Stata skills: turns a plain-language data/statistics task into correct, runnable Stata code, with good-vs-bad command guidance, Python↔Stata interop, and a growing recipe library (ARDL, time-series and panel regression, unit-root tests).
Files GitHub issue(s) for a PR's work — one flat issue, or a parent epic plus children via GitHub's native parent/child relationship — then rewrites the PR's title (kept short) and description with an itemized, correctly nested issue list.
Turns a plain-language data/statistics task into correct, runnable Stata code, with good-vs-bad command guidance, Python↔Stata interop, and a growing recipe library (ARDL, time-series and panel regression, unit-root tests).
Docs skills: writes verified user documentation for a library, CLI or framework, structured like FastAPI, Pydantic, DuckDB and Effect docs: Getting Started, Tutorial, How-to Guides, Concepts, Reference, FAQ, SVG diagrams, and llms.txt for AI agents. Every snippet is run before it is published.
Skills for generating images in a variety of styles and applying visual styles or effects to existing images, including art walls and simulated comment overlays.
Persona skills: /prodigy acts as the top performer in any role or field and shares how they frame problems, deliver, avoid pitfalls, decide, and how to level up.
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
Hypothesis testing, A/B experiment analysis, sample size calculation, and confidence intervals. 3 stdlib-only Python tools: Z-test/t-test/chi-square with effect sizes, sample size calculator with power tradeoffs, and Wilson score confidence intervals.
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