Awesome-Agent-Skills-for-Empirical-Research claude-academic-workflow-guide
Claude Code template for LaTeX, Beamer, and R research workflows
install
source · Clone the upstream repo
git clone https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/43-wentorai-research-plugins/skills/research/automation/claude-academic-workflow-guide" ~/.claude/skills/brycewang-stanford-awesome-agent-skills-for-empirical-research-claude-academic-w && rm -rf "$T"
manifest:
skills/43-wentorai-research-plugins/skills/research/automation/claude-academic-workflow-guide/SKILL.mdsource content
Claude Code Academic Workflow Guide
Overview
A template and workflow guide for using Claude Code in academic research — managing LaTeX papers, Beamer presentations, R analysis scripts, and multi-agent peer review. Provides structured CLAUDE.md configurations, project templates, and automation patterns for common academic tasks. Designed for economists, social scientists, and quantitative researchers.
Project Structure
research-project/ ├── CLAUDE.md # Claude Code instructions ├── paper/ │ ├── main.tex # Main LaTeX document │ ├── references.bib # Bibliography │ ├── sections/ # LaTeX sections │ └── figures/ # Generated figures ├── slides/ │ ├── presentation.tex # Beamer slides │ └── figures/ ├── code/ │ ├── analysis.R # Main analysis │ ├── data_clean.R # Data preparation │ └── figures.R # Figure generation ├── data/ │ ├── raw/ # Original data │ └── processed/ # Cleaned data └── output/ ├── tables/ # LaTeX tables └── figures/ # PDF/PNG figures
CLAUDE.md Configuration
# Project: [Your Paper Title] ## Instructions - This is an academic research project in economics - LaTeX compiler: pdflatex (paper) or xelatex (if CJK) - R version: 4.3+ with tidyverse, fixest, ggplot2 - Citation style: natbib, authoryear - Always compile paper after LaTeX changes - Run R scripts from project root ## Paper Conventions - Use \input{sections/intro} for section includes - Tables: booktabs package, generated from R - Figures: PDF format, width=\textwidth - Cross-refs: \label{sec:}, \label{tab:}, \label{fig:} ## R Conventions - Style: tidyverse style guide - Data: read from data/processed/ - Output: tables/ (LaTeX), figures/ (PDF) - Reproducibility: set.seed(42) for all random ops ## Build Commands - Paper: `cd paper && pdflatex main && bibtex main && pdflatex main && pdflatex main` - Slides: `cd slides && pdflatex presentation` - Analysis: `cd code && Rscript analysis.R`
LaTeX Paper Workflow
# Claude Code can manage the full LaTeX workflow: # 1. Draft a section # "Write the methodology section for our diff-in-diff analysis" # 2. Generate tables from R output # "Create a LaTeX table from the regression results in output/tables/" # 3. Fix compilation errors # "The paper won't compile — fix the LaTeX errors" # 4. Update bibliography # "Add the Callaway & Sant'Anna (2021) reference" # 5. Format for submission # "Format the paper for AER submission guidelines"
Beamer Presentations
% Template for academic presentations \documentclass[aspectratio=169]{beamer} \usetheme{metropolis} \title{Your Presentation Title} \subtitle{Conference/Seminar Name} \author{Author Name} \institute{University} \date{\today} \begin{document} \maketitle \begin{frame}{Motivation} \begin{itemize} \item Research question \item Why it matters \item What we do \end{itemize} \end{frame} \begin{frame}{Data} \input{figures/summary_stats_table} \end{frame} \begin{frame}{Results} \centering \includegraphics[width=0.8\textwidth]{figures/main_result.pdf} \end{frame} \end{document}
R Analysis Integration
# analysis.R — Main analysis script library(tidyverse) library(fixest) library(modelsummary) # Load cleaned data df <- read_csv("data/processed/analysis_data.csv") # Main regression model1 <- feols(outcome ~ treatment | year + state, data = df) model2 <- feols(outcome ~ treatment + controls | year + state, data = df, cluster = ~state) # Export table for LaTeX modelsummary( list("(1)" = model1, "(2)" = model2), output = "output/tables/main_results.tex", stars = c("*" = 0.1, "**" = 0.05, "***" = 0.01), gof_map = c("nobs", "r.squared", "FE: year", "FE: state"), ) # Export figure ggplot(df, aes(x = year, y = outcome, color = treated)) + geom_point(alpha = 0.3) + geom_smooth(method = "loess") + theme_minimal() + labs(x = "Year", y = "Outcome", color = "Treatment Group") ggsave("output/figures/treatment_trends.pdf", width = 8, height = 5)
Multi-Agent Review
### Self-Review Workflow Use Claude Code to simulate peer review: 1. "Review this paper as a critical referee for AER" 2. "Check all mathematical derivations in section 3" 3. "Verify that all tables match the R code output" 4. "Check for consistency between text claims and results" 5. "List potential referee objections and how to address them"
Common Tasks
### Things to ask Claude Code: - "Compile the paper and fix any errors" - "Add robustness check using propensity score matching" - "Create a Beamer slide summarizing Table 2" - "Generate event study plot from the regression results" - "Convert this Word draft to LaTeX format" - "Check all cross-references are correct" - "Format references in AEA style"
Use Cases
- Paper writing: LaTeX drafting and compilation workflow
- Data analysis: R script development and debugging
- Presentations: Beamer slide creation from paper content
- Self-review: Multi-agent review simulation
- Submission prep: Format conversion for journal submission