skills/25-HosungYou-Diverga/skills/i1/SKILL.md
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
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research i1Install this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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No prerequisites required for this agent.
diverga_mark_checkpoint("SCH_DATABASE_SELECTION", decision, rationale)diverga_mark_checkpoint("SCH_API_KEY_VALIDATION", decision, rationale)Read research/decision-log.yaml (or .research/decision-log.yaml for legacy projects) directly to verify prerequisites. Conversation history is last resort.
Agent ID: I1 Category: I - Systematic Review Automation Tier: MEDIUM (Sonnet) Icon: 📄🔍
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.
| Database | API | PDF Availability | Rate Limit | |----------|-----|------------------|------------| | Semantic Scholar | REST | ~40% open access | 100 req/5min | | OpenAlex | REST | ~50% open access | Polite pool (email) | | arXiv | OAI-PMH | 100% | 3s delay |
| Database | API Key Env | Coverage |
|----------|-------------|----------|
| Scopus | SCOPUS_API_KEY | Comprehensive metadata |
| Web of Science | WOS_API_KEY | Citation data |
| Database | Access | Coverage | Best For | |----------|--------|----------|----------| | ERIC | Free API (IES) | 1.9M+ records | Education research, K-12, higher ed | | PsycINFO | APA subscription | 5M+ records | Psychology, behavioral science | | SSRN | Open access | 1M+ preprints | Working papers, social science | | ProQuest Dissertations | Institutional | 5M+ dissertations | Doctoral 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.
| Database | API Key Env | Coverage | Primary Discipline |
|----------|-------------|----------|-------------------|
| ERIC | ERIC_API_KEY | Education research | Education |
| PsycINFO (via APA PsycNET) | PSYCINFO_API_KEY | Psychology & behavioral sciences | Psychology |
| SSRN | — (open access) | Social science preprints | Multi-discipline |
| ProQuest | PROQUEST_API_KEY | Dissertations & theses | Multi-discipline |
# 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
| Research Area | Recommended Databases | |--------------|----------------------| | Education | ERIC + Semantic Scholar + OpenAlex | | Psychology | PsycINFO + Semantic Scholar + OpenAlex | | Social Work | Semantic Scholar + OpenAlex + SSRN | | Interdisciplinary | OpenAlex + Semantic Scholar + ERIC + PsycINFO | | STEM crossover | arXiv + Semantic Scholar + OpenAlex | | Dissertations | ProQuest + OpenAlex |
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"
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"
Before executing queries, I1 MUST:
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?
WAIT for explicit user selection
CONFIRM selection before executing
After database selection, I1 MUST validate API keys:
CHECK environment for required keys:
S2_API_KEY (optional but recommended for higher rate limits)SCOPUS_API_KEY (required if selected)WOS_API_KEY (required if selected)ERIC_API_KEY (optional, basic search is free)PSYCINFO_API_KEY (required if selected)PROQUEST_API_KEY (required if selected)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
RECORD via MCP: diverga_mark_checkpoint("SCH_API_KEY_VALIDATION", decision, rationale)
# 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}
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
# Semantic Scholar: Exponential backoff
rate_limit = {
"requests_per_window": 100,
"window_seconds": 300,
"backoff_base": 2.0
}
# OpenAlex: Polite pool (add email)
headers = {"mailto": "[email protected]"}
# arXiv: Fixed delay
delay_between_requests = 3 # seconds
| Error | Action | |-------|--------| | 429 Rate Limit | Exponential backoff, max 5 retries | | 500 Server Error | Retry after 30s | | Timeout | Retry with increased timeout | | API Key Missing | STOP → trigger 🔴 SCH_API_KEY_VALIDATION checkpoint → AskUserQuestion |
| Keywords (EN) | Keywords (KR) | Action | |---------------|---------------|--------| | fetch papers, retrieve papers | 논문 수집, 논문 검색 | Activate I1 | | search databases | 데이터베이스 검색 | Activate I1 | | Semantic Scholar, OpenAlex, arXiv | 시맨틱스칼라 | Activate I1 |
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
"""
)
requires: ["I0-review-pipeline-orchestrator"]
sequential_next: ["I2-screening-assistant"]
parallel_compatible: ["B1-literature-review-strategist"]
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.