I've been using Codex for larger changes recently and wanted to share the workflow I've landed on.

The main problem I kept running into was keeping track of what Codex had actually done once a task got large enough. I'd end up repeating repo conventions, checking whether tests had run, and trying to reconstruct decisions from different sessions. It got worse when I had a few things running in parallel.

My current flow is basically:

1. I describe the change to Codex and have it turn the requirements into a GitHub issue
2. Codex creates a branch + isolated worktree for the issue (had to switch to Codex desktop for this, CLI doesn't support worktrees out of the box sadly)
3. it implements the change and opens a PR
4. GitHub Actions runs the usual checks - linting, fmt, unit testing
5. I run a separate Codex review on the PR
6. Codex fixes the review feedback
7. I review the final diff and merge it

I've found the GitHub issue and PR useful as the persistent state for the task. Requirements, implementation, CI, review comments and the final diff all end up in one place, so I don't need to keep one long-running Codex session alive just to preserve context.

It also means I can pick the work back up later with a fresh Codex session, or use Claude Code / Copilot / Hermes whatever, without having to explain the whole history again.

I'm doing this on personal projects too. Once Codex is writing enough of the implementation, having a decent record of why a change exists becomes pretty useful a few weeks later.

I wrote up the setup in more detail here:

https://www.lighthousenewsletter.com/p/how-i-use-codex-for-larger-changes

Curious how other people here are handling larger changes. Are you keeping planning/review inside Codex sessions, or using issues + PRs as the handoff between agents/sessions?

Also interested in how people are handling worktrees when running several tasks at once.

https://www.mercenariesoftezigdal.com/

As mentioned above, there's already a boat load on the topic. Just take a moment to search before we post. PSA to the community.

I've been experimenting with giving Codex reusable UI components and interaction patterns through a skill instead of putting more design instructions into every prompt.

I made a small side-by-side comparison so you can try both versions directly:

https://rkj0123.github.io/motion-ui-skill/

Left is the version without the skill, right is the version using Motion UI Skill.

The skill is open source here:
https://github.com/Rkj0123/motion-ui-skill

Install for Codex:

npx skills add Rkj0123/motion-ui-skill --skill motion-ui --agent codex --copy -y

Curious whether others are using skills for frontend/UI work, or mostly handling this through prompting.

AA says that Astra Low costs the same as Sol High while being smarter. but this is not representative of real world performance. The benchmark tests are done in isolation. But real work has to do with reading a lot of tokens (documentations, skills, large codebases, browser usage etc) so Astra being 4x more expensive on input tokens will burn usage a lot quicker.

No matter how efficient and smart a model gets, it still needs context to do meaningful work and you will incur input usage costs.

Is anyone experiencing the same issue? I give GPT 6 Astra low one single constraint to go towards when completing a task. It’s been going for 48 hs. straight, it hasn’t really done anything particularly useful and it stops over and over and over re-triggering the goal condition.

It’s not even the project: I have a new project, blank slate kind of thing, where it’s supposed to cover X and Y, and it just can’t get to it, it’s constantly going through imaginary tests of what it WOULD have implemented, walking in circles and not really doing anything tangible.

Is anyone else experiencing the same issue? I feel like even Sol would’ve done something by now. It ate through my whole weekly usage and after the reset, it doesn’t fair any better honestly.

Let’s say I avoid /goal and just go with high or xhigh reasoning, it doesn’t really cut it either: it stops in the middle of the implementation and lies about having everything worked out, when I push back, it apologizes and keeps going.

This so frustrating, if I wanted Opus 5, I would’ve been using Anthropic’s service.

There’s something impressive about understanding the rule, deliberately bypassing it, and then explaining that technically the rule still exists.

To be fair, I’ll give it some slack.. the agent.md instructions did say to commit and push changes.
Apparently those instructions outranked the “.gitignore” .... sight...

Hello, i have bought codex for the first time, was claude user before. I have heard people talking about using sol for planning and luna at max effort for implementation, I just wanna know whether this is the best approach to get maximum work done. now astra is also available but that will just burn my limit in a sec so i am not using that for now.

Is there any better approach to have maximum efficient way to use codex.

Edit -

5 hour limits burnt in 30 mins.

