u/ImpossibleCollege635

How can I handoff from one agent to another?

I often end up hitting my limit in say claude code. Id love to just continue the conversation in cursor/ codex. Are there any tools that enable me to do that? Context0 seems to be in that direction but not quite that?

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▲ 1 r/stata+1 crossposts

Long time reader, first time poster here.

Are you intrigued by or already using coding agents?
Me too. But agents hallucinate, confidently make decisions we don't approve of, and only sometimes disclose their assumptions. Also their style is often odd 😂

The software dev/ datascience community has approached this problem for a while now using all sorts of tools and guardrails for agents. The hottest one on the block right now: Agent skills

Over the years of doing econometrics I developed my own set of favoured approaches, tools, assumptions, etc. (as I am sure you all have too!)
I packaged mine into a set of rules and skills for my coding agents & I am a bit shocked HOW MUCH BETTER they get at doing things the way I want.

I built these to streamline my own econometrics research. The defaults that ship with general-purpose AI tools are uneven: they happily generate plain TWFE on staggered treatment, report F > 10 as a sufficient first-stage test, paste regression numbers into LaTeX by hand, and mix red-green palettes for treatment vs. control. The skills here force the agent into the patterns I want (and the patterns I think most applied economists should want).

They are deliberately highly opinionated. The opinions come from:

  • DIME Analytics' DIME Wikiiefolderiecodebookieduplicates, master do-files, the four-tier replicability standard, the Reviewing Graphs and Submit Table checklists, and the general "single source of truth + master orchestrator" mindset (translated to Python, Julia, and LaTeX where DIME's guidance is Stata-only).
  • Modern econometrics literature: Goodman-Bacon (2021), Callaway-Sant'Anna (2021), Sun-Abraham (2021), Borusyak-Jaravel-Spiess (2024), de Chaisemartin-D'Haultfoeuille; Olea-Pflueger (2013) and Lee et al. (2022) for weak IV; Calonico-Cattaneo-Titiunik (2014) for RDD; Cameron-Gelbach-Miller (2008) and MacKinnon-Webb (2018) for wild cluster bootstrap; Roth, Sant'Anna, Bilinski & Poe (2023) for the modern DiD landscape.
  • Modern packages:  fixest / pyfixestdiddidimputationeventstudyinteractcsdiddid_imputationboottest/fwildclusterbootrdrobustlinearmodelsmodelsummary/stargazer/esttab.

The repo was inspired by meleantonio/awesome-econ-ai-stuff — the original curated catalog. This is a narrower, more opinionated rewrite focused on four workflow stages.

Have a look, use, critique, contribute if you fancy: https://github.com/JonasWeinert/EconAgentSkills

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u/ImpossibleCollege635 — 7 days ago