skills/43-wentorai-research-plugins/skills/writing/latex/math-typesetting-guide/SKILL.md
LaTeX math typesetting, equation formatting, and cross-referencing
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research math-typesetting-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Comprehensive reference for typesetting mathematical notation, equations, and theorems in LaTeX with correct formatting, numbering, and cross-referencing.
\usepackage{amsmath} % Core math environments (align, gather, etc.)
\usepackage{amssymb} % Additional math symbols
\usepackage{amsthm} % Theorem environments
\usepackage{mathtools} % Extensions to amsmath (dcases, coloneqq, etc.)
\usepackage{bm} % Bold math symbols (\bm{x})
\usepackage{bbm} % Blackboard bold for indicators (\mathbbm{1})
\usepackage{nicefrac} % Inline fractions (\nicefrac{1}{2})
\usepackage{siunitx} % SI units (\SI{9.8}{m/s^2})
Use $...$ or \(...\) for math within text:
The loss function $\mathcal{L}(\theta) = -\sum_{i=1}^{N} \log p(y_i | x_i; \theta)$
minimizes the negative log-likelihood.
Use \[...\] for centered, unnumbered equations:
\[
\nabla_\theta \mathcal{L}(\theta) = -\frac{1}{N} \sum_{i=1}^{N}
\nabla_\theta \log p(y_i | x_i; \theta)
\]
Use the equation environment for numbered equations:
\begin{equation}
E = mc^2
\label{eq:einstein}
\end{equation}
Reference with \eqref{eq:einstein} to produce "(1)" with parentheses automatically.
Use align for multi-line equations with alignment points (&):
\begin{align}
\mathcal{L}(\theta) &= \mathbb{E}_{(x,y) \sim \mathcal{D}} \left[ \ell(f_\theta(x), y) \right] \label{eq:loss} \\
&= \frac{1}{N} \sum_{i=1}^{N} \ell(f_\theta(x_i), y_i) \label{eq:empirical-loss} \\
&\approx \frac{1}{B} \sum_{j=1}^{B} \ell(f_\theta(x_j), y_j) \label{eq:minibatch-loss}
\end{align}
Use align* for unnumbered multi-line equations. Use \nonumber to suppress numbering on specific lines.
Use split inside equation for a single equation number spanning multiple lines:
\begin{equation}
\begin{split}
\text{ELBO}(\theta, \phi; x) &= \mathbb{E}_{q_\phi(z|x)} \left[ \log p_\theta(x|z) \right] \\
&\quad - D_\text{KL}\left( q_\phi(z|x) \| p(z) \right)
\end{split}
\label{eq:elbo}
\end{equation}
For piecewise functions:
\begin{equation}
\text{ReLU}(x) =
\begin{cases}
x & \text{if } x > 0 \\
0 & \text{otherwise}
\end{cases}
\label{eq:relu}
\end{equation}
| Notation | LaTeX | Category |
|----------|-------|----------|
| Real numbers | \mathbb{R} | Sets |
| Integers | \mathbb{Z} | Sets |
| Natural numbers | \mathbb{N} | Sets |
| Expectation | \mathbb{E} | Probability |
| Probability | \mathbb{P} or \Pr | Probability |
| Normal distribution | \mathcal{N}(\mu, \sigma^2) | Distributions |
| Partial derivative | \frac{\partial f}{\partial x} | Calculus |
| Gradient | \nabla f | Calculus |
| Matrix transpose | \mathbf{A}^\top | Linear algebra |
| Matrix inverse | \mathbf{A}^{-1} | Linear algebra |
| Frobenius norm | \|\mathbf{A}\|_F | Linear algebra |
| L2 norm | \|\mathbf{x}\|_2 | Linear algebra |
| Inner product | \langle \mathbf{x}, \mathbf{y} \rangle | Linear algebra |
| Indicator function | \mathbbm{1}_{[condition]} | Functions |
| Summation | \sum_{i=1}^{N} | Operations |
| Product | \prod_{i=1}^{N} | Operations |
| Argmin/argmax | \operatorname*{argmin}_\theta | Optimization |
| KL divergence | D_\text{KL}(p \| q) | Information theory |
Define custom operators for clean notation:
% In preamble
\DeclareMathOperator*{\argmin}{arg\,min}
\DeclareMathOperator*{\argmax}{arg\,max}
\DeclareMathOperator{\Tr}{Tr} % Matrix trace
\DeclareMathOperator{\diag}{diag} % Diagonal matrix
\DeclareMathOperator{\softmax}{softmax}
\DeclareMathOperator{\sigmoid}{\sigma}
\newcommand{\R}{\mathbb{R}} % Shorthand for real numbers
\newcommand{\E}{\mathbb{E}} % Shorthand for expectation
\newcommand{\norm}[1]{\left\| #1 \right\|} % Norm shorthand
\newcommand{\abs}[1]{\left| #1 \right|} % Absolute value
\newcommand{\inner}[2]{\langle #1, #2 \rangle} % Inner product
% Matrix with parentheses
\begin{equation}
\mathbf{W} = \begin{pmatrix}
w_{11} & w_{12} & \cdots & w_{1n} \\
w_{21} & w_{22} & \cdots & w_{2n} \\
\vdots & \vdots & \ddots & \vdots \\
w_{m1} & w_{m2} & \cdots & w_{mn}
\end{pmatrix}
\end{equation}
% Matrix with square brackets
\begin{equation}
\mathbf{A} = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix}
\end{equation}
% In preamble: define theorem environments
\newtheorem{theorem}{Theorem}[section]
\newtheorem{lemma}[theorem]{Lemma}
\newtheorem{proposition}[theorem]{Proposition}
\newtheorem{corollary}[theorem]{Corollary}
\theoremstyle{definition}
\newtheorem{definition}[theorem]{Definition}
\theoremstyle{remark}
\newtheorem{remark}[theorem]{Remark}
% In document:
\begin{theorem}[Universal Approximation]
\label{thm:universal-approx}
For any continuous function $f: [0,1]^n \to \mathbb{R}$ and any
$\epsilon > 0$, there exists a feedforward neural network $g$ with
one hidden layer such that $\sup_{x \in [0,1]^n} |f(x) - g(x)| < \epsilon$.
\end{theorem}
\begin{proof}
The proof proceeds by construction. Consider a network with
$\sigmoid$ activation functions...
% End proof with QED symbol (automatic with amsthm)
\end{proof}
% Use cleveref for automatic reference formatting
\usepackage[capitalise,noabbrev]{cleveref}
% Then reference with:
\cref{eq:loss} % -> "Equation 1"
\cref{thm:universal-approx} % -> "Theorem 1"
\Cref{eq:loss} % -> "Equation 1" (capital, for start of sentence)
\crefrange{eq:loss}{eq:minibatch-loss} % -> "Equations 1 to 3"
% Label naming conventions:
% eq:name for equations
% thm:name for theorems
% lem:name for lemmas
% def:name for definitions
% fig:name for figures
% tab:name for tables
% sec:name for sections
\left( and \right) for auto-sizing delimiters, or explicit sizes: \big(, \Big(, \bigg(, \Bigg(\text{...} for words within math mode: $p(\text{data} | \theta)$\quad or \qquad for spacing in equations\phantom{x} for invisible spacing to align elements$$...$$ (plain TeX); use \[...\] or environments insteadtools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
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Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
tools
Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".