skills/43-wentorai-research-plugins/skills/writing/templates/beamer-presentation-guide/SKILL.md
Guide to creating academic presentations with LaTeX Beamer
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research 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}
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.