skills/43-wentorai-research-plugins/skills/writing/latex/academic-writing-latex/SKILL.md
LaTeX-based academic writing assistant for thesis and paper templates
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research academic-writing-latexInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This guide covers the workflow of writing academic papers and theses using LaTeX templates. It addresses template selection, document structure, common environments, bibliography management, and compilation. Designed for researchers who need to produce professional documents conforming to specific institutional or publisher formatting requirements.
| Publisher | Template Source | Document Class |
|-----------|----------------|---------------|
| IEEE | ieee.org/conferences | IEEEtran |
| ACM | acm.org/publications | acmart |
| Springer | LNCS template package | llncs |
| Elsevier | elsarticle | elsarticle |
| Nature | Author submission guidelines | nature |
| APS/AIP | REVTeX | revtex4-2 |
% Chinese university thesis examples
\documentclass{thuthesis} % Tsinghua University
\documentclass{sjtuthesis} % Shanghai Jiao Tong University
\documentclass{ustcthesis} % USTC
\documentclass{xjtuthesis} % Xi'an Jiao Tong University
% International thesis
\documentclass{Dissertate} % Harvard-style
\documentclass[phd]{novathesis} % Universidade Nova de Lisboa
\documentclass[conference]{IEEEtran}
% Preamble — packages
\usepackage{amsmath,amssymb}
\usepackage{graphicx}
\usepackage{booktabs}
\usepackage{hyperref}
\usepackage[utf8]{inputenc}
\begin{document}
\title{Your Paper Title}
\author{
\IEEEauthorblockN{First Author}
\IEEEauthorblockA{Affiliation\\Email: [email protected]}
\and
\IEEEauthorblockN{Second Author}
\IEEEauthorblockA{Affiliation\\Email: [email protected]}
}
\maketitle
\begin{abstract}
Your abstract here (150-250 words).
\end{abstract}
\begin{IEEEkeywords}
keyword1, keyword2, keyword3
\end{IEEEkeywords}
\section{Introduction}
\label{sec:intro}
Your introduction text...
\section{Related Work}
\section{Methodology}
\section{Experiments}
\section{Results}
\section{Conclusion}
\bibliographystyle{IEEEtran}
\bibliography{references}
\end{document}
\documentclass[12pt,a4paper]{report}
\begin{document}
\frontmatter
\include{chapters/titlepage}
\include{chapters/abstract}
\include{chapters/acknowledgments}
\tableofcontents
\listoffigures
\listoftables
\mainmatter
\include{chapters/introduction}
\include{chapters/literature-review}
\include{chapters/methodology}
\include{chapters/results}
\include{chapters/discussion}
\include{chapters/conclusion}
\appendix
\include{chapters/appendix-a}
\backmatter
\bibliographystyle{apalike}
\bibliography{references}
\end{document}
\begin{figure}[htbp]
\centering
\includegraphics[width=0.8\columnwidth]{figures/architecture.pdf}
\caption{System architecture overview. The input module processes
raw data before passing to the transformer encoder.}
\label{fig:architecture}
\end{figure}
% Two subfigures side by side
\usepackage{subcaption}
\begin{figure}[htbp]
\centering
\begin{subfigure}[b]{0.48\columnwidth}
\includegraphics[width=\textwidth]{fig_a.pdf}
\caption{Training loss}
\label{fig:loss}
\end{subfigure}
\hfill
\begin{subfigure}[b]{0.48\columnwidth}
\includegraphics[width=\textwidth]{fig_b.pdf}
\caption{Validation accuracy}
\label{fig:acc}
\end{subfigure}
\caption{Training dynamics over 100 epochs.}
\label{fig:training}
\end{figure}
\begin{table}[htbp]
\centering
\caption{Comparison of methods on benchmark dataset.}
\label{tab:results}
\begin{tabular}{lccc}
\toprule
Method & Precision & Recall & F1 \\
\midrule
Baseline & 0.72 & 0.68 & 0.70 \\
Method A & 0.81 & 0.76 & 0.78 \\
\textbf{Ours} & \textbf{0.89} & \textbf{0.85} & \textbf{0.87} \\
\bottomrule
\end{tabular}
\end{table}
% Inline math
The loss function $\mathcal{L}(\theta) = -\sum_{i=1}^{N} y_i \log \hat{y}_i$
minimizes cross-entropy.
% Display equation (numbered)
\begin{equation}
\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V
\label{eq:attention}
\end{equation}
% Aligned equations
\begin{align}
\nabla_\theta J(\theta) &= \mathbb{E}_{\tau \sim \pi_\theta}
\left[\sum_{t=0}^{T} \nabla_\theta \log \pi_\theta(a_t|s_t) A_t\right]
\label{eq:policy_grad} \\
A_t &= Q(s_t, a_t) - V(s_t) \label{eq:advantage}
\end{align}
% Refer to equations
As shown in Equation~\eqref{eq:attention}, the attention mechanism...
\usepackage{algorithm}
\usepackage{algorithmic}
\begin{algorithm}[htbp]
\caption{Training procedure}
\label{alg:training}
\begin{algorithmic}[1]
\REQUIRE Dataset $\mathcal{D}$, learning rate $\eta$, epochs $E$
\ENSURE Trained model parameters $\theta^*$
\STATE Initialize $\theta$ randomly
\FOR{$e = 1$ to $E$}
\FOR{each batch $B \in \mathcal{D}$}
\STATE Compute loss $\mathcal{L}(B; \theta)$
\STATE $\theta \leftarrow \theta - \eta \nabla_\theta \mathcal{L}$
\ENDFOR
\ENDFOR
\RETURN $\theta$
\end{algorithmic}
\end{algorithm}
# Compilation sequence (4 steps)
pdflatex paper.tex # 1. First pass (generates .aux)
bibtex paper # 2. Process bibliography
pdflatex paper.tex # 3. Second pass (resolves refs)
pdflatex paper.tex # 4. Final pass (fixes page numbers)
# Or use latexmk for automatic compilation
latexmk -pdf paper.tex
@article{vaswani2017attention,
title={Attention is all you need},
author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and ...},
journal={Advances in neural information processing systems},
volume={30},
year={2017}
}
@inproceedings{devlin2019bert,
title={BERT: Pre-training of deep bidirectional transformers},
author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and ...},
booktitle={Proceedings of NAACL-HLT 2019},
pages={4171--4186},
year={2019}
}
# XeLaTeX (for Unicode/CJK support)
xelatex paper.tex && bibtex paper && xelatex paper.tex && xelatex paper.tex
# LuaLaTeX (alternative Unicode engine)
lualatex paper.tex
# Automated with latexmk
latexmk -xelatex paper.tex # XeLaTeX
latexmk -pdf paper.tex # pdfLaTeX
latexmk -c paper.tex # Clean auxiliary files
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.