BeClaude

lit-synthesis

New
12Community RegistryDevelopmentby Neal Caren · MIT

Deep reading and synthesis of literature corpus. Theoretical mapping, thematic clustering, and debate identification using Zotero MCP for full-text access.

First seen 5/22/2026
View Source

Summary

This skill enables deep reading and synthesis of a literature corpus by leveraging Zotero MCP for full-text access.

  • It performs theoretical mapping, thematic clustering, and debate identification to help researchers systematically organize and analyze scholarly works.

Overview

A Claude Code plugin marketplace with skills for rigorous quantitative and qualitative analysis in sociology and related social sciences. These skills guide you through systematic, publication-ready research workflows.

Installation

bash
# Add this marketplace to Claude Code
/plugin marketplace add nealcaren/social-data-analysis

# Install only the plugins you need
/plugin install r-analyst@social-data-analysis
/plugin install stata-analyst@social-data-analysis
/plugin install interview-analyst@social-data-analysis
/plugin install interview-writeup@social-data-analysis
/plugin install dag-development@social-data-analysis
/plugin install abductive-analyst@social-data-analysis
/plugin install text-analyst@social-data-analysis
/plugin install lecture-designer@social-data-analysis
/plugin install lit-review@social-data-analysis

Available Plugins

Each plugin provides a single focused skill. Install only what you need:

SkillInvocationDescription
R Statistical Analyst/r-analystPhased quantitative analysis workflow using R (DiD, IV, matching, etc.)
Stata Statistical Analyst/stata-analystPhased quantitative analysis workflow using Stata
Interview Analyst/interview-analystPragmatic qualitative analysis for interview data
Interview Write-Up/interview-writeupWrite-up support for interview methods and findings
DAG Development/dag-developmentDevelop causal diagrams and render publication-ready figures (Mermaid, R, Python)
Abductive Analyst/abductive-analystAbductive analysis (Timmermans & Tavory) for theory-generating qualitative research
Text Analyst/text-analystComputational text analysis with R and Python (topic models, sentiment, classification)
Lecture Designer/lecture-designerTransform textbook chapters into engaging lectures with Quarto slides
Lit Review/lit-reviewBuild literature databases via OpenAlex

Each skill uses a phased workflow with mandatory pauses between phases for user review and decision-making.

Workflow Overview

Quantitative Analysis (R/Stata)

code
Phase 0: Research Design → Establish identification strategy
    ↓ [User Review]
Phase 1: Data Familiarization → Descriptives, quality checks
    ↓ [User Review]
Phase 2: Model Specification → Pre-specify models before estimation
    ↓ [User Review]
Phase 3: Main Analysis → Run models, interpret results
    ↓ [User Review]
Phase 4: Robustness → Sensitivity analysis, placebo tests
    ↓ [User Review]
Phase 5: Output → Publication-ready tables, figures, narrative

Qualitative Analysis (Interviews)

code
Phase 0: Theory Preparation → Sensitizing concepts (optional)
    ↓ [User Review]
Phase 1: Immersion → Read transcripts, create memos
    ↓ [User Review]
Phase 2: Coding → Develop codebook, apply codes
    ↓ [User Review]
Phase 3: Interpretation → Identify patterns, develop explanations
    ↓ [User Review]
Phase 4: Quality Check → Assess against 5 quality indicators
    ↓ [User Review]
Phase 5: Synthesis → Write publication-ready sections

Abductive Analysis (Timmermans & Tavory)

code
Phase 0: Theoretical Preparation → Build theoretical sensitivity
    ↓ [User Review]
Phase 1: Familiarization → Open coding, flag surprises
    ↓ [User Review]
Phase 2: Theoretical Casing → Apply multiple theoretical lenses
    ↓ [User Review]
Phase 3: Anomaly Analysis → Identify contradictions and puzzles
    ↓ [User Review]
Phase 4: Memo Writing → Develop tentative theory
    ↓ [User Review]
Phase 5: Integration → Test theory against full dataset
    ↓ [User Review]
Phase 6: Writing Up → Rhetorical abduction for publication

