skills/31-thalysandratos-claude-code-skills/_skills/communication/beamer-presentation/SKILL.md
Create academic presentations in Beamer with professional themes
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research beamer-presentationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
This skill helps economists create professional academic presentations using LaTeX Beamer. It provides templates for conference talks, job market presentations, and seminar presentations with proper structure and clean aesthetics.
Ask the user:
| Duration | Structure | |----------|-----------| | 15-20 min | Motivation (2) → Question (1) → Method (2) → Results (3-4) → Conclusion (1) | | 45-60 min | Add literature review, more results detail, robustness | | 90 min | Full seminar with theoretical framework, extensive empirics |
\pause or <+-> for complex slides\documentclass[aspectratio=169, 11pt]{beamer}
% ============================================
% THEME AND APPEARANCE
% ============================================
% Clean minimal theme
\usetheme{metropolis}
\usecolortheme{default}
% Or for a more traditional look:
% \usetheme{Madrid}
% \usecolortheme{whale}
% Custom colors
\definecolor{darkblue}{RGB}{0, 51, 102}
\definecolor{lightgray}{RGB}{245, 245, 245}
\setbeamercolor{frametitle}{bg=darkblue, fg=white}
\setbeamercolor{title}{fg=darkblue}
\setbeamercolor{structure}{fg=darkblue}
% Remove navigation symbols
\setbeamertemplate{navigation symbols}{}
% Frame numbers
\setbeamertemplate{footline}[frame number]
% ============================================
% PACKAGES
% ============================================
\usepackage{graphicx}
\usepackage{booktabs}
\usepackage{tikz}
\usepackage{pgfplots}
\pgfplotsset{compat=1.17}
% ============================================
% TITLE PAGE
% ============================================
\title{The Effect of X on Y: \\Evidence from Z}
\subtitle{Short and Descriptive}
\author{Your Name}
\institute{Your University}
\date{Conference Name \\ Month Year}
\begin{document}
% Title slide
\begin{frame}[plain]
\titlepage
\end{frame}
% ============================================
% MOTIVATION (2-3 slides)
% ============================================
\begin{frame}{Motivation: Why This Matters}
\begin{itemize}
\item<1-> \textbf{Big picture:} [One sentence on broad relevance]
\item<2-> \textbf{Specific puzzle:} [What we don't know]
\item<3-> \textbf{Stakes:} [Why should we care?]
\end{itemize}
\vspace{1em}
\only<4>{
\begin{block}{Key Statistic}
\Large \textbf{X\%} of [outcome] can be explained by [factor]
\end{block}
}
\end{frame}
\begin{frame}{What We Know (and Don't Know)}
\textbf{Previous literature:}
\begin{itemize}
\item Author et al. (2020): Finding 1
\item Other Author (2019): Finding 2
\end{itemize}
\vspace{1em}
\textbf{Gap we fill:}
\begin{itemize}
\item[\textcolor{red}{?}] [Open question our paper addresses]
\end{itemize}
\end{frame}
% ============================================
% RESEARCH QUESTION (1 slide)
% ============================================
\begin{frame}{This Paper}
\begin{center}
\Large
\textbf{Research Question:} \\[1em]
Does [X] cause [Y]? \\[2em]
\end{center}
\textbf{Preview of findings:}
\begin{itemize}
\item Main result in plain language
\item Key magnitude: [Quantitative summary]
\end{itemize}
\end{frame}
% ============================================
% EMPIRICAL STRATEGY (2-3 slides)
% ============================================
\begin{frame}{Data}
\textbf{Sources:}
\begin{itemize}
\item Dataset 1: [Description, years, N]
\item Dataset 2: [Description, matching method]
\end{itemize}
\vspace{1em}
\textbf{Sample:}
\begin{itemize}
\item Unit of observation: [What is an observation?]
\item Final sample: [N] observations, [Time period]
\end{itemize}
\end{frame}
\begin{frame}{Identification Strategy}
\textbf{Challenge:} [Endogeneity concern in one sentence]
\vspace{1em}
\textbf{Solution:} We exploit [natural experiment / instrument / RDD]
\vspace{1em}
\textbf{Key assumption:} [Identification assumption in plain language]
\begin{equation*}
Y_{it} = \alpha + \beta \cdot \text{Treatment}_{it} + \gamma X_{it} + \mu_i + \delta_t + \varepsilon_{it}
\end{equation*}
\end{frame}
% ============================================
% RESULTS (3-5 slides)
% ============================================
\begin{frame}{Main Result}
\begin{center}
\includegraphics[width=0.8\textwidth]{figures/main_result.pdf}
\end{center}
\vspace{0.5em}
\textbf{Takeaway:} [One sentence interpretation]
\end{frame}
\begin{frame}{Main Result: Regression Table}
\begin{table}
\centering
\small
\begin{tabular}{lccc}
\toprule
& (1) & (2) & (3) \\
& OLS & + Controls & + FE \\
\midrule
Treatment & 0.052*** & 0.048*** & 0.041** \\
& (0.012) & (0.011) & (0.015) \\
\midrule
Controls & No & Yes & Yes \\
Fixed Effects & No & No & Yes \\
N & 10,000 & 9,850 & 9,850 \\
\bottomrule
\end{tabular}
\end{table}
\textbf{Economic magnitude:} 1 SD increase in X $\rightarrow$ Y\% increase in outcome
\end{frame}
\begin{frame}{Robustness Checks}
\begin{itemize}
\item[\checkmark] Alternative specifications
\item[\checkmark] Placebo tests
\item[\checkmark] Different sample cuts
\item[\checkmark] [Other relevant checks]
\end{itemize}
\vspace{1em}
$\rightarrow$ Results robust across specifications
\end{frame}
% ============================================
% CONCLUSION (1 slide)
% ============================================
\begin{frame}{Takeaways}
\begin{enumerate}
\item \textbf{Finding 1:} [Main result]
\item \textbf{Finding 2:} [Secondary result]
\item \textbf{Implication:} [Policy/theory takeaway]
\end{enumerate}
\vspace{2em}
\begin{center}
\Large Thank you! \\[0.5em]
\normalsize [email protected]
\end{center}
\end{frame}
% ============================================
% APPENDIX
% ============================================
\appendix
\begin{frame}[noframenumbering]{Appendix: Additional Results}
[Backup slides for Q\&A]
\end{frame}
\end{document}
| Audience | Theme | Notes |
|----------|-------|-------|
| Academic | metropolis | Clean, modern, minimal |
| Conference | Madrid | Traditional, professional |
| Job market | default with custom colors | Safe, customizable |
| Policy | CambridgeUS | Authoritative look |
\pausedevelopment
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.