nealcaren

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 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.

Meta-skill for creating genre-analysis-based writing skills. Analyzes a corpus of article sections, discovers clusters, and generates complete skills with phases, cluster guides, and techniques.

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.

Orchestrate manuscript revision by routing feedback to specialized writing skills

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.

Write article introductions and conclusions for sociology interview research. Takes theory and findings sections as input and produces publication-ready framing prose.

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.

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.

Turn a finished analysis into a submission-ready replication archive — prove it rebuilds from raw in a clean session, then assemble the master runner, README, codebook, environment snapshot, data-access statement, and an output→exhibit map that journals now…

Read-only code review for Python analysis scripts and notebooks (pandas, statsmodels, scikit-learn, and text-analysis pipelines). Checks reproducibility, correctness, pandas/NumPy numerical traps, data leakage, and whether reported numbers match what the code…

Read-only code review for R analysis scripts. Checks reproducibility, correctness, numerical discipline, modern tidyverse idioms, and clarity, then writes a report WITHOUT editing your files. Use when the user says "review this R script", "check my R code",…

Read-only code review for Stata .do files. Checks reproducibility, correctness, Stata's notorious numerical/missing-value traps, idioms, and whether reported numbers match what the code produces — then writes a report WITHOUT editing your files. Use when the…

Guard against hallucinated statistics in write-ups. Defines the results-ledger convention (analysis scripts emit every reportable number to output/results.json at full precision) and runs a reconciliation pass that flags any number in a report's prose that…

Stata 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 Stata for academic papers.

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.

Working guide for an LLM agent helping a social scientist run a topic-modeling analysis with the topica Python library — building a defensible corpus, choosing and justifying K, fitting a model, validating it, estimating covariate effects with honest…

End-to-end workflow for adding a new topic-modeling algorithm to the topica library (a Rust + PyO3 + numpy package). Use when a user asks to add, port, or implement a topic model (e.g. "add CTM-2", "port BTM", "implement an anchored model") into topica.…

Run a two-agent "sample user" usability audit of a topica model's end-to-end analysis workflow, from the seat of a first-time domain-expert researcher. Use when a user wants to evaluate how usable a model is in practice ("do a sample-user run on LDA",…

Run computational jobs on UNC Chapel Hill's HPC clusters (Longleaf and Sycamore). Use when a user wants to: submit SLURM batch jobs, set up Python/conda environments for ML/deep learning, run PyTorch or TensorFlow training on GPUs, run Hugging Face model…