Codex Coder logo

Codex Coder

Organization
sno-ai
codex-coder

Route production coding tasks from Claude Code to the user-level Codex Shell, TypeScript, Python, or Rust workflow through a direct `codex exec` run. Use for "codex-coder", "ask Codex to write code", "generate with Codex", language migrations, or mixed Shell/TypeScript/Python/Rust implementation.

Overview

Publishersno-ai
Repositorysno-station-skills
Skill namecodex-coder
Stars
857
Forks
609
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by sno-ai on GitHub. Read the source before you install it.

Installation

Install the Codex Coder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/sno-ai/sno-station-skills/tree/main/skills/M-build-and-rollout/codex-coder/overlay/claude
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/sno-ai/sno-station-skills.git /tmp/sno-station-skills
mkdir -p .claude/skills
cp -r /tmp/sno-station-skills/skills/M-build-and-rollout/codex-coder/overlay/claude .claude/skills/codex-coder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codex Coder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Codex Coder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Codex Coder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Codex Coder Router for Claude Code

Carry the task's finite time budget into the coding assignment, including test time. Require a duration estimate before each targeted test run; full evaluation, unit, integration, or repository suites require the owner's explicit request, not dispatcher approval. New security checks require the owner's specific approval; auxiliary checks must not block the requested function or development work.

Act as a thin dispatcher. Determine the target language, then hand the whole implementation to Codex through a direct codex exec run owned by this session. The canonical language instructions live under ~/.codex/skills; do not use same-named Claude skills as substitutes.

Dispatch always uses low effort, set by this skill's frontmatter. The separate Codex coding task uses the model from role coder with high effort. If the original request selects the reviewer model, use the model from role reviewer with low effort. Roles live in the [models] table of ${XDG_CONFIG_HOME:-$HOME/.config}/sno/models.toml; an absent file, missing role, or empty value means omit --model so the Codex default applies. An effort explicitly selected in the original request overrides either default. Loading a language skill does not switch effort inside a running task: set both values when launching that task.

Codex runs with the sandbox off

Keep the owner's configured sandbox_mode = "danger-full-access"; do not override it in a dispatch, config, flag, or profile. A connection or credential error does not identify its cause. Inspect the effective configuration, actual endpoint, and error; repair the observed cause within existing authority. Preserve valid results from unaffected tests.

Transport: codex exec from the dispatching session

The coding run is a plain codex exec started by the session that runs this skill, with run_in_background, and watched by heartbeat. Nothing sits between this session and the Codex process.

Why not the plugin's app-server route: the plugin's codex-rescue subagent starts the task, and the task's life is tied to that subagent's session plus a broker that is shared by every Claude session in the same directory. The plugin's SessionEnd hook shuts that broker down and kills the session's jobs without checking whether a task is still running — so a subagent session ending early, or any other session in that directory ending, kills a coding run mid-flight and leaves its record saying running forever. A direct codex exec has one owner: this session.

Do not:

  • dispatch through the codex:codex-rescue subagent or the plugin's task command;
  • call mcp__codex__codex as part of this workflow;
  • pass any flag or profile that turns a sandbox back on.

Route the Target Language

Use this precedence order:

  1. The language or destination the user explicitly requested.
  2. The files the task must create or change.
  3. The repository's manifest, immediate callers, and nearby tests.
  4. For a migration, the language being produced; treat the source language as context.
  5. For generated code, the maintained source or generator language unless the user explicitly asks to edit generated output.

Ask one concise clarification only when those signals still do not identify the destination. Do not infer a language from personal preference or tool availability.

DestinationStrong signalsCodex skill
Bash or shell.sh, a Bash shebang, shell CLI, automation, process wrapper, or Git/infra helper$shellscript-coder
TypeScript or TSX.ts, .tsx, .mts, .cts, tsconfig.json, a typed Node or React target$ts-coder
Python.py, pyproject.toml, Python service, package, script, or test target$python-coder
Rust.rs, Cargo.toml, Cargo workspace, crate, Rust CLI, service, library, or FFI target$rust-coder

JavaScript without a TypeScript target is not automatically TypeScript. An unsupported destination must be surfaced instead of silently routed to the nearest skill.

The language skills are optional. When the selected one is not installed under ~/.codex/skills, leave it out of the handoff, tell Codex to follow the repository's own conventions, and say in the report which skill was missing.

Build One Codex Handoff

Use one codex exec run for the full user request, including mixed-language work. In the forwarded prompt:

  1. Name every selected skill explicitly, such as $ts-coder and $rust-coder.
  2. Tell Codex to read each selected user-level SKILL.md and its required references completely before editing.
  3. Tell Codex that this codex exec run is already the primary execution run. It must follow the selected skill's repository, language, implementation, and verification rules directly, and must not launch a nested Codex process.
  4. Preserve the user's request verbatim under a short routing preamble.
  5. Include the current working directory and the language-to-file ownership found during routing.
  6. For mixed work, require one shared contract, contract-owner-first implementation, language-specific checks, and the smallest cross-language integration check.

