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Collect papers across research databases, turn research questions into targeted search queries, remove duplicate records, and extract available PDF links. Useful for preparing systematic literature reviews, with database selection and access checks before retrieval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Paper Retrieval Agent - Multi-database paper fetching from Semantic Scholar, OpenAlex, arXiv Handles rate limiting, deduplication, and PDF URL extraction Use when: fetching papers, searching databases, paper retrieval Triggers: fetch papers, retrieve papers, database search, Semantic Scholar, OpenAlex, arXiv

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

  • Collecting papers across multiple research databases
  • Turning research questions into database search queries
  • Removing duplicate records and finding PDF links
  • Choosing databases for education or psychology research

포함된 파일(1)

  • SKILL.md8.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

⛔ Prerequisites (v8.2 — MCP Enforcement)

No prerequisites required for this agent.

Checkpoints During Execution

  • 🔴 SCH_DATABASE_SELECTION → diverga_mark_checkpoint("SCH_DATABASE_SELECTION", decision, rationale)
  • 🔴 SCH_API_KEY_VALIDATION → diverga_mark_checkpoint("SCH_API_KEY_VALIDATION", decision, rationale)

Fallback (MCP unavailable)

Read research/decision-log.yaml (or .research/decision-log.yaml for legacy projects) directly to verify prerequisites. Conversation history is last resort.


I1-PaperRetrievalAgent

Agent ID: I1 Category: I - Systematic Review Automation Tier: MEDIUM (Sonnet) Icon: 📄🔍

Overview

Executes multi-database paper retrieval for systematic literature reviews. Queries Semantic Scholar, OpenAlex, and arXiv (open access), with optional Scopus and Web of Science (institutional). Handles rate limiting, deduplication, and PDF URL extraction.

Capabilities

Open Access Databases (No API Key Required)

DatabaseAPIPDF AvailabilityRate Limit
Semantic ScholarREST~40% open access100 req/5min
OpenAlexREST~50% open accessPolite pool (email)
arXivOAI-PMH100%3s delay

Institutional Databases (API Key Required)

DatabaseAPI Key EnvCoverage
ScopusSCOPUS_API_KEYComprehensive metadata
Web of ScienceWOS_API_KEYCitation data

Social Science Databases (Recommended for Social Science Research)

DatabaseAccessCoverageBest For
ERICFree API (IES)1.9M+ recordsEducation research, K-12, higher ed
PsycINFOAPA subscription5M+ recordsPsychology, behavioral science
SSRNOpen access1M+ preprintsWorking papers, social science
ProQuest DissertationsInstitutional5M+ dissertationsDoctoral research, theses

💡 Social science focus: These databases are essential for education, psychology, and social work research. ERIC and SSRN are freely accessible. PsycINFO and ProQuest require institutional access.

API Key Configuration

DatabaseAPI Key EnvCoveragePrimary Discipline
ERICERIC_API_KEYEducation researchEducation
PsycINFO (via APA PsycNET)PSYCINFO_API_KEYPsychology & behavioral sciencesPsychology
SSRN— (open access)Social science preprintsMulti-discipline
ProQuestPROQUEST_API_KEYDissertations & thesesMulti-discipline

ERIC API Integration Example

# ERIC API (free, no key required for basic search)
curl "https://api.ies.ed.gov/eric/?search=meta-analysis+education+technology&format=json&rows=50"

ERIC fields: title, author, source, publicationdateyear, description, subject, peerreviewed

Database Selection Guide

Research AreaRecommended Databases
EducationERIC + Semantic Scholar + OpenAlex
PsychologyPsycINFO + Semantic Scholar + OpenAlex
Social WorkSemantic Scholar + OpenAlex + SSRN
InterdisciplinaryOpenAlex + Semantic Scholar + ERIC + PsycINFO
STEM crossoverarXiv + Semantic Scholar + OpenAlex
DissertationsProQuest + OpenAlex

Input Schema

Required:
  - query: "string"
  - databases: "list[enum[semantic_scholar, openalex, arxiv, scopus, wos, eric, psycinfo, ssrn, proquest]]"

Optional:
  - year_range: "list[int, int]"
  - max_results_per_db: "int"
  - open_access_only: "boolean"

Output Schema

main_output:
  databases_queried: "list[string]"
  results:
    semantic_scholar: "int"
    openalex: "int"
    arxiv: "int"
  total_identified: "int"
  after_deduplication: "int"
  duplicates_removed: "int"
  output_file: "string"

Human Checkpoint Protocol

🔴 SCH_DATABASE_SELECTION (REQUIRED)

Before executing queries, I1 MUST:

  1. PRESENT database options:

    Available databases for your systematic review:
    
    ✅ Open Access (recommended):
    - Semantic Scholar (~40% PDF URLs)
    - OpenAlex (~50% PDF URLs)
    - arXiv (100% PDF access)
    
    🔒 Institutional (requires API keys):
    - Scopus (SCOPUS_API_KEY: {status})
    - Web of Science (WOS_API_KEY: {status})
    
    📚 Social Science:
    - ERIC (free, education research)
    - PsycINFO (PSYCINFO_API_KEY: {status})
    - SSRN (open access, preprints)
    - ProQuest Dissertations (PROQUEST_API_KEY: {status})
    
    Which databases would you like to query?
    
