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Found 317 Skills
Develop causal diagrams (DAGs) from social-science research questions and literature, then render publication-ready figures using Mermaid, R, or Python.
Write article introductions and conclusions for sociology interview research. Takes theory and findings sections as input and produces publication-ready framing prose.
Draft publication-ready Methods sections for interview-based sociology articles. Guides pathway selection, component coverage, and calibration based on analysis of 77 Social Problems/Social Forces articles.
Computational text analysis for sociology research using R or Python. Guides you through topic models, sentiment analysis, classification, and embeddings with systematic validation. Supports both traditional (LDA, STM) and neural (BERT, BERTopic) methods.
Orchestrate manuscript revision by routing feedback to specialized writing skills
Simulate peer review by constructing reviewer personas from Zotero sources. Identifies relevant perspectives, retrieves full texts, builds reviewer profiles, and generates focused reviews on theory/methods and findings.
Draft case justification sections for interview-based sociology articles. Guides cluster selection, component coverage, and calibration based on analysis of 32 Social Problems/Social Forces articles.
Draft publication-ready Theory sections for sociology research. Guides structure, paragraph functions, sentence craft, and calibration based on analysis of 80 Social Problems/Social Forces articles.
Deep reading and synthesis of literature corpus. Theoretical mapping, thematic clustering, and debate identification using Zotero MCP for full-text access.
Pragmatic qualitative analysis for interview data in sociology research. Guides you through systematic coding, interpretation, and synthesis with quality checkpoints. Supports theory-informed (Track A) or data-first (Track B) approaches.
R statistical analysis for publication-ready sociology research. Guides you through phased workflows for DiD, IV, matching, panel methods, and more. Use when doing quantitative analysis in R for academic papers.
Analyze user retention and churn using survival analysis, cohort analysis, and machine learning. Calculate retention rates, build survival curves, predict churn risk, and generate retention optimization strategies. Use when working with user subscription data, membership information, or when user mentions retention, churn, survival analysis, or customer lifetime value.