Claude Code is a command-line AI coding assistant built by Anthropic. It lets you interact with an AI directly in your terminal, making it especially powerful for researchers who spend time writing scripts, cleaning data, and running statistical analyses.
claude in any project directory to start a session.A simple example: loading and describing a dataset in Stata.
Claude Code will read your files, understand the context, write the Stata code, and explain the results — all in one conversation.
These Claude Code skills are .md files you place in your .claude/skills/ directory. Once installed, activate any skill by typing /skill-name in a Claude Code session.
Automate regression analysis with proper diagnostics, clustered standard errors, and publication-ready tables.
.claude/skills/. In Claude Code, type /stata-regression and describe your regression setup. The skill will generate Stata code with proper clustering, fixed effects, and esttab output.
Causal inference workflows including IV, DiD, and RDD using the fixest package for high-dimensional fixed effects.
.claude/skills/. Type /r-econometrics and specify your identification strategy. The skill generates R code using fixest, lfe, or rdrobust as appropriate.
Automated data cleaning pipeline for survey and administrative data — handles missing values, outliers, and merges.
/data-cleaning. Point it at your raw data files and describe the target analysis. It will produce a documented cleaning do-file or R script with a full data audit.
Organize research papers, extract key findings, and create synthesis tables for your literature review.
/lit-review then paste or link a list of papers. The agent reads abstracts, extracts key findings, and outputs a structured synthesis table in Markdown or LaTeX.
Generate and refine research questions from a phenomenon, dataset, or policy change.
/research-ideation and describe a policy, dataset, or empirical puzzle. The skill generates potential research questions, identification strategies, and relevant literature pointers.
Draft and structure economics papers with proper academic style for introduction, results, or conclusion sections.
/academic-writer. Provide your results, identification strategy, and target journal. The skill drafts the section in AER/JPE style with proper economic writing conventions.
Automated checking and validation of replication packages — ensures code runs and produces expected results.
/replication-lint on your project directory. The agent checks file structure, verifies all referenced datasets exist, tests script execution order, and generates an AEA-compliant README.
Step-by-step guide to importing raw ENAHO/ENNIV data, handling complex survey weights, recoding variables, and producing a clean analysis dataset ready for regression.
Setting up panel data, testing parallel trends, specifying event-study regressions, and creating publication-ready coefficient plots with confidence intervals.
ggplot2 and Stata graph styling for academic journals — color schemes, font sizes, aspect ratios, and exporting at correct DPI.
Organizing scripts from raw data to final tables, documenting intermediate outputs, and setting up a master do-file that runs the entire analysis end-to-end.
Setting up Anthropic API access, writing prompts for open-ended response classification, validating outputs, and optimizing cost with prompt caching.
describe so Claude understands the structure.set trace on (Stata) or options(error = traceback) (R) and share the trace.