WSU-Carbon-Lab

Pandas and Polars for tabular data: when to use which, indexing and dtypes, joins and reshaping, lazy Polars, I/O and Parquet, nulls, Arrow interop, and performance. Use for DataFrame/Series/LazyFrame code. Triggers on pandas, polars, DataFrame, LazyFrame, parquet, groupby, merge, join.

Python stack hub: uv/ruff/ty workflows, builtins and collections, functions and classes, dataclasses, typing boundaries, pytest and NumPy-style docs. Use for any Python task in repos that follow this stack; pairs with python-reviewer, python-types, and python-refactor. Related skills: numpy-scientific, dataframes, numpy-docstrings, matplotlib-scientific, lab-instrumentation. Triggers on Python, uv, ruff, ty, pytest, dataclass, typing.

Applies cross-language defaults for scientific/engineering work, documentation discipline, non-lazy execution, and effective use of rules, skills, and documentation tools.

PyVISA and lab instrument control in Python: VISA resource lifecycle, timeouts and terminations, raw TCP/UDP sockets vs VISA, when to introduce hardware abstraction layers, validating user and config input before hardware, testing without hardware, and extracting tables or text from instrument manuals and datasheets (PDF). Triggers on pyvisa, VISA, GPIB, USB-TMC, serial instrument, socket lab, SCPI, datasheet PDF, instrument driver.

Build publication-quality Matplotlib figures for scientific Python. Use when plotting experiment or analysis results, multi-panel figures, journal-sized layouts, tick formatting, legends (fancy/plain, placement, compact handles), point annotations, inset zooms, twin axes, colored spines/ticks, colorblind-safe palettes, PNG/PDF export, or SciencePlots styling. Triggers on matplotlib, pyplot, subplots, savefig, legend, annotate, inset, twinx, scientific figures, panel labels.

Write and review NumPy-style (numpydoc) docstrings for Python public APIs. Covers section order, semantics (what belongs in docstrings vs types vs tests), anti-patterns (comment soup, stub docs, wrong style), Parameters, Returns, Yields, Raises, See Also, Notes, References, Examples, and class/module docs. Use when authoring or auditing docstrings. Triggers on docstring, numpydoc, Parameters, Returns, Examples, Sphinx, TODO, comment.

NumPy for scientific Python: dtypes and casting, creation, reshaping, broadcasting, indexing vs views, ufuncs and reductions, linear algebra and einsum, random Generator, I/O, structured arrays, performance and pandas boundaries. Use when writing or reviewing ndarray code. Triggers on numpy, ndarray, broadcasting, dtype, ufunc, einsum, vectorization.

Applies dotagent conventions for Rust PyO3 and Maturin extension crates on top of the base Rust stack.

HeroUI v3 React component library (Tailwind CSS v4 + React Aria). Use when building UIs with HeroUI — creating Buttons, Modals, Forms, Cards; installing @heroui/react; configuring dark/light themes with oklch variables; or fetching component docs. Keywords: HeroUI, Hero UI, heroui, @heroui/react, @heroui/styles.

TypeScript hub for Bun workflows, tsconfig and tsc, interface vs type vs enum, no any, unknown only with explicit user approval, ESM, lint/format, Vitest or Bun test, JSDoc or TSDoc, delegation to typescript-types. Triggers on TypeScript, interface, type, enum, any, unknown, Bun, tsc, eslint.

HeroUI as the only component library: v3 imports from @heroui/react, theme tokens from globals.css, Tailwind composition without fighting HeroUI variants, forms and accessibility hooks. Triggers on HeroUI, heroui, Button, Input, Modal, TSX UI.

Deep TypeScript typing for discriminated unions, exhaustive handling, generics, satisfies, branding, and fixing tsc output without any or silent unknown on public APIs. Use when resolving complex type errors or designing type-heavy APIs. Pairs with general-typescript (no any; unknown only with explicit user approval). Triggers on generics, union, narrowing, exhaustive, satisfies, inference.