Use this prompt shape:

Include this standing test rule in every forwarded prompt: a test written to prove a fix must assert observable results and literal values only; never import or reference a symbol introduced by the same change (a new export, constant, or function). Stashing the source change must let the unchanged test run red, without patching the test.

text
Use the user-level Codex skills $<language-skill> [and $<language-skill>]. Read each selected SKILL.md under ~/.codex/skills and every required reference completely, then follow their repository, language, implementation, and verification rules as the execution contracts. This `codex exec` run is already the primary execution run: implement directly and do not launch a nested Codex process. Work in <current working directory>. Keep the implementation minimal and verify it with the repository's real commands.

<standing test rule above, verbatim>

Routing evidence:
- <language>: <owned targets and decisive repository signal>

Before editing, name the one claim in this request you would most challenge — a wrong line number, a fix that could change behaviour the request says stays identical, a test that cannot go red — and say why. If there is none, say so in one line.

Original request:
<verbatim user request>

Do not restate the language standards in the handoff. The selected Codex skills are the single source of truth; only the transport is this session's codex exec run.

Launch and wait

  1. Write the handoff prompt to a file in the scratchpad (<scratch>/codex-<slug>.prompt.md).

  2. Resolve model and effort from the original request: default to role coder and model_reasoning_effort=high; an explicit reviewer-model selection uses role reviewer and model_reasoning_effort=low. Read the role's value from [models] in ${XDG_CONFIG_HOME:-$HOME/.config}/sno/models.toml and pass it as --model <value>; omit --model when the value is absent or empty. If the request explicitly selects an effort, use it with the selected model.

  3. Start the run in the background — one Bash call, run_in_background: true, never a trailing &:

    cd <workspace> && codex exec --skip-git-repo-check \
        [--model <value>] -c model_reasoning_effort=<effort> -c model_reasoning_summary=detailed \
        -o <scratch>/codex-<slug>.last.md - < <scratch>/codex-<slug>.prompt.md \
        > <scratch>/codex-<slug>.out.log 2> <scratch>/codex-<slug>.err.log; \
      printf '%s\n' "$?" > <scratch>/codex-<slug>.done

    Use the coding effort above; do not inherit the dispatcher's low. The .done marker is written only when codex has exited, and carries the exit status.

  4. Arm the wait with the heartbeat skill: heartbeat --label codex-<slug> --until-file <scratch>/codex-<slug>.done -- tail -c 300 <scratch>/codex-<slug>.err.log, then attach the reader line the arm output prints. Each tick shows the newest reasoning summary, which is the only liveness signal a codex run gives (a healthy run can be silent on stdout for minutes).

  5. When the marker lands: exit status 0 and a non-empty .last.md is the report — relay it. Any other status, or an empty .last.md, is a failed run: read .err.log, say what it says, and decide with the user whether to rerun. Never report success without the marker.

<workspace> is the checkout and branch the user is working in. Every run, parallel or not, edits there.

A continuation ("continue the previous Codex task") is a fresh codex exec whose prompt starts with the previous run's .last.md verbatim under a one-line "previous run said:" header; there is no session to resume.

Coding is write-capable by default (the sandbox is off). Respect explicit read-only, diagnosis-only, or review-only requests by saying so in the prompt.

The selected Codex skill owns repository inspection, edits, verification, and refinement. Claude does not run a parallel implementation or silently repair a failed Codex result.

Failure Boundary

If codex is not on PATH or authentication fails, locate the installed runtime and repair PATH or the existing sanctioned session setup, then resume. Ask only when authentication requires an owner-only action; continue independent repository work meanwhile. A run whose .err.log says it cannot reach the network or secrets requires inspection of the effective configuration and actual endpoint. Repair the measured cause within existing authority and resume; do not infer the cause from the error alone. A run with no growth in .err.log for 15 minutes is stalled: kill that codex process by the pid the background task reports (never by pattern), record it, and rerun the same prompt once.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Codex Coder AI skill do?

Route production coding tasks from Claude Code to the user-level Codex Shell, TypeScript, Python, or Rust workflow through a direct `codex exec` run. Use for "codex-coder", "ask Codex to write code", "generate with Codex", language migrations, or mixed Shell/TypeScript/Python/Rust implementation.

Why use Codex Coder on TypingMind?

Because you install it once and use it with any model. Codex Coder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Codex Coder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sno-ai/sno-station-skills/tree/main/skills/M-build-and-rollout/codex-coder/overlay/claude. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Codex Coder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Codex Coder?

As many as you like. As long as a model supports skills, you can use Codex Coder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Codex Coder AI skill free?

Yes. It is published on GitHub by sno-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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