How does multi-scale attention improve long-range dependency modeling in vision transformers?" # Define search terms search_terms="(multi-scale OR hierarchical) AND (attention OR transformer) AND (vision OR image)" ``` ### 2. Database Search ```bash # Search ArXiv python scripts/search_arxiv.py \ --query "$search_terms" \ --start-date "2020-01-01" \ --max-results 500 \ --output literature/arxiv_results.json # Search Semantic Scholar python scripts/search_semantic_scholar.py \ --query "$search_terms" \ --fields "title,abstract,authors,year,citationCount,venue" \ --min-citations 10 \ --output literature/semantic_scholar_results.json # Search Papers with Code python scripts/search_papers_with_code.py \ --task "image-classification" \ --method "transformer" \ --output literature/pwc_results.json ``` ### 3. Screening and Selection ```bash # Title/abstract screening python scripts/screen_papers.py \ --input literature/*_results.json \ --inclusion-criteria literature/inclusion_criteria.yaml \ --output literature/screened_papers.json # Full-text review python scripts/full_text_review.py \ --input literature/screened_papers.json \ --download-dir literature/pdfs/ \ --output literature/selected_papers.json ``` ### 4. Synthesis ```bash # Extract SOTA benchmarks python scripts/extract_sota_benchmarks.py \ --papers literature/selected_papers.json \ --datasets "ImageNet,CIFAR-10,CIFAR-100" \ --output literature/sota_benchmarks.csv # Identify research gaps python scripts/identify_gaps.py \ --papers literature/selected_papers.json \ --output literature/research_gaps.md ``` ### 5. Generate Literature Review ```bash # Generate review document python scripts/generate_literature_review.py \ --papers literature/selected_papers.json \ --benchmarks literature/sota_benchmarks.csv \ --gaps literature/research_gaps.md \ --template templates/literature_review_template.md \ --output docs/literature /*----------------------------------------------------------------------------*/ /* S4 SUCCESS CRITERIA */ /*----------------------------------------------------------------------------*/ [define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S5 MCP INTEGRATION */ /*----------------------------------------------------------------------------*/ [define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S6 MEMORY NAMESPACE */ /*----------------------------------------------------------------------------*/ [define|neutral] MEMORY_NAMESPACE := { pattern: "skills/research/SKILL/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "SKILL-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S7 SKILL COMPLETION VERIFICATION */ /*----------------------------------------------------------------------------*/ [direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S8 ABSOLUTE RULES */ /*----------------------------------------------------------------------------*/ [direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* PROMISE */ /*----------------------------------------------------------------------------*/ [commit|confident] SKILL_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]