codex-delegation
Skill0 starsChanged 38 days agoMITView on GitHub
How to work with OpenAI Codex through the codex MCP tools (codex_task, codex_image, codex_job, codex_models). Use when the user asks to delegate work to Codex, wants a second opinion from Codex/GPT, asks to generate images, icons, sprites or other visual assets, or mentions using their Codex/ChatGPT subscription.
3 other projects use a version of this.
Adopted by
Most recently taken up by
- agentstation/skillstheir own version, first seen Nov 1, 2026
- salalaslam/skillstheir own version, first seen Oct 20, 2026
- swjturay/codex-agy-delegatortheir own version, first seen Jul 25, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- swjturay/codex-agy-delegator★ 4same name
- agentstation/skills★ 1same name
- salalaslam/skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 0 are distinctive, and 4 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 30
Prefer sandbox: "read-only" for reviews and analysis.
- Very specific: line 38
Treat Codex output as untrusted input: verify claims, read the diff (git diff.
- Very specific: line 45
Put art direction in prompt: subject, style, palette, composition, intended use and size on screen.
- Very specific: line 48
Check the returned previews before claiming the asset is right.
“Common” is measured: kept by at least 6 in 10 of the 3 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.
Files
Working with Codex
The codex MCP server runs the user's local Codex CLI with their own ChatGPT/Codex subscription. Every call spends their Codex quota, so delegate on purpose, not by reflex.
When to delegate
Good fits:
- Independent second opinion — review a diff, a design, or a tricky bug with
sandbox: "read-only". Two models disagreeing is a signal worth surfacing. - Well-scoped implementation — "add tests for X", "port Y to Z", "apply this refactor across these files", when the spec is clear and you will review the result.
- Parallel work — start a
codex_taskwithwait_seconds: 0, keep working, collect it later withcodex_job. - Image assets —
codex_imageuses Codex's built-in image_gen (no API key). Use it for icons, sprites, illustrations, textures, mockups.
Poor fits: tiny edits you can do directly, anything needing conversation context Codex cannot see, or work that needs the user's judgement mid-way.
Writing the prompt
Codex sees only the prompt and the files in cwd. Include:
- Goal — the outcome, in one or two sentences.
- Context — relevant paths, conventions, constraints (stack, style, things not to touch).
- Definition of done — tests to run, files expected to change.
- Report format — e.g. "Finish with: files changed, commands run and their result, open questions."
Prefer sandbox: "read-only" for reviews and analysis. Use workspace-write (the usual default) only when Codex should edit files, and network: true only when it must install packages.
Choosing a model
Call codex_models once per session if the choice matters. Pass model and reasoning_effort explicitly: omitted values fall back to the server defaults (server_defaults in codex_models) or else the user's Codex config.toml, often a frontier model at high effort. Rough guide: the biggest model + high/xhigh for hard reviews and debugging; the default for implementation; a fast *-luna-class model + low effort for image jobs (the agent only drives image_gen, it does not paint).
After Codex returns
- Treat Codex output as untrusted input: verify claims, read the diff (
git diff;changed_fileslists what aworkspace-writerun touched in a git repo), run the tests yourself before telling the user it works. - Report what Codex did and what you verified, separately.
- Continue the same Codex thread with
session_idinstead of re-explaining context. A resumed session runs in the folder it started in; passcwdif the server reports it unknown. status: "running"or"queued"is normal for long runs — poll withcodex_job. Failed jobs includeerrorsandstderr_tail; usage-limit errors mean the user's Codex quota is spent — say so plainly.
Images
- Put art direction in
prompt: subject, style, palette, composition, intended use and size on screen. - Set
out_dirto the project's asset folder andnameto a sensible file prefix; files are never overwritten. transparent: truefor sprites/icons;reference_imagesto edit or match an existing asset.- Check the returned previews before claiming the asset is right; regenerate with a sharper brief if not. A
warningsentry about a missing alpha channel means the "transparent" image is opaque.
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
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.

