Awesome Econ AI Stuff skill
17 skills for economists: research ideas, literature review, Stata and R, LaTeX models, tables and Beamer slides
- 662 ★ on the repository
- Last commit
Extended access
Why this level
- The commit-push-pr skill commits, pushes and opens a pull request
- The api-data-fetcher skill calls external economic APIs
Install
- Works in
In your terminal, with SkillFoxx CLI
npx skillfoxx add skills/awesome-econ-ai-stuffDetects the agents on your machine, shows the access level and pins the version.
Other ways to install
Access and checks
How we reviewExtended accessThe tool runs code or makes network calls and only reads external data.
- The commit-push-pr skill commits, pushes and opens a pull request
- The api-data-fetcher skill calls external economic APIs
This is third-party code. Review the repository files before installing.
What it does
Awesome Econ AI Stuff keeps 17 SKILL.md instructions in the _skills folder, arranged by research stage. Research-ideation drafts questions from an economic phenomenon, and lit-review-assistant searches and summarizes papers. For theory, latex-econ-model writes models in LaTeX and general-equilibrium-model-builder builds and solves Walrasian general equilibrium models in Julia. stata-data-cleaning (cleaning in Stata) and api-data-fetcher (series from FRED and the World Bank) handle data. Analysis runs through r-econometrics (IV, DiD and RDD with diagnostics), python-panel-data and stata-regression. Before computing, r-econometrics asks for the identification strategy, the unit of observation and the fixed effects. For writing and presenting there are academic-paper-writer, latex-tables, beamer-presentation and econ-visualization. Four engineering skills (sdd, techdebt, commit-push-pr, code-simplifier) cover everyday code work.
Who it is for. PhD students and economics researchers who compute in Stata, R or Python and typeset papers in LaTeX.
Good fit when
- A program effect is estimated by difference-in-differences on panel data and the code needs clustered standard errors
- A Stata export needs cleaning: missing values, duplicates and recoded variables
- A general equilibrium model is written in LaTeX and solved numerically in Julia
- Results go into journal-ready tables and Beamer slides for a seminar
Not a fit when
- You want unattended computation and methodological control: the skills give code drafts and the researcher checks the conclusions
- You work in Codex and expect native SKILL.md loading: the README points Codex users to AGENTS.md
- You need Python econometrics beyond panel data or methods outside IV, DiD and RDD: coverage is limited to the README list
Example request
/r-econometrics Estimate the program effect with difference-in-differences on my panel data and cluster standard errors by regionLimitations
The skills give drafts of code and text, so check results from Stata, R and Julia against your own data and methodology. For Codex the README suggests the AGENTS.md format, and support for GitHub Copilot and Aider is called partial. GitHub did not recognise the repository license automatically; the text is in the LICENSE file. The panel-balance entry in the catalog lives in a separate repository, and the project site on GitHub Pages shows the same list of skills.
How to disable. Delete the skill folder from the agent's skills directory, for example `~/.claude/skills/r-econometrics`, and the skill stops loading.
FAQ
How do I install the econometrics skill in Claude Code?
Create `~/.claude/skills` and copy the folder of the skill you need into it, for example `_skills/analysis/r-econometrics`. Call it with `/r-econometrics` or a plain request.
Which methods does r-econometrics cover?
Instrumental variables, difference-in-differences and regression discontinuity in R, with diagnostics and robust standard errors. The skill generates publication-ready code, including two-way fixed effects and event studies.
Where does api-data-fetcher get its data?
From economic APIs: the README names FRED and the World Bank. The skill builds the requests and the series download, and access to the APIs runs directly from your environment.
Can I add my own skill?
Yes, through the web form on the project site or an issue from the skill-proposal template. The skill structure is described in _skills/SKILL_TEMPLATE.md and CONTRIBUTING.md.
How is a skill file structured?
It is a SKILL.md with name, description, workflow_stage and compatibility fields and Purpose, When to Use and Instructions sections. A template sits in `_skills/SKILL_TEMPLATE.md`.
Discussion
No questions or reviews yet.
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README in short
The README explains why economists need skills and maps each research stage to a skill: idea, literature, theory, data, analysis, writing, presentation and engineering. Installation by copying a folder, a compatibility table for six tools and the route for submitting a new skill follow.
README badge
Original SKILL.md
--- name: r-econometrics description: Run IV, DiD, and RDD analyses in R with proper diagnostics workflow_stage: analysis compatibility: - claude-code - cursor - codex - gemini-cli author: Awesome Econ AI Community version: 1.0.0 tags: - R - econometrics - causal-inference - fixest - regression --- # R Econometrics ## Purpose This skill helps economists run rigorous econometric analyses in R, including Instrumental Variables (IV), Difference-in-Differences (DiD), and Regression Discontinuity Design (RDD). It generates publication-ready code with proper diagnostics and robust standard errors. ## When to Use - Running causal inference analyses - Estimating treatment effects with panel data - Creating publication-ready regression tables - Implementing modern econometric methods (two-way fixed effects, event studies) ## Instructions ### Step 1: Understand the Research Design Before generating code, ask the user: 1. What is your identification strategy? (IV, DiD, RDD, or simple regression) 2. What is the unit of observation? (individual, firm, country-year, etc.) 3. What fixed effects do you need? (entity, time, two-way) 4. How should standard errors be clustered? ### Step 2: Generate Analysis Code Based on the research design, generate R code that: