It is increasingly clear that the infrastructure around the model is a force multiplier for what the model can achieve. I am still skeptical that I can always tell when one top model is better than a competitor. But I am curious and ready to be proven wrong. Partly as an educational exercise, and partly to expand Codex's capacity in my workflow to do more autonomous work, I worked with ChatGPT and Codex to translate Pedro Sant'Anna's claude-code-my-workflow repo so that as much of the same functionality as possible could be emulated in Codex. I was particularly interested in the specialist agent capabilities (R reviewer, proofreader, writer, etc.) and the adversarial agent that critiques presentational work and code so the model iterates on itself to make the final product and pipeline stronger. So far, Codex seems great for building out codebases in fairly frequent contact with the human researcher, which I mostly prefer as a workflow because it is easier to keep track of the project. But it was not immediately clear how to get it to fully run away and write a paper on its own.

So this port was an attempt to learn more about how Codex and Claude work while also expanding what I can do with the models. Specifically: how much of the Claude workflow from Sant'Anna's repo can be preserved when the assistant changes from Claude to Codex?

You can find the repo for this project here: codex-my-workflow

As Codex explains it to me:

The simplest way to think about a workflow repo is as a toolbox plus a rulebook for working with an AI assistant.

- instructions about how to behave

- templates for plans and reports

- reusable specialist roles

- project memory that survives past one conversation

- verification steps so "done" means something real

That structure turns an AI assistant from "a smart autocomplete window" into

something closer to a junior contractor with a clipboard, a checklist, and a filing cabinet.

The Claude Code setup was appealing because it treated reliability as a workflow problem. It had several appealing features:

• Plan first. This is pretty standard operating procedure with Codex or Claude ("enter plan mode" or "make a plan").
• Keep durable notes on disk so context is not trapped in one chat window.
• Use specialists when one general reviewer is not enough.
• Verify outputs instead of trusting fluent prose.
• Gate completion with explicit quality thresholds.
• Use an adversarial critic/fixer loop instead of a single pass.

So I forked the repo, then asked ChatGPT 5.4 Pro in the browser how I could work with Codex to port it into a Codex-focused version with as much of the same functionality as possible. ChatGPT helped draft initial AGENTS.md and plan documents to guide the Codex rewrite, and it also helped draft the prompt that got Codex started on the translation. Codex then evaluated the Claude repo and decided which documents could generally be preserved, what had to be adapted, and what could not be recreated one-for-one.

The first lesson is that, at a high level, Claude and Codex are similar enough that a port is genuinely possible. They can both read repository content, edit files, follow detailed instructions, run shell commands, work iteratively, and use role-like specializations. They also both benefit greatly from foundational project guidance rather than one-off prompts. But the interesting part of porting this workflow was where they differ.

As Codex put it:

Claude and Codex are similar in the way two kitchens are similar: both have heat, knives, and counters, but the appliances are in different places and some of them work differently. A recipe can survive that move, but not by copying the layout blindly.

There are some important differences. The syntax of guiding documents varies, so the repo structure through which Claude and Codex receive guidance differs. Codex translated Claude project guidance into root and nested AGENTS.md files. Claude settings became Codex config, hooks, and rules. Claude agent definitions became .codex/agents/*.toml. Claude skills became repo-local skills under .agents/skills/.

Another difference is that Codex makes explicitness more important. Codex is less likely to automatically spawn agents such as the adversarial reviewer or specialist agents. This functionality can still be achieved, but prompts need to ask for it explicitly to better ensure those agents get deployed. That changes how the workflow has to be written.

One takeaway is that in the original Claude-centered workflow, Claude can feel more anticipatory of likely researcher desires, and therefore more autonomous. In Codex, the safer pattern is to be more explicit. Prompts generally should name the specialists to be spun up, tell Codex what to do with them, and state verification expectations directly. Codex thus feels a little less magical and in need of a bit more handholding from the researcher and the workflow structure around it. But this is one of the most useful lessons from the port. When platforms differ, a good fallback is often not "invent more automation." It is "store the important files so the model can read them and stay on target."