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aikazuaikazu/claude-codex-mcp/skills/codex-delegation/SKILL.md

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

  • Other versions3 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 3

    1. swjturay/codex-agy-delegator★ 4same name
    2. agentstation/skills★ 1same name
    3. 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
  1. Very specific:

    Prefer sandbox: "read-only" for reviews and analysis.

    line 30
  2. Very specific:

    Treat Codex output as untrusted input: verify claims, read the diff (git diff.

    line 38
  3. Very specific:

    Put art direction in prompt: subject, style, palette, composition, intended use and size on screen.

    line 45
  4. Very specific:

    Check the returned previews before claiming the asset is right.

    line 48

“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.

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

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_task with wait_seconds: 0, keep working, collect it later with codex_job.
  • Image assets — codex_image uses 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:

  1. Goal — the outcome, in one or two sentences.
  2. Context — relevant paths, conventions, constraints (stack, style, things not to touch).
  3. Definition of done — tests to run, files expected to change.
  4. 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_files lists what a workspace-write run 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_id instead of re-explaining context. A resumed session runs in the folder it started in; pass cwd if the server reports it unknown.
  • status: "running" or "queued" is normal for long runs — poll with codex_job. Failed jobs include errors and stderr_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_dir to the project's asset folder and name to a sensible file prefix; files are never overwritten.
  • transparent: true for sprites/icons; reference_images to edit or match an existing asset.
  • Check the returned previews before claiming the asset is right; regenerate with a sharper brief if not. A warnings entry about a missing alpha channel means the "transparent" image is opaque.

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