Computational Text Analysis (R/Python)

code
Phase 0: Research Design → Method selection, language choice (R or Python)
    ↓ [User Review]
Phase 1: Corpus Preparation → Load, clean, explore text data
    ↓ [User Review]
Phase 2: Specification → Document preprocessing, specify parameters
    ↓ [User Review]
Phase 3: Analysis → Run topic models, classifiers, sentiment
    ↓ [User Review]
Phase 4: Validation → Human validation, diagnostics, robustness
    ↓ [User Review]
Phase 5: Output → Publication-ready tables, figures, replication

Lecture Design

code
Phase 0: Context & Outcomes → Define measurable learning outcomes
    ↓ [Instructor Review]
Phase 1: Content Audit → Narrative arc (ABT), chunk map, hook design
    ↓ [Instructor Review]
Phase 2: Active Learning → Polls, ConcepTests, peer instruction
    ↓ [Instructor Review]
Phase 3: Slide Development → Quarto reveal.js with speaker notes
    ↓ [Instructor Review]
Phase 4: Review → Timing audit, backup plans, instructor guide

Repository Structure

code
.claude-plugin/
└── marketplace.json              # Plugin marketplace definition (9 plugins)

plugins/
├── r-analyst/
│   └── skills/r-analyst/
│       ├── SKILL.md              # R orchestrator
│       ├── phases/               # Phase agents
│       └── techniques/           # R code reference guides
│
├── stata-analyst/
│   └── skills/stata-analyst/
│       ├── SKILL.md              # Stata orchestrator
│       ├── phases/               # Phase agents
│       └── techniques/           # Stata code reference guides
│
├── interview-analyst/
│   └── skills/interview-analyst/
│       ├── SKILL.md              # Interview orchestrator
│       └── phases/               # Phase agents
│
├── interview-writeup/
│   └── skills/interview-writeup/
│       ├── SKILL.md              # Interview write-up orchestrator
│       └── phases/               # Phase agents
│
├── dag-development/
│   └── skills/dag-development/
│       ├── SKILL.md              # DAG development orchestrator
│       └── phases/               # Phase agents
│
├── abductive-analyst/
│   └── skills/abductive-analyst/
│       ├── SKILL.md              # Abductive analysis orchestrator
│       └── phases/               # Phase agents (7 phases)
│
├── text-analyst/
│   └── skills/text-analyst/
│       ├── SKILL.md              # Text analysis orchestrator
│       ├── phases/               # Phase agents
│       ├── concepts/             # Method concepts (language-agnostic)
│       ├── r-techniques/         # R text analysis code guides
│       └── python-techniques/    # Python text analysis code guides
│
└── lecture-designer/
    └── skills/lecture-designer/
        ├── SKILL.md              # Lecture design orchestrator
        ├── phases/               # Phase agents
        ├── pedagogy/             # Teaching methodology (overview)
        └── quarto/               # Quarto reveal.js reference

└── lit-review/
    └── skills/lit-review/
        ├── SKILL.md              # Literature review orchestrator
        ├── phases/               # Phase agents
        └── api/                  # OpenAlex API reference

Key Features

Quantitative Skills

  • •Identification-first: Establish research design before estimation
  • •Pre-specification: Document model choices before seeing results
  • •Robustness built-in: Sensitivity analysis, placebo tests, wild bootstrap
  • •Nonlinear model interpretation: AMEs, predicted probabilities, proper diagnostics
  • •Missing data handling: Multiple imputation with adequate m
  • •Survey methodology: Weighting, design effects, response rates
  • •Publication checklists: Minimum, strong, and exemplary standards

Qualitative Skills

  • •Theory-informed or data-first: Choose your approach
  • •Systematic coding: Codebook development with examples
  • •Quality indicators: Cognitive empathy, heterogeneity, palpability, follow-up, self-awareness
  • •Evidence selection: Luminous exemplars, not just typical quotes
  • •Methods transparency: Detailed templates for sampling, recruitment, saturation
  • •Write-up support: Methods drafting, findings structure, quote use, and revision checklists

Methods Skills

  • •DAG development: Build causal diagrams from theory and render figures in Mermaid, R, or Python

Abductive Analysis Skills

  • •Theory-first approach: Build theoretical sensitivity before data engagement
  • •Map and compass theories: Both substantive and grammatical frameworks
  • •Anomaly detection: Systematic identification of contradictions and puzzles
  • •Theoretical casing: View data through multiple theoretical lenses
  • •Rhetorical abduction: Structure writing as what we knew → surprise → new theory

