@daniel_mac8: Codex Pro tip: turn Codex into a research engineer. Take any new agent paper and: 1. Ask: “How would this work in Codex…

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Summary

A tip for Codex users to implement agent research papers directly into their Codex environment using goal mode and local config, with SkillOpt as an example that improved a GPT-5.5 agent by +24.8 points.

Codex Pro tip: turn Codex into a research engineer. Take any new agent paper and: 1. Ask: “How would this work in Codex?” 2. Enter /goal mode 3. Set the goal to implement it in your local config There’s a wave of research coming out on harness engineering: skills, memory, eval loops, tool use, scaffolds, agent feedback. This workflow lets you pull those ideas into your actual Codex environment instead of just bookmarking them. SkillOpt is a perfect example. It treats agent skills as trainable external state: run a rollout, edit the skill, validate, keep what works. In the paper, SkillOpt improved GPT-5.5 inside a Codex-style harness by +24.8 points over the no-skill baseline. That’s the loop you can implement today. It's already possible: research paper → Codex goal → local agent upgrade
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Cached at: 05/26/26, 03:11 PM

Codex Pro tip: turn Codex into a research engineer.

Take any new agent paper and:

  1. Ask: “How would this work in Codex?”
  2. Enter /goal mode
  3. Set the goal to implement it in your local config

There’s a wave of research coming out on harness engineering: skills, memory, eval loops, tool use, scaffolds, agent feedback.

This workflow lets you pull those ideas into your actual Codex environment instead of just bookmarking them.

SkillOpt is a perfect example.

It treats agent skills as trainable external state: run a rollout, edit the skill, validate, keep what works.

In the paper, SkillOpt improved GPT-5.5 inside a Codex-style harness by +24.8 points over the no-skill baseline.

That’s the loop you can implement today. It’s already possible:

research paper → Codex goal → local agent upgrade

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