/literature-engine

name: literature-engine
description: >
  ALWAYS activate when the user needs to find, organize, review, or synthesize
  academic literature. Uses academic APIs (Semantic Scholar, OpenAlex, CrossRef, arXiv)
  via scripts/academic_search.py. Handles search strategy, snowballing, screening,
  concept matrices, narrative synthesis, and literature monitoring (detecting new
  publications since last search). NEVER use web scraping for paper discovery —
  APIs first, web search only for verification.

> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`: > ``` > ### Skill Activation: Literature Engine > **Timestamp:** [current date/time] > **Actor:** AI Agent (literature-engine) > **Input:** [brief description of the search/synthesis request] > **Output:** [brief description of results — e.g., "47 papers found across 4 databases, deduplicated to 38"] > ```

Literature Engine

Data Source Priority — STRICT

1. **Academic APIs** via `scripts/academic_search.py` — ALWAYS first

• Semantic Scholar (200M+ papers): citation graphs, TLDR, snowballing
• OpenAlex (474M+ works): broadest coverage, German publications, CC0
• CrossRef (150M+ works): DOI resolution, journal metadata
• arXiv (2.4M+ preprints): CS/AI/ML cutting-edge

2. **Web search** — ONLY for: VHB-JOURQUAL rankings, specific CFPs, conference info 3. **Firecrawl** — LAST RESORT for sources without APIs (AIS eLibrary, specific repositories)

Search Strategy

Step 1: Query Construction

For any research topic, construct 4-6 search queries:

| Query Type | Purpose | Example (GenAI/Agents paper) | |-----------|---------|------------------------------| | Core English | Main topic | "generative AI enterprise implementation" | | Synonym English | Alternative terms | "large language models organizational adoption" | | Narrow English | Specific aspect | "autonomous AI agents business process" | | Adjacent English | Related field | "AI transformation strategy organizational change" | | German | German publications | "generative KI Unternehmen Implementierung" | | Theoretical | Theory-specific | "sociotechnical systems artificial intelligence" |

Step 2: Execute Search

from scripts.academic_search import search_all, search_semantic_scholar, search_openalex, snowball, deduplicate_papers, papers_to_csv, papers_to_bibtex_file

# Round 1: Broad multi-API search
papers = search_all("generative AI enterprise implementation", 
                    max_results_per_source=20, year_from=2020)
papers += search_all("autonomous AI agents organizational", 
                     max_results_per_source=20, year_from=2020)
papers += search_all("LLM adoption business strategy", 
                     max_results_per_source=15, year_from=2022)

# Round 2: German sources via OpenAlex
papers += search_openalex("generative KI Implementierung Unternehmen", 
                          max_results=15, year_from=2020)

# Deduplicate
papers = deduplicate_papers(papers)
print(f"After dedup: {len(papers)} unique papers")

Step 3: Snowball Key Papers

# Sort by citations, snowball top 5
top = sorted(papers, key=lambda p: -(p.get("citation_count") or 0))[:5]
for p in top:
    if p.get("doi"):
        result = snowball(p["doi"], direction="both", limit=15)
        papers.extend(result.get("forward", []))
        papers.extend(result.get("backward", []))

# Also snowball seminal papers you know are relevant
seminal_dois = [
    # Add known seminal papers here, e.g.:
    # "10.2307/25148667",  # DeLone & McLean IS Success
]
for doi in seminal_dois:
    result = snowball(doi, direction="forward", limit=20)
    papers.extend(result.get("forward", []))

papers = deduplicate_papers(papers)

Step 4: Export & Save

# Save for further analysis
papers_to_csv(papers, "literature_base.csv")
papers_to_bibtex_file(papers, "references.bib")

Screening

Quick Screening (for building a paper's literature base)

Not a full SLR — just filter the most relevant papers:

• Year range appropriate?
• Published in a reputable venue?
• Title/abstract clearly relevant to the research questions?
• Methodology aligned with what we're looking for?

Formal SLR Screening

Use `scripts/screening.py` for systematic reviews:

from scripts.screening import screen_title_abstract, save_screening_results, generate_prisma_counts

results = screen_title_abstract(
    papers=papers,
    include_keywords=["generative AI", "LLM", "AI agent", "implementation", "adoption", "organization"],
    exclude_keywords=["medical imaging", "drug discovery", "protein folding"],
    min_year=2020,
    require_abstract=True
)
save_screening_results(results, "screening/")
print(generate_prisma_counts(results))

SLR Protocol Template (PRISMA-aligned)

## Systematic Literature Review Protocol

### Research Questions
- RQ1: [Primary question]
- RQ2: [Secondary question]

### Search Strategy
- Databases: Semantic Scholar, OpenAlex, CrossRef, arXiv [+ manual: AIS eLibrary, Google Scholar]
- Search terms: [list all queries]
- Date range: [YYYY] to [YYYY]
- Language: English [+ German if applicable]

### Inclusion Criteria
- IC1: [criterion]
- IC2: [criterion]
- IC3: [criterion]

### Exclusion Criteria
- EC1: [criterion]
- EC2: [criterion]

### Quality Assessment
- [criteria: VHB B+, peer-reviewed, minimum citation threshold for older papers]

### Data Extraction Categories
- [list what to extract from each paper]

### Synthesis Method
- [Webster & Watson concept matrix / thematic synthesis / meta-analysis / vote counting]

Organizing Literature: The Concept Matrix

The single most important tool for turning a pile of papers into a structured literature review (Webster & Watson, 2002).

How to Build One

| Source | [Concept A] | [Concept B] | [Concept C] | [Concept D] | Method | Context |
|--------|------------|------------|------------|------------|----