peer-reviewer

Sociology Analysis Agents for Claude Code

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

# 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:

Skill	Invocation	Description
R Statistical Analyst	/r-analyst	Phased quantitative analysis workflow using R (DiD, IV, matching, etc.)
Stata Statistical Analyst	/stata-analyst	Phased quantitative analysis workflow using Stata
Interview Analyst	/interview-analyst	Pragmatic qualitative analysis for interview data
Interview Write-Up	/interview-writeup	Write-up support for interview methods and findings
DAG Development	/dag-development	Develop causal diagrams and render publication-ready figures (Mermaid, R, Python)
Abductive Analyst	/abductive-analyst	Abductive analysis (Timmermans & Tavory) for theory-generating qualitative research
Text Analyst	/text-analyst	Computational text analysis with R and Python (topic models, sentiment, classification)
Lecture Designer	/lecture-designer	Transform textbook chapters into engaging lectures with Quarto slides
Lit Review	/lit-review	Build 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)

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)

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)

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)

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

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

.claude-plugin/