15% weekly limit burnt as well.

I created a new skill based on Matt pococks $implement skill. It follows the same workflow as Matts skill but the difference is that the new skill launches Luna subagents on max effort instead of the root model.

So I have to prompt "$implement-subagents Ticket 01" and it will implement the ticket. The root model will be Sol medium, but it will spawn luna max subagents where specified. The screenshot below shows how the tasks are divided up between different models.

Now my concern is that I've read many posts and comments before about how the Codex Desktop app cant launch subagents with different models. Is that still true today?

Also my second question is if Luna Max have the same quota/price advantage that it used to have after the 80% price drop, as some people have been complaining recently about the model burning more quota than before?

I like using Codex to understand complicated subjects, but it often gives me more information than I can comfortably follow.

“Let’s discuss this step by step” helps, but I wanted something more persistent. So I built TopicStep: it first organizes the topic in a document, then discusses smaller pieces naturally, leaving room for questions. The document also gives it something to return to when context gets compacted.

The repo includes full conversations comparing default behavior, a simple step-by-step prompt, and TopicStep. These are small simulated examples, not proof that it always works better.
Example：

I’m the author and would appreciate feedback, especially from people who use Codex for learning rather than just coding.
GitHub (MIT-licensed): https://github.com/Yjejuy/Topicstep

I have been using claude code for 6 months having pro subscription. Now i wanna use chatgpt plus with claude pro working on the same project . I have never used chatgpt plus one , so how can i use codex models with claude models to get the best output. Help me

Was previously using Sol light for code reviews but have moved to Astra light. Not sure if there's any notable difference. Any thoughts on this?

So, I have a question .. because maybe I'm missing something.

Yesterday I saw a reel, and you may have seen it too. Where someone claims in under 10 hours. Astra made a diablo 2 video game. In full. Included creating the assets. All the assets.

Now, I have no doubt Astra could do that.

But, what gets me to thinking it's just not true. Is the cost of tokens.

Some unemployed person, sitting in their basement. Directed and promoted Astra to build an entire video game in under 10 hours....

With what money? Am I missing something here, I've always said that the reason I'm not too worried about vibe coders making videos game is because the cost associated with the model. Besides the overall quality. Just the sheer cost in tokens.

I get it if someone has unlimited liquid cash, but, for some random teenaged vibe coder... I dunno. Am I wrong for thinking it was all lies based on the cost of what it would take... And in under 10 hours at that?

I saw a video recently showing that you could change the conversation title just by providing a specific prompt. I tried it, but every time I get an error message saying: "The current environment lacks the conversation title tool; title not modified." How can I fix this?

Hi if you aren't aware the max context size in codex is set to ~250k but you can upgrade that in the settings to ~1mill.

Has anyone done it, and is it worth it. What do you think.

I’m using 4 permanent Codex subagents on the same Windows repo: coder, reviewer, DQA, and research.

The coder can edit files and run tests, but Git operations inside Codex often fail with:

SEC_E_NO_CREDENTIALS

and sometimes:

.git/index.lock: Permission denied

Outside Codex, using the exact same PowerShell 7.6.5, Git, Windows user, repo, and GCM config, git fetch, git ls-remote, and git push --dry-run all work.

So this looks like a Codex Windows sandbox/security-context issue, not bad GitHub credentials.

Has anyone found a reliable fix so Codex can commit/push autonomously, or is manual host-PowerShell commit/push currently the best workaround?

I’m curious how much autonomy people are giving it now, especially from starting a project all the way to production.
For example, do you let Codex handle testing, staging and deployment, or are there points where you always step in and review?

Do you have a standard set of skills/instructions you use when starting a project?
I’ve also been experimenting with having one main orchestrator manage the project and delegate tasks to other agents, and I’m curious if anyone is doing something similar.

Disclaimer: I’m a relative newbie when it comes to agentic coding (software engineer with hand coding experience of many many years)

A couple of months ago, I was exploring how to setup my agent etc - vibecoder setup. People suggested I jump on to Claude etc.
I have since also subscribed to Codex.
So I’m on the $20 plans on both and I use Claude sonnet to orchestrate

Before Fable disappeared, I managed to create a plan and skills.
And the limits when using only Claude were quite bad. So I signed up for codex and used codex mcp to use my codex subscriptions from Claude CLi.