Text Analysis Skills

  • •Dual-language support: R for topic models/STM; Python for transformers/BERTopic
  • •Method selection guidance: Match methods to research questions
  • •Validation required: Human validation, coherence metrics, robustness checks
  • •Topic modeling: LDA, STM (R), BERTopic (Python) with K selection guidance
  • •Sentiment analysis: VADER, lexicon-based, and ML approaches
  • •Supervised classification: Traditional ML and transformer fine-tuning
  • •Reproducibility: Documented preprocessing, seeds, package versions

Lecture Design Skills

  • •Learning outcomes first: Backward design from measurable outcomes
  • •Narrative structure: ABT (And-But-Therefore) for cognitive engagement
  • •Cognitive load management: Chunking, attention resets every 12-18 minutes
  • •Active learning integration: Polls, ConcepTests, Peer Instruction
  • •Quarto reveal.js output: Publication-quality slides with speaker notes

Requirements

  • •Claude Code CLI
  • •R (for R skills) or Stata (for Stata skills)
  • •Interview transcripts (for interview skill)
  • •R and/or Python (for text analysis skill)

Contributing

Contributions welcome! Please:

  1. Fork the repo
  2. Create a feature branch
  3. Submit a pull request

License

MIT License - see LICENSE file

Acknowledgments

These skills draw on methodological guidance from:

  • •Gerson & Damaske, The Science and Art of Interviewing
  • •Small & Calarco, Qualitative Literacy
  • •Long & Mustillo (2017), Mize (2019) on nonlinear model interpretation
  • •Various Social Forces editorial guidelines

Install & Usage

1
Add a marketplace
/plugin marketplace add <org/repo>
2
Install the plugin

Add the configuration to /plugin install lit-synthesis@<marketplace>

3
Manage with /plugin
/plugin

Use Cases

Map theoretical frameworks across a set of papers on social movements to identify key concepts and their relationships.
Cluster a corpus of 50 articles on inequality into emergent themes and sub-themes for a literature review.
Identify and summarize major debates or points of disagreement in the literature on causal inference methods.
Extract and compare definitions of key constructs (e.g., 'social capital') from multiple sources in a Zotero collection.
Generate a chronological narrative of how a specific theory evolved over time based on full-text analysis.
Synthesize findings from a mixed-methods corpus to highlight gaps and propose future research directions.

Usage Examples

1

/lit-synthesis Analyze my Zotero collection 'Sociology of Education' and produce a thematic map with 5-7 clusters.

2

Using the papers in my Zotero folder 'Network Analysis', identify the three main theoretical debates and summarize each side's arguments.

3

/lit-synthesis From the Zotero group library 'Climate Justice', extract all definitions of 'environmental racism' and compare them in a table.

View source on GitHub
mcpliterature-synthesiszoterotheoretical-mappingsociologyresearch

Security Audits

LicensePassSourceWarnRepositoryPass

Frequently Asked Questions

What is lit-synthesis?

This skill enables deep reading and synthesis of a literature corpus by leveraging Zotero MCP for full-text access. It performs theoretical mapping, thematic clustering, and debate identification to help researchers systematically organize and analyze scholarly works.

How to install lit-synthesis?

To install lit-synthesis: add a marketplace (/plugin marketplace add <org/repo>), then add the config to /plugin install lit-synthesis@<marketplace>. Finally, /plugin in Claude Code.

What is lit-synthesis best for?

lit-synthesis is a plugin categorized under Development. It is designed for: mcp, literature-synthesis, zotero, theoretical-mapping, sociology, research. Created by Neal Caren.

What can I use lit-synthesis for?

lit-synthesis is useful for: Map theoretical frameworks across a set of papers on social movements to identify key concepts and their relationships.; Cluster a corpus of 50 articles on inequality into emergent themes and sub-themes for a literature review.; Identify and summarize major debates or points of disagreement in the literature on causal inference methods.; Extract and compare definitions of key constructs (e.g., 'social capital') from multiple sources in a Zotero collection.; Generate a chronological narrative of how a specific theory evolved over time based on full-text analysis.; Synthesize findings from a mixed-methods corpus to highlight gaps and propose future research directions..