  2. WAIT for explicit user selection

  3. CONFIRM selection before executing

🔴 SCH_API_KEY_VALIDATION (REQUIRED)

After database selection, I1 MUST validate API keys:

  1. CHECK environment for required keys:

    • Semantic Scholar: S2_API_KEY (optional but recommended for higher rate limits)
    • OpenAlex: Email for polite pool (optional)
    • arXiv: No key needed
    • Scopus: SCOPUS_API_KEY (required if selected)
    • Web of Science: WOS_API_KEY (required if selected)
    • ERIC: ERIC_API_KEY (optional, basic search is free)
    • PsycINFO: PSYCINFO_API_KEY (required if selected)
    • SSRN: No key needed
    • ProQuest: PROQUEST_API_KEY (required if selected)
  2. IF any selected database requires a missing key: → Call AskUserQuestion with SCH_API_KEY_VALIDATION template → WAIT for user response → If "Provide Key": Show setup instructions (export SCOPUS_API_KEY=your_key), then re-validate → If "Skip DB": Remove from selection, re-confirm remaining databases → If "Pause": Save state, stop pipeline

  3. RECORD via MCP: diverga_mark_checkpoint("SCH_API_KEY_VALIDATION", decision, rationale)

Execution Commands

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

# Paper retrieval (Stage 1)
python scripts/01_fetch_papers.py \
  --project {project_path} \
  --query "{boolean_query}" \
  --databases semantic_scholar openalex arxiv

# Deduplication (Stage 2)
python scripts/02_deduplicate.py \
  --project {project_path}

Query Building

I1 transforms natural language research questions into optimized Boolean queries:

Input: "How do AI chatbots improve speaking skills in language learning?"

Output:

Semantic Scholar: (AI OR "artificial intelligence" OR chatbot OR "conversational agent") AND ("language learning" OR "foreign language" OR L2) AND (speaking OR oral OR pronunciation)

OpenAlex: Same query with OpenAlex field mapping

arXiv: cs.CL AND (chatbot OR conversational) AND language

Rate Limiting Strategy

# Semantic Scholar: Exponential backoff
rate_limit = {
    "requests_per_window": 100,
    "window_seconds": 300,
    "backoff_base": 2.0
}

# OpenAlex: Polite pool (add email)
headers = {"mailto": "your-email@example.com"}

# arXiv: Fixed delay
delay_between_requests = 3  # seconds

Error Handling

ErrorAction
429 Rate LimitExponential backoff, max 5 retries
500 Server ErrorRetry after 30s
TimeoutRetry with increased timeout
API Key MissingSTOP → trigger 🔴 SCH_API_KEY_VALIDATION checkpoint → AskUserQuestion

Auto-Trigger Keywords

Keywords (EN)Keywords (KR)Action
fetch papers, retrieve papers논문 수집, 논문 검색Activate I1
search databases데이터베이스 검색Activate I1
Semantic Scholar, OpenAlex, arXiv시맨틱스칼라Activate I1

Integration with B1

I1 can call B1-systematic-literature-scout for advanced search strategy:

Task(
    subagent_type="diverga:b1",
    model="sonnet",
    prompt="""
    Help design search strategy for:
    Research question: {question}

    Generate:
    1. Database-specific Boolean queries
    2. MeSH/thesaurus terms (if applicable)
    3. Grey literature sources
    """
)

Dependencies

requires: ["I0-review-pipeline-orchestrator"]
sequential_next: ["I2-screening-assistant"]
parallel_compatible: ["B1-literature-review-strategist"]

Related Agents

  • I0-review-pipeline-orchestrator: Pipeline coordination
  • I2-screening-assistant: PRISMA screening
  • B1-literature-review-strategist: Search strategy design

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같은 저장소의 스킬

a1

무료

Turn a broad research idea into clear, testable questions. Compare familiar and less conventional approaches, structure the selected question around its population and methods, and assess variables, feasibility, and potential theoretical contributions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Turn a research topic into testable questions
  • Narrow an overly broad research question
  • Compare alternative research angles
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

a2

무료

Helps build a research framework by comparing theories, mapping relationships between variables, and deriving testable hypotheses. Explores less conventional options, examines weaknesses and competing explanations, and produces diagrams with anticipated reviewer objections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Comparing theories for a research question
  • Deriving hypotheses from relationships between variables
  • Examining weaknesses and anticipated reviewer objections
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

a5

무료

Clarify the philosophical assumptions behind a research project and check whether they fit the proposed methods. Get a reasoned research stance, draft proposal wording, and guidance for explaining how the researcher's background and values shape the study.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Clarifying philosophical foundations for a research proposal
  • Explaining why the chosen research methods fit the study
  • Drafting a statement about the researcher's position
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

Plan and draft academic papers through specialized agent roles covering literature, arguments, citations, and review. Get chapter guidance, revision roadmaps, Traditional Chinese and English abstracts, and documents formatted as LaTeX, DOCX, PDF, or Markdown.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Planning a paper chapter by chapter
  • Drafting a paper from research materials
  • Turning reviewer comments into a revision roadmap
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

Turn a detailed outline into a philosophy or interdisciplinary manuscript. Draft chapters with repeated quality reviews, check terminology and arguments across sections, and prepare the manuscript, evaluation reports, abstract, and metadata for submission.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Draft a full paper from a detailed outline
  • Review argument and citation quality chapter by chapter
  • Check terminology and argument flow across chapters
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

Simulate academic peer review through five reviewer roles tailored to the paper's field. Receive separate critiques, an editorial decision, and a prioritized revision roadmap, or check whether a revised manuscript addresses earlier comments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Review a manuscript before journal submission
  • Examine research methods and statistical validity
  • Challenge core arguments and alternative explanations
brycewang-stanford/Auto-Empirical-Research-Skills4.7천2026년 10월 20일 업데이트

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