Agent skill

literature-synthesis

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/DNYoussef/context-cascade/tree/main/skills/research/literature-synthesis

SKILL.md

/============================================================================/ /* 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] <promise>SKILL_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]

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