skills/writing/templates/beamer-presentation-guide/SKILL.md
Guide to creating academic presentations with LaTeX Beamer
npx skillsauth add wentorai/research-plugins beamer-presentation-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Create professional academic presentations using LaTeX Beamer with themes, animations, and best practices for conference talks and research seminars.
\documentclass[aspectratio=169]{beamer} % 16:9 aspect ratio
% Other options: aspectratio=43 (4:3, default), aspectratio=1610
\usetheme{Madrid} % Visual theme
\usecolortheme{default} % Color scheme
\usefonttheme{professionalfonts}
\usepackage{amsmath,amssymb}
\usepackage{graphicx}
\usepackage{booktabs} % Better tables
\usepackage{hyperref}
\title[Short Title]{Full Title of Your Presentation}
\subtitle{Conference Name 2025}
\author[A. Smith]{Alice Smith\inst{1} \and Bob Jones\inst{2}}
\institute[MIT, Stanford]{
\inst{1}MIT \and \inst{2}Stanford University
}
\date{March 15, 2025}
\begin{document}
\begin{frame}
\titlepage
\end{frame}
\begin{frame}{Outline}
\tableofcontents
\end{frame}
\section{Introduction}
\begin{frame}{Motivation}
\begin{itemize}
\item Research question and why it matters
\item Key challenge in the field
\item Our contribution in one sentence
\end{itemize}
\end{frame}
\end{document}
| Theme | Style | Best For |
|-------|-------|----------|
| Madrid | Professional, structured headers | Conference talks |
| Metropolis (mtheme) | Modern, minimal, flat design | CS/tech conferences |
| CambridgeUS | Traditional academic | University seminars |
| Singapore | Clean navigation sidebar | Long presentations |
| Bergen | Compact, information-dense | Technical deep dives |
| default | Plain, no decoration | Maximum content area |
% Metropolis is a modern, clean theme widely used in CS/ML talks
\documentclass[aspectratio=169]{beamer}
\usetheme{metropolis}
% Customize colors
\definecolor{customPrimary}{RGB}{0, 83, 159} % University blue
\setbeamercolor{frametitle}{bg=customPrimary}
\setbeamercolor{progress bar}{fg=customPrimary}
% Optional: use Fira Sans font (matches Metropolis design)
% \usepackage[sfdefault]{FiraSans}
\begin{frame}{Main Result}
\begin{theorem}[Our Main Theorem]
For any $\epsilon > 0$, Algorithm~\ref{alg:ours} achieves an
approximation ratio of $(1 - \epsilon)$ in time $O(n \log n / \epsilon)$.
\end{theorem}
\vspace{0.5em}
Key implications:
\begin{enumerate}
\item First polynomial-time approximation scheme for this problem
\item Improves over Smith et al. (2023) by a factor of $O(\log n)$
\item Extends to weighted variants
\end{enumerate}
\end{frame}
\begin{frame}{Method Overview}
\begin{columns}[T]
\begin{column}{0.48\textwidth}
\textbf{Architecture}
\begin{itemize}
\item Encoder: 6-layer Transformer
\item Decoder: 6-layer Transformer
\item Hidden dim: 512
\item Attention heads: 8
\end{itemize}
\end{column}
\begin{column}{0.48\textwidth}
\textbf{Training}
\begin{itemize}
\item Optimizer: AdamW
\item Learning rate: $3 \times 10^{-4}$
\item Batch size: 256
\item Epochs: 100
\end{itemize}
\end{column}
\end{columns}
\end{frame}
\begin{frame}{Experimental Results}
\begin{figure}
\centering
\includegraphics[width=0.85\textwidth]{figures/results-comparison.pdf}
\caption{Our method (blue) outperforms baselines across all benchmarks.}
\end{figure}
\end{frame}
\begin{frame}{Comparison with State of the Art}
\centering
\small
\begin{tabular}{lcccc}
\toprule
Method & Accuracy & F1 & Params & Speed \\
\midrule
Baseline A & 85.2 & 83.1 & 110M & 1.0x \\
Baseline B & 87.5 & 85.8 & 340M & 0.3x \\
\textbf{Ours} & \textbf{89.1} & \textbf{87.4} & 125M & 0.9x \\
\bottomrule
\end{tabular}
\end{frame}
\begin{frame}{Key Contributions}
\begin{itemize}
\item<1-> First contribution: novel problem formulation
\item<2-> Second contribution: efficient algorithm
\item<3-> Third contribution: theoretical guarantees
\item<4-> Fourth contribution: extensive experiments
\end{itemize}
\only<4>{
\vspace{1em}
\alert{All code and data are publicly available.}
}
\end{frame}
\begin{frame}{Pipeline}
Step 1: Data collection
\begin{itemize}
\item \alert<2>{Crawl 10M web pages}
\item \alert<3>{Filter and deduplicate}
\item \alert<4>{Annotate with human labels}
\end{itemize}
\uncover<5->{
\begin{block}{Result}
Final dataset: 2.3M high-quality labeled examples.
\end{block}
}
\end{frame}
\usepackage{listings}
\lstset{
basicstyle=\ttfamily\scriptsize,
keywordstyle=\color{blue}\bfseries,
commentstyle=\color{gray},
stringstyle=\color{red},
breaklines=true,
frame=single,
backgroundcolor=\color{gray!10}
}
\begin{frame}[fragile]{Implementation} % [fragile] required for listings
\begin{lstlisting}[language=Python]
import torch
import torch.nn as nn
class TransformerBlock(nn.Module):
def __init__(self, d_model, n_heads):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_heads)
self.norm = nn.LayerNorm(d_model)
def forward(self, x):
return self.norm(x + self.attn(x, x, x)[0])
\end{lstlisting}
\end{frame}
| Talk Length | Slides | Content | |------------|--------|---------| | 5 min (lightning) | 5-7 | Problem, method, key result | | 15 min (conference) | 12-18 | + motivation, related work brief, 2-3 results | | 30 min (seminar) | 20-30 | + background, methods detail, analysis | | 60 min (colloquium) | 35-50 | + extensive background, all results, future work |
\appendix for Q&A% Backup slides (not counted in slide numbers)
\appendix
\begin{frame}{Proof of Theorem 1}
...
\end{frame}
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