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Stata Regression

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brycewang-stanford
stata-regression

Run regression analyses in Stata with publication-ready output tables.

Overview

Publisherbrycewang-stanford
RepositoryAuto-Empirical-Research-Skills
Skill namestata-regression
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3.8K
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Bundled files
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  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by brycewang-stanford on GitHub. Read the source before you install it.

Installation

Install the Stata Regression AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git /tmp/Auto-Empirical-Research-Skills
mkdir -p .claude/skills
cp -r /tmp/Auto-Empirical-Research-Skills/skills/09-meleantonio-awesome-econ-ai-stuff/_skills/analysis/stata-regression .claude/skills/stata-regression
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Stata Regression in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Stata Regression on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Stata Regression is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Stata Regression

Purpose

This skill produces reproducible regression analysis workflows in Stata, including model diagnostics and publication-ready tables using esttab or outreg2.

When to Use

  • Estimating linear or nonlinear regression models in Stata
  • Producing tables for academic papers and reports
  • Running robustness checks and alternative specifications

Instructions

Follow these steps to complete the task:

Step 1: Understand the Context

Before generating any code, ask the user:

  • What is the dependent variable and key regressors?
  • What controls and fixed effects are required?
  • How should standard errors be clustered?
  • What output format is needed (LaTeX, Word, or CSV)?

Step 2: Generate the Output

Based on the context, generate Stata code that:

  1. Loads and checks the data - Handle missing values and verify variable types
  2. Runs the requested specification - Use regress, reghdfe, or xtreg as appropriate
  3. Adds robust or clustered standard errors - Match the study design
  4. Exports tables - Use esttab or outreg2 with clear labels

Step 3: Verify and Explain

After generating output:

  • Explain what each model estimates
  • Highlight assumptions and diagnostics
  • Suggest robustness checks or alternative models

Example Prompts

  • "Run OLS with firm and year fixed effects, clustering by firm"
  • "Estimate a logit model and export results to LaTeX"
  • "Create a regression table with three specifications"

Example Output

stata
* ============================================
* Regression Analysis with Stata
* ============================================

* Load data
use "data.dta", clear

* Summary stats
summarize y x1 x2 x3

* Main regression with clustered SEs
regress y x1 x2 x3, vce(cluster firm_id)
eststo model1

* Alternative specification with fixed effects
reghdfe y x1 x2 x3, absorb(firm_id year) vce(cluster firm_id)
eststo model2

* Export table
esttab model1 model2 using "results/regression_table.tex", replace se label

Requirements

Software

  • Stata 17+

Packages

  • estout (for esttab)
  • reghdfe (optional, for high-dimensional fixed effects)

Install with:

stata
ssc install estout
ssc install reghdfe

Best Practices

  1. Match standard errors to the design (cluster where treatment varies)
  2. Report all model variants used in the analysis
  3. Document variable definitions and transformations

Common Pitfalls

  • Not clustering standard errors at the correct level
  • Omitting fixed effects when required by the design
  • Exporting tables without clear labels and notes

References

Changelog

v1.0.0

  • Initial release

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Stata Regression AI skill do?

Run regression analyses in Stata with publication-ready output tables.

Why use Stata Regression on TypingMind?

Because you install it once and use it with any model. Stata Regression is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Stata Regression in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/09-meleantonio-awesome-econ-ai-stuff/_skills/analysis/stata-regression. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Stata Regression?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Stata Regression?

As many as you like. As long as a model supports skills, you can use Stata Regression with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Stata Regression AI skill free?

It is published on GitHub by brycewang-stanford. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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