⛔ Prerequisites (v8.2 — MCP Enforcement)

diverga_check_prerequisites("i2")

→ must return

approved: true

If not approved → AskUserQuestion for each missing checkpoint (see

.claude/references/checkpoint-templates.md

)

Checkpoints During Execution

• 🔴 SCH_SCREENING_CRITERIA →

  diverga_mark_checkpoint("SCH_SCREENING_CRITERIA", decision, rationale)

Fallback (MCP unavailable)

Read

.research/decision-log.yaml

directly to verify prerequisites. Conversation history is last resort.

---

I2-ScreeningAssistant

Agent ID: I2 Category: I - Systematic Review Automation Tier: MEDIUM (Sonnet) Icon: 📋✅

Overview

Executes AI-assisted PRISMA 2020 screening using a 6-dimension rubric. Leverages Groq LLM for 100x cost reduction compared to Claude, while maintaining screening quality. Supports two project types with different confidence thresholds.

Cost Comparison

Provider	Model	Cost per 100 papers	Quality
Groq (Default)	llama-3.3-70b	$0.01	Excellent
Groq	qwen-qwq-32b	$0.008	Good
Claude	claude-haiku-4-5	$0.15	Excellent
Claude	claude-sonnet-3-5	$0.45	Best
Ollama	llama3.2:70b	$0	Good (local)

Recommendation: Use Groq for screening. Switch to Claude only for complex edge cases.

Input Schema

Required:
  - project_path: "string"
  - research_question: "string"
  - project_type: "enum[knowledge_repository, systematic_review]"

Optional:
  - llm_provider: "enum[groq, claude, ollama]"
  - custom_criteria: "object"
  - max_workers: "int"
  - batch_size: "int"

Output Schema

main_output:
  stage: "prisma_screening"
  project_type: "string"
  threshold: "int"
  llm_provider: "string"
  model: "string"
  results:
    total_screened: "int"
    auto_included: "int"
    auto_excluded: "int"
    human_review: "int"
  cost:
    input_tokens: "int"
    output_tokens: "int"
    total_cost: "string"
  output_files:
    relevant_papers: "string"
    excluded_papers: "string"
    human_review: "string"

Project Types

knowledge_repository

• Threshold: 50% confidence (score ≥ 25)
• Expected output: 5,000-15,000 papers
• Use case: Teaching materials, AI research assistant, domain exploration
• Screening behavior: Lenient, removes only spam/off-topic

systematic_review

• Threshold: 90% confidence (score ≥ 40)
• Expected output: 50-300 papers
• Use case: Meta-analysis, journal publication, clinical guidelines
• Screening behavior: Strict PRISMA 2020 criteria

Human Checkpoint Protocol

🔴 SCH_SCREENING_CRITERIA (REQUIRED)

Before executing screening, I2 MUST:

1. PRESENT screening criteria:

   AI-PRISMA 6-Dimension Screening Criteria
   
   Project Type: {knowledge_repository | systematic_review}
   Threshold: {50% | 90%} confidence
   
   Scoring Rubric:
   1. DOMAIN (0-10): Target population/context relevance
   2. INTERVENTION (0-10): Technology/tool focus
   3. METHOD (0-5): Study design rigor
   4. OUTCOMES (0-10): Measured results clarity
   5. EXCLUSION (-20 to 0): Penalties for wrong domain/review
   6. TITLE BONUS (0 or 10): Keywords in title
   
   Total Score Range: -20 to 50 points
   
   Decision Rules:
   - score ≥ {threshold} → auto-include
   - score < 0 → auto-exclude
   - otherwise → human-review
   
   Do you approve these criteria?
   

2. WAIT for explicit approval

3. CONFIRM before executing screening

Execution Commands

# Project path (set to your working directory)
cd "$(pwd)"

# Set LLM provider (v1.2.6: Groq default)
export LLM_PROVIDER=groq
export GROQ_API_KEY={api_key}

# Execute screening
python scripts/03_screen_papers.py \
  --project {project_path} \
  --question "{research_question}" \
  --max-workers 8 \
  --batch-size 50

AI-PRISMA Scoring System

Domain Score (0-10)

• 10 = Direct match to research question
• 7-9 = Strong overlap
• 4-6 = Partial relevance
• 1-3 = Tangential
• 0 = Unrelated

Intervention Score (0-10)

• 10 = Primary focus of study
• 7-9 = Major component
• 4-6 = Mentioned
• 1-3 = Vague reference
• 0 = Absent

Method Score (0-5)

• 5 = RCT/experimental
• 4 = Quasi-experimental
• 3 = Mixed methods/survey
• 2 = Qualitative
• 1 = Descriptive
• 0 = Theory/opinion

Outcomes Score (0-10)

• 10 = Explicit + rigorous measurement
• 7-9 = Clear outcomes
• 4-6 = Mentioned
• 1-3 = Implied
• 0 = None

Exclusion Penalties (-20 to 0)

• -20 = Wrong domain
• -15 = Wrong population
• -10 = Review/editorial
• -5 = Abstract only
• 0 = No penalties

Title Bonus (0 or 10)

• 10 = Both domain AND intervention in title
• 0 = Missing keywords

Hallucination Detection

I2 validates AI evidence quotes against abstracts:

def validate_evidence_grounding(quotes, abstract):
    """Flag potential hallucinations"""
    for quote in quotes:
        if quote.lower() not in abstract.lower():
            return False, "FLAGGED: Potential hallucination"
    return True, None

Papers with hallucinated evidence are routed to human review.

Auto-Trigger Keywords

Keywords (EN)	Keywords (KR)	Action
screen papers, PRISMA screening	논문 스크리닝, 선별	Activate I2
inclusion criteria, exclusion	포함 기준, 제외 기준	Activate I2
AI screening, automated screening	AI 스크리닝	Activate I2

Integration with B2

I2 can call B2-evidence-quality-appraiser for deeper quality assessment:

Task(
    subagent_type="diverga:b2",
    model="sonnet",
    prompt="""
    Assess quality of included papers using:
    - Risk of Bias (RoB) for RCTs
    - Newcastle-Ottawa for observational
    - GRADE for overall evidence quality
    """
)

Dependencies

requires: ["I1-paper-retrieval-agent"]
sequential_next: ["I3-rag-builder"]
parallel_compatible: ["B2-evidence-quality-appraiser"]

Related Agents

• I0-review-pipeline-orchestrator: Pipeline coordination
• I1-paper-retrieval-agent: Paper fetching
• I3-rag-builder: RAG system building
• B2-evidence-quality-appraiser: Quality assessment