topica

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.