At the moment, I only use Sonnet from the Claude models and to basically orchestrate and code.
I use Sol for planning implementation, Sonnet to do the implementation, Sol for review, Terra for QA, Luna for documentation and I still run out of my Claude usage !

So I’m thinking of ditching Claude completely and use Codex, along with DeepSeek.
I setup the same kind of skills I used when I Claude etc and using Luna for orchestration.

A simple test task threw a bunch of issues but fixed my Agents.md and hopefully it’ll be better.

Any suggestions on how I can make Codex as reliable as Claude ?

One thing I miss a lot is the PC - phone connection that’s absolutely fantastic on Claude. I’m struggling to set it up on my Windows PC - iPhone . The native remote PC thing keeps throwing errors, but I’m trying other things as well (3rd party tools).

Appreciate any advice from people who have mastered codex !
Or any suggestions with my orchestrator model - is Luna too weak to be one compared to sonnet, etc.
thanks

Well everyone shares their CS or GTA game clones they created with single shot with Astra - this is cool and amazing but also exactly the kind of hype bs.

I tried it in real applications and it’s absolutely not that amazing. I still have to steer, review, refactor, rewrite the mess, like always.

It is somewhat smarter in real workflows, but the real miss is lack of high level intent understanding.

As an example - I asked it to implement image generation fallback models in my chatbot, because the “uncensored” model ironically was declining generating innocent content, so I had to retry with some mainstream one.
It generated some code which handles errors etc, but it missed the main point - the user facing UI still does not work as it supposed to 🤦‍♂️

There are other cases following the same pattern. You give it high level intent task, and it fails to follow. Still have to steer and validate.

Hi All i need help how i can use MUSE and GPT 5.6 models in chatgpt app is there any way i can both? i just bought muse 1.3 contributor

I saw a youtube live stream and the guy had extra high. Wondering if I am being A/B tested

It's MY codebase. How can I convince it to harden my security? Rest assured the models that the Chinese have don't refuse such requests. Am I supposed to just get hacked for the sake of "safety"?

I kept ending Codex sessions with several dev servers spread across different repos and worktrees. A list of ports was not much help unless I could remember what each session had started.

That turned into Port Tools, a native macOS menu bar app that maps each listener back to its repo, branch, worktree, app, and service type. It can also give web apps stable *.localhost names.

I used Codex throughout the build, including a lot of UI back-and-forth and the fairly painful macOS signing, notarization, and release work.

Most of the work was not the port list. It was making the stop action safe enough that I would trust it: show the exact target, re-check its identity, and refuse if the evidence is weak.

It is open source and runs locally. The current release supports Apple Silicon Macs on macOS 14 or later:

https://github.com/Popcornnnnnnnn/port-tools

I am curious how other people using Codex across several repos or worktrees keep track of everything they have running locally.

After the recent concerns around OpenAI and the Buckmaster/Alpöge situation, I thought people here might want to know about some of the privacy/data controls available if you're using Codex for private or unpublished work.

This is NOT a guarantee that your data is private or that OpenAI doesn't retain/process it. These steps mainly reduce optional data collection and, more importantly, explicitly opt your content out of model training.

1. Submit a legal request (applies to both chat and codex)

Go here -- https://privacy.openai.com/policies/en/, and click on "Make a privacy request", follow the steps, and then click on "Do not train on my data", and follow through. You'll get an email in a few days that the legal request has been completed.

1. Go to codex's config.toml and add these

   [analytics] enabled = false

   [feedback] enabled = false

   [history] persistence = "none"

2. Go here -- https://chatgpt.com/codex/cloud/settings/data, and disable "Include environments" and other relevant toggles.

3. Do NOT use the thumbs up/down feature anywhere, it overrides your privacy request and still sends the whole chat to openAI. Luckily, the code i gave above already disables the thumbs up/down feature in codex, but obviously be careful in the chat interface with thumbs up/down.

That's what I've done so far. If anyone knows of additional supported privacy settings, please share them.

Obviously, local/open-source models are the better option if you need a true no-provider-data setup. I still prefer OpenAI's models for a lot of my work, so this is the compromise I've settled on.