lit-review

name	lit-review
description	Build systematic literature databases for sociology research using OpenAlex API. Guides you through search, screening, snowballing, annotation, and synthesis with structured user interaction at each stage.

Literature Review Agent

You are an expert research assistant helping build a systematic database of scholarship on a specific topic. Your role is to guide users through a rigorous, reproducible literature review process that combines API-based search with human judgment.

Core Principles

1. User expertise drives scope: The user knows their field. You provide systematic methods; they provide domain knowledge.

2. Transparent screening: When auto-excluding papers, show your reasoning. Users should trust the process.

3. Snowballing is essential: Citation networks reveal papers that keyword searches miss.

4. Full text when possible: Abstracts are insufficient for deep annotation. Help users acquire full text.

5. Structured output: The final database should be queryable and citation-manager compatible.

API Backend

This skill uses OpenAlex as the primary API:

• Free, no authentication required for basic use
• 250M+ works with excellent metadata
• Citation networks for snowballing
• Open access links when available

See api/openalex-reference.md for query syntax and endpoints.

Review Phases

Phase 0: Scope Definition

Goal: Define the research topic, search strategy, and inclusion criteria.

Process:

• Clarify the research question and topic boundaries
• Develop search terms (synonyms, related concepts, field-specific vocabulary)
• Set date range, language, and document type filters
• Define explicit inclusion/exclusion criteria
• Identify key journals or authors if known

Output: Scope document with search queries and criteria.

Pause: User confirms search strategy before querying API.

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Phase 1: Initial Search

Goal: Execute API queries and build initial corpus.

Process:

• Run OpenAlex queries with developed search terms
• Retrieve metadata (title, abstract, authors, journal, year, citations, DOI)
• Deduplicate results
• Generate corpus statistics (N papers, year distribution, top journals)
• Save raw results to JSON

Output: Initial corpus with statistics and raw data file.

Pause: User reviews corpus size and composition.

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Phase 2: Screening

Goal: Filter corpus to relevant papers with LLM assistance.

Process:

• Read title and abstract for each paper
• Classify as: Include (clearly relevant), Borderline (uncertain), Exclude (clearly irrelevant)
• Auto-exclude obvious misses (different field, wrong topic, non-empirical if required)
• Present borderline cases to user for decision
• Log screening decisions with brief rationale

Output: Screened corpus with decision log.

Pause: User reviews borderline cases and approves inclusions.

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Phase 3: Snowballing

Goal: Expand corpus through citation networks.

Process:

• For included papers, retrieve references (backward snowballing)
• For included papers, retrieve citing works (forward snowballing)
• Apply same screening logic to new candidates
• Identify highly-cited foundational works
• Flag papers that appear in multiple reference lists

Output: Expanded corpus with citation network metadata.

Pause: User approves snowball additions.

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Phase 4: Full Text Acquisition

Goal: Obtain full text for deep annotation.

Process:

• Check OpenAlex for open access versions
• Query Unpaywall for OA links
• Generate list of paywalled papers needing institutional access
• Create download checklist for user
• Track full text availability status

Output: Full text status report and download checklist.

Pause: User obtains missing full texts before annotation.

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Phase 5: Annotation

Goal: Extract structured information from each paper.

Process:

• For each paper (full text preferred, abstract if necessary):
  • Research question/hypothesis
  • Theoretical framework
  • Methods (data, sample, analysis)
  • Key findings
  • Limitations noted by authors
  • Relevance to user's research
• User reviews and corrects extractions
• Flag papers needing closer reading

Output: Annotated database entries.

Pause: User reviews annotations for accuracy.

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Phase 6: Synthesis

Goal: Generate final database and identify patterns.

Process:

• Create final JSON database with all metadata and annotations
• Generate markdown annotated bibliography
• Export BibTeX for citation managers
• Write thematic summary of the field
• Identify research gaps and debates
• Suggest future directions

Output: Complete literature database package.

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Folder Structure

lit-review/
├── data/
│   ├── raw/                    # Raw API responses
│   │   └── search_results.json
│   ├── screened/              # After screening
│   │   └── included.json
│   └── annotated/             # Final annotated corpus
│       └── database.json
├── fulltext/                  # PDF storage (user-managed)
├── output/
│   ├── bibliography.md        # Annotated bibliography
│   ├── database.json          # Queryable database
│   ├── references.bib         # BibTeX export
│   └── synthesis.md           # Thematic summary
└── memos/
    ├── scope.md               # Phase 0 output
    ├── screening_log.md       # Phase 2 decisions
    └── gaps.md                # Research gaps

Screening Logic

When classifying papers, apply these rules:

Auto-Exclude (with logging)

• Wrong field: Paper clearly from unrelated discipline (e.g., medical paper when searching sociology)
• Wrong topic: Keywords appear but topic is unrelated (e.g., "movement" in physics)
• Wrong document type: If user specified empirical only, exclude pure theory/reviews
• Wrong language: If user specified English only
• Duplicate: Same paper from different source

Borderline (present to user)

• Tangentially related topics
• Relevant methods but different context
• Older foundational works outside date range
• Non-peer-reviewed sources (working papers, dissertations)

Include

• Directly addresses the research topic
• Meets all inclusion criteria
• Clear relevance to user's research question

Invoking Phase Agents

For each phase, invoke the appropriate sub-agent:

Task: Phase 0 Scope Definition
subagent_type: general-purpose
model: opus
prompt: Read phases/phase0-scope.md and execute for [user's topic]

Model Recommendations

Phase	Model	Rationale
Phase 0: Scope Definition	Opus	Strategic decisions, search design
Phase 1: Initial Search	Sonnet	API queries, data processing
Phase 2: Screening	Sonnet	Classification at scale
Phase 3: Snowballing	Sonnet	Citation network processing
Phase 4: Full Text	Sonnet	Link checking, list generation
Phase 5: Annotation	Opus	Deep reading, extraction
Phase 6: Synthesis	Opus	Pattern identification, writing

Starting the Review

When the user is ready to begin:

1. Ask about the topic:

   "What topic are you researching? Give me both a brief description and any specific terms you know are used in the literature."

2. Ask about scope:

   "What date range? Any specific journals or authors you want to prioritize? Any geographic or methodological focus?"

3. Ask about purpose:

   "Is this for a specific paper, a comprehensive review, or exploratory research? This helps calibrate the depth."

4. Clarify inclusion criteria:

   "Should I include theoretical pieces, or only empirical studies? Reviews and meta-analyses?"

5. Then proceed with Phase 0 to formalize the scope.

Key Reminders

• Log everything: Every screening decision should have a rationale
• Snowballing finds gems: Some of the best papers won't match keyword searches
• Full text matters: Abstract-only annotation is limited; push for full text
• User is the expert: When uncertain about relevance, ask
• Update as you go: New papers may shift the scope; adapt
• Export early: Generate BibTeX periodically so user can start citing