agentleFS
SateezgSateezg/codex-bridge/skills/codex-delegate/SKILL.md

codex-delegate

Skill248 starsChanged 3 months agoMITView on GitHub

Offer to hand heavy work to Codex (GPT-5) as a subagent so it runs on the ChatGPT plan quota instead of burning Claude context and tokens. Use at the START of any task that is large, repetitive, or asset-producing — bulk refactors across many files, generating boilerplate or fixtures, exhaustive repo-wide audits, writing many similar tests, and anything needing mockups or images. Also use whenever the user says "save tokens", "use codex", "delegate this", or "who should do this".

15 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

7 rules. 0 are common, 7 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 11 edited versions we read keep these.

  • 7

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Keep it yourself when the task is low-volume and high-judgment: architecture calls, ambiguous requirements, anything needing the …

    line 28
  2. Distinctive:

    When the rubric says delegate, say so in one or two sentences before starting, and be concrete about the split.

    line 35
  3. Distinctive:

    If the user has already said "use Codex" or "save tokens", skip the question and just do it.

    line 43
  4. Distinctive:

    Verify what comes back: Read the diff, run the tests, spot-check the images.

    line 62
  5. Distinctive:

    Never forward Codex's claim of success without checking.

    line 63
  6. Distinctive:

    Never delegate a task you can't verify: If you couldn't tell a good result from a bad one, do it yourself.

    line 69
  7. Distinctive:

    Uncommitted work: ask the user to commit or stash first when Codex will write to files, so its changes are reviewable as a clean diff.

    line 73

“Common” is measured: kept by at least 6 in 10 of the 11 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Delegate to Codex when it saves the user tokens

The Codex CLI on this machine runs on the user's ChatGPT plan quota — a separate budget from this Claude session. Work pushed to Codex costs no Claude output tokens and, more importantly, keeps large intermediate output (file dumps, generated boilerplate, long search results) out of this context window.

Your job is to notice when that trade is worth making, offer it, and then orchestrate.

The decision rubric

Delegate to Codex when the task is high-volume and low-judgment:

SignalExample
Mechanical edits across many filesrename a symbol in 40 files, migrate an import style, add a header to every module
Bulk generationscaffold 20 CRUD endpoints, write fixtures, generate a large seed dataset
Exhaustive sweeps"find every place we call the old API", audit all routes for auth
Assetsany image, icon set, mockup, or diagram — always Codex, it has gpt-image-2
Long, self-contained subtasksport a module to TypeScript, write the test suite for one file
A genuinely independent opinionyou already have an answer and want it challenged

Keep it yourself when the task is low-volume and high-judgment: architecture calls, ambiguous requirements, anything needing the conversation history, small edits (under ~3 files), or anything the user is iterating on interactively. Round-tripping a two-line fix through Codex is slower and costs more overall.

How to offer it

When the rubric says delegate, say so in one or two sentences before starting, and be concrete about the split. Don't ask a vague "should I use Codex?" — the user can't judge that. Tell them what each side does and what it saves:

This touches ~35 files with the same mechanical change. I can hand the edits to Codex — it runs on your ChatGPT quota and keeps 35 file dumps out of this context — then review its diff here. Want me to?

If the user has already said "use Codex" or "save tokens", skip the question and just do it. If the task is small, don't offer at all — the offer itself is noise.

How to orchestrate

You stay the orchestrator. The pattern is always: you scope, Codex executes, you verify.

  1. Scope it yourself. Work out exactly what needs doing — the file list, the rule, the acceptance criteria. This is the judgment part and it's cheap.
  2. Write a self-contained brief. Codex has none of this conversation. Include the goal, the exact files or glob, the rule to apply, what must not change, and the output you expect back.
  3. Pick the right delegate:
    • codex-implementer subagent — for edits to files (runs workspace-write)
    • codex-reviewer subagent — for review of a diff or module
    • codex-debugger subagent — for root-causing a failure
    • codex-artist subagent — for images, icons, mockups
    • codex-run "<task>" in Bash — for a single quick question, no subagent needed
  4. Verify what comes back. Read the diff, run the tests, spot-check the images. Never forward Codex's claim of success without checking. This is the part that makes delegation safe, and it's why you don't delegate judgment.
  5. Report the split. Tell the user what Codex did and what you checked.

Guardrails

  • Never delegate a task you can't verify. If you couldn't tell a good result from a bad one, do it yourself.
  • workspace-write only when file changes are the point, and tell the user before Codex writes to their repo. Default everything else to read-only.
  • Uncommitted work: ask the user to commit or stash first when Codex will write to files, so its changes are reviewable as a clean diff.
  • Don't fan out. One Codex task at a time unless the user asked for parallel work; each one spends their quota.
  • Codex is not always right. It has no conversation context and can misunderstand a brief. Treat its output as a draft from a capable stranger.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.