Screen research papers against a research question using six scoring dimensions. Choose criteria for a broad knowledge collection or a systematic review, then receive inclusion, exclusion, and human-review lists with evidence quotes checked against abstracts.
Screening Assistant - AI-PRISMA 6-dimension screening with Groq LLM (100x cheaper) Supports two project types with different confidence thresholds Use when: screening papers, PRISMA screening, inclusion/exclusion criteria Triggers: screen papers, PRISMA screening, inclusion criteria, exclusion criteria, AI screening
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
diverga_check_prerequisites("i2") → must return approved: true
If not approved → AskUserQuestion for each missing checkpoint (see .claude/references/checkpoint-templates.md)
diverga_mark_checkpoint("SCH_SCREENING_CRITERIA", decision, rationale)Read .research/decision-log.yaml directly to verify prerequisites. Conversation history is last resort.
Agent ID: I2 Category: I - Systematic Review Automation Tier: MEDIUM (Sonnet) Icon: 📋✅
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.
| 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 | $0 | Good (local) |
Recommendation: Use Groq for screening. Switch to Claude only for complex edge cases.
Required:
- project_path: "string"
- research_question: "string"
- project_type: "enum[knowledge_repository, systematic_review]"
Optional:
- llm_provider: "enum[groq, claude, ollama]"
- custom_criteria: "object"
- max_workers: "int"
- batch_size: "int"
main_output:
stage: "prisma_screening"
project_type: "string"
threshold: "int"
llm_provider: "string"
model: "string"
results:
total_screened: "int"
auto_included: "int"
auto_excluded: "int"
human_review: "int"
cost:
input_tokens: "int"
output_tokens: "int"
total_cost: "string"
output_files:
relevant_papers: "string"
excluded_papers: "string"
human_review: "string"
Before executing screening, I2 MUST:
PRESENT screening criteria:
AI-PRISMA 6-Dimension Screening Criteria
Project Type: {knowledge_repository | systematic_review}
Threshold: {50% | 90%} confidence
Scoring Rubric:
1. DOMAIN (0-10): Target population/context relevance
2. INTERVENTION (0-10): Technology/tool focus
3. METHOD (0-5): Study design rigor
4. OUTCOMES (0-10): Measured results clarity
5. EXCLUSION (-20 to 0): Penalties for wrong domain/review
6. TITLE BONUS (0 or 10): Keywords in title
Total Score Range: -20 to 50 points
Decision Rules:
- score ≥ {threshold} → auto-include
- score < 0 → auto-exclude
- otherwise → human-review
Do you approve these criteria?
WAIT for explicit approval
CONFIRM before executing screening
# Project path (set to your working directory)
cd "$(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
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.
| 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 |
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
"""
)
requires: ["I1-paper-retrieval-agent"]
sequential_next: ["I3-rag-builder"]
parallel_compatible: ["B2-evidence-quality-appraiser"]
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