Pyfixest

Community

Barrios88

pyfixest

Fast high-dimensional fixed-effects regression in Python with pyfixest (fixest-like syntax). Use for OLS/IV/Poisson with many FEs, cluster-robust SEs, modern DiD (did2s, Sun-Abraham), and publication tables — the Python counterpart to Stata reghdfe/fixest for accounting and finance panels.

Overview

Publisher	Barrios88
Repository	`barrios-skills`
Skill name	`pyfixest`
Stars	64
Forks	30
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by Barrios88 on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/Barrios88/barrios-skills/tree/main/skills/econometrics/pyfixest
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.

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/Barrios88/barrios-skills.git /tmp/barrios-skills
mkdir -p .claude/skills
cp -r /tmp/barrios-skills/skills/econometrics/pyfixest .claude/skills/pyfixest

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pyfixest 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 Pyfixest 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 Pyfixest 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.

Barrios Skills — John Barrios's curated workflow for economists and accountants. Prioritize reproducible empirical work, clear identification language, and journal-ready output.

PyFixest for empirical accounting & finance

When to use

Firm–year / firm–quarter panels with firm and year (or industry×year) fixed effects
High-dimensional FE that are slow in linearmodels or plain statsmodels
IV with FE, Poisson/PPML with FE, or modern DiD helpers in Python
You already know Stata reghdfe / R fixest and want the same grammar in Python

Prefer python-panel-data when the design is a simple two-way FE / RE with linearmodels and you want textbook panel diagnostics. Prefer r-econometrics / Stata skills when the paper's replication package is R/Stata-first.

Install

bash
python -m pip install pyfixest
# optional: python -m pip install "pyfixest[plots]"

Docs: pyfixest.org

Iron law: specify clustering and FE before estimating

State the unit of treatment / residual correlation (usually firm, sometimes industry or CEO)
State fixed effects that absorb the design (firm + year; firm + industry×year; etc.)
Estimate with matching vcov / cluster
Inspect N, absorbed FE counts, and a coefficient sample before writing results into a paper

Quick patterns

OLS with multi-way FE + firm clustering

python
import pyfixest as pf

fit = pf.feols(
    "roa ~ treat + size + mb | gvkey + fyear",
    data=df,
    vcov={"CRV1": "gvkey"},
)
fit.summary()

Multiple specs (fixest-style)

python
fit = pf.feols(
    "roa ~ treat + size | gvkey + fyear",
    data=df,
    vcov={"CRV1": "gvkey"},
)
# Compare specs with pf.etable([...]) for paper tables

IV with FE

python
fit = pf.feols(
    "y ~ 1 | gvkey + fyear | endog ~ instrument",
    data=df,
    vcov={"CRV1": "gvkey"},
)

Staggered DiD / event study

Prefer package DiD helpers (did2s, Sun-Abraham / local projections as documented upstream) over naive TWFE when adoption is staggered. Report an event-study plot from a robust estimator, not only TWFE — see econ-writing-plus for narrative conventions.

Accounting / finance defaults

Design	Typical FE	Cluster
Firm–year treatment	`gvkey + fyear`	firm (`gvkey`)
Industry shock	`gvkey + fyear` or `gvkey + industry^fyear`	firm; sometimes industry
CEO / executive panel	exec + year or firm + year	firm or exec (justify)
State policy	firm + year	firm; often state×year FE debate in text

Journal-ready output

Export LaTeX via pyfixest / maketables helpers; pair with latex-tables for house style
Report within R² / FE set / cluster level in table notes
Never paste coefficients without SE and N

Checklist before claiming results

Sample filters documented (Compustat indfmt/datafmt/popsrc/consol if from WRDS)
FE and cluster match the identification story
Staggered timing → not only TWFE
One exhibit inspected (sign, magnitude, N) against a raw crosstab

Frequently asked questions

What does the Pyfixest AI skill do?

Why use Pyfixest on TypingMind?

Because you install it once and use it with any model. Pyfixest 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 Pyfixest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Barrios88/barrios-skills/tree/main/skills/econometrics/pyfixest. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pyfixest?

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 Pyfixest?

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

Is the Pyfixest AI skill free?

Yes. It is published on GitHub by Barrios88 under the MIT license. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

PyFixest for empirical accounting & finance

When to use

Install

Iron law: specify clustering and FE before estimating

Quick patterns

OLS with multi-way FE + firm clustering

Multiple specs (fixest-style)

IV with FE

Staggered DiD / event study

Accounting / finance defaults

Journal-ready output

Checklist before claiming results

Frequently asked questions

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