T3 Next.js App Router umbrella: stack roles, src/ layout, RSC vs client, HeroUI-only UI, globals.css theme authority. Load web-trpc-api, web-url-search-state, heroui-components for depth; general-typescript for Bun and TS.

tRPC procedures with auth context, Zod inputs, query vs mutation semantics, Prisma on server, error mapping, and optional Next.js REST route handlers that share validation. For T3 Next.js apps. Triggers on tRPC, router, procedure, protectedProcedure, prisma, zod, app/api route.ts.

Next.js App Router URL search state: Zod schemas for searchParams, shareable list/filter URLs, sync with Link and router, SSR-friendly patterns. Triggers on searchParams, nuqs, URL, query string, bookmark.

Xray Atlas project guidelines - stack, architecture, auth, design review, and production build. Use for stack questions, design architecture, auth patterns, UI review, accessibility audit, or pre-deployment checklist.

Pandas and Polars for tabular data: when to use which, indexing and dtypes, joins and reshaping, lazy Polars, I/O and Parquet, nulls, Arrow interop, and performance. Use for DataFrame/Series/LazyFrame code. Triggers on pandas, polars, DataFrame, LazyFrame, parquet, groupby, merge, join.

Python stack hub: uv/ruff/ty workflows, builtins and collections, functions and classes, dataclasses, typing boundaries, pytest and NumPy-style docs. Use for any Python task in repos that follow this stack; pairs with python-reviewer, python-types, and python-refactor. Related skills: numpy-scientific, dataframes, numpy-docstrings, matplotlib-scientific, lab-instrumentation. Triggers on Python, uv, ruff, ty, pytest, dataclass, typing.

Applies cross-language defaults for scientific/engineering work, documentation discipline, non-lazy execution, and effective use of rules, skills, and documentation tools.

PyVISA and lab instrument control in Python: VISA resource lifecycle, timeouts and terminations, raw TCP/UDP sockets vs VISA, when to introduce hardware abstraction layers, validating user and config input before hardware, testing without hardware, and extracting tables or text from instrument manuals and datasheets (PDF). Triggers on pyvisa, VISA, GPIB, USB-TMC, serial instrument, socket lab, SCPI, datasheet PDF, instrument driver.

Build publication-quality Matplotlib figures for scientific Python. Use when plotting experiment or analysis results, multi-panel figures, journal-sized layouts, tick formatting, legends (fancy/plain, placement, compact handles), point annotations, inset zooms, twin axes, colored spines/ticks, colorblind-safe palettes, PNG/PDF export, or SciencePlots styling. Triggers on matplotlib, pyplot, subplots, savefig, legend, annotate, inset, twinx, scientific figures, panel labels.

Write and review NumPy-style (numpydoc) docstrings for Python public APIs. Covers section order, semantics (what belongs in docstrings vs types vs tests), anti-patterns (comment soup, stub docs, wrong style), Parameters, Returns, Yields, Raises, See Also, Notes, References, Examples, and class/module docs. Use when authoring or auditing docstrings. Triggers on docstring, numpydoc, Parameters, Returns, Examples, Sphinx, TODO, comment.

NumPy for scientific Python: dtypes and casting, creation, reshaping, broadcasting, indexing vs views, ufuncs and reductions, linear algebra and einsum, random Generator, I/O, structured arrays, performance and pandas boundaries. Use when writing or reviewing ndarray code. Triggers on numpy, ndarray, broadcasting, dtype, ufunc, einsum, vectorization.

Python and Jupyter notebook performance optimization guidelines for X-ray data science (XRR, SAXS, WAXS, XRD, XAS, NEXAFS). This skill should be used when writing, reviewing, or refactoring Python data science code to ensure optimal performance patterns and correct physics-based analysis. Triggers on tasks involving X-ray data analysis, reflectivity fitting, spectroscopic analysis, or scientific computing workflows.