Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Scaffold or audit a social-science replication package at a target directory. Generates folder structure, README, master.R, figure/table crosswalk, codebook template, LICENSE placeholder, and pre-release checklist. Adapted from Yusaku Horiuchi's replication-package-guide with FAIR-principle integration; platform-neutral (Harvard Dataverse, OSF, Zenodo, GitHub releases, institutional archives).
Machine learning: clustering, PCA/t-SNE/UMAP, classification, prediction regression (Ridge/Lasso/ensemble), cross-validation, Pipelines. For unsupervised analysis, classification, or prediction. For econometric regression use pyfixest/statsmodels.
Develop high-performance C/C++ plugins for Stata using the stplugin.h SDK. Use when the user asks to create a Stata plugin, write C/C++ code for Stata, accelerate a Stata command with C, build cross-platform Stata plugins, or translate/port a Python or R package into Stata. Covers the full lifecycle: SDK setup, data flow, memory safety, .ado wrappers with preserve/merge, cross-platform compilation, performance optimization (pthreads, pre-sorted indices, XorShift RNG), debugging, and distribution via net install. Also includes a translation workflow for porting Python/R packages to Stata — wrapping existing C++ backends when available, or writing C from scratch when not.
Stata-to-Python translation for data analysis. Maps Stata commands (reghdfe, xtreg, ivregress, margins, esttab, svy:) to Python (polars, pyfixest, statsmodels, svy). Use when user has Stata background or requests Stata-equivalent code comments.
Prepare a replication package for the sewage-house-prices project. Generates AEA-compliant README, master script, numbered script order, install script, and deposit checklist. Validates the package against 10 verification checks. This skill should be used when asked to "prepare replication", "data deposit", "create replication package", or "package for submission".
Dense, machine-readable API reference for PyFixest — high-dimensional fixed-effects OLS/WLS/IV and Poisson (feols, fepois, feglm), clustered/robust standard errors, R-style formula syntax, and post-estimation. Use when writing or debugging Python fixed-effects regressions with the pyfixest package.
Reproducible Python environments, notebooks, and literate programming
Improve, modernize, and optimize existing Stata code for performance, portability, and maintainability. Use when legacy patterns such as preserve/restore, cd,
Use when building a Django app with the Viewflow library — workflows (BPMN), CRUD viewsets, FSM state transitions, jsonstore process data, or Viewflow forms. Loads Viewflow conventions and the full docs.
Creates, edits, and migrates Letta Code statusline mods. Use when handling the /statusline command or continuing work started by /statusline.