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.

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. Covers grounding in the literature, finding and running the reference implementation to build a gold-standard result set, implementing a Rust core with Python bindings under topica's conventions, validating with an independent benchmark agent and an author-emulation review agent, and shipping via a GitHub issue + PR that also updates the README and docs.

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 uncertainty, and reporting results. Use when the user wants to USE topica to analyze a text corpus (not extend the library). The companion developer skill for adding a model is add-topic-model.

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 inference or fine-tuning (including Unsloth for fast QLoRA), use Jupyter notebooks on the cluster, transfer data, manage storage, monitor GPU utilization, write SLURM scripts, do hyperparameter sweeps, or anything involving UNC Research Computing infrastructure. Covers both Longleaf (high-throughput, broad GPU selection) and Sycamore (HPC, H100 GPUs).