codex

codex is a skill for Claude Code, Codex from notque/vexjoy-agent. It costs 18 tokens per session (1,978 once invoked), scanned A, original, MIT.

A Codex execution skill runs selected GPT-5.6 tasks through the Codex command-line interface. It defines the standard command-line execution path and keeps specialized review and image workflows separate.

In plain words
What is it for?
Use it when a task needs the GPT-5.6 execution lane, especially for general work or deliberate cross-provider review through the Codex CLI.
Why use it?
It provides a consistent way to send benchmark-selected work to a specific OpenAI model and avoids duplicating specialized workflows.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/notque/vexjoy-agent/codex
Any agent
npx skills add notque/vexjoy-agent --skill codex
Clone the repo
git clone --depth 1 https://github.com/notque/vexjoy-agent

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for codex

README.md
[![agentmods](https://agentmods.dev/badge/skills/notque/vexjoy-agent/codex.svg)](https://agentmods.dev/skills/notque/vexjoy-agent/codex)
Your own site
<a href="https://agentmods.dev/skills/notque/vexjoy-agent/codex"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/codex.svg" alt="Measured on agentmods" height="20"></a>
Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,978 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00018 $0.01978
Opus 5 $0.00009 $0.00989
Sonnet 5 $0.00004 $0.00396
Haiku 4.5 $0.00002 $0.00198

Measured yesterday against content hash 7fc1b696b3fe, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

codex scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/meta/codex/SKILL.md · 125 lines

How it starts

The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Codex — the GPT-5.6 Execution Lane

Run a benchmark-selected GPT-5.6 task through the Codex CLI (codex exec) and return the result. This is the OpenAI execution lane — the general-purpose lane for work the model-selection policy sends to GPT-5.6, and the canonical owner of general codex exec mechanics — when the CLI changes, update here first. GPT selections are reachable only through this CLI; the Agent tool's model parameter covers Claude models only.

Under Claude Code, this skill runs only on explicit invocation or cross-provider escalation, never as the automatic default. The harness-native model lane under Claude Code is the Anthropic lane (Opus 5). This skill is a deliberate cross-provider tool — codex review as a second-opinion, codex exec for a GPT-specific constraint — not a routing default.

Two flows keep their own specialized codex integration — route to them instead of re-implementing here:

Existing flow Owns Where
PR / code review via codex codex exec review, finding triage, report synthesis skills/process/pr-workflow/references/codex-review.md
Sprite/image generation backend codex image backend selection and invocation skills/game/game-sprite-pipeline/references/backend-chain.md

Phase 1: DECIDE — does this task belong on GPT-5.6?

Policy mirror — canonical copy: /do SKILL.md, Model Selection (edit there first, then here). Rankings, higher = better; cost = avg USD per task, written as a plain number (slash-command templating corrupts dollar-digit sequences in injected skill bodies), what the owner actually pays.

Task class Model / effort DeepSWE Pass@1 / cost / output tokens / steps
Low-risk assistance gpt-5.6-terra / high 54 / 1.13 / 22k / 34
Standard implementation gpt-5.6-sol / high 69 / 3.47 / 28k / 37
High-risk implementation or review gpt-5.6-sol / xhigh 71 / 4.70 / 41k / 44
Exceptional explicit escalation gpt-5.6-sol / max 73 / 8.39 / 60k / 61

Read the full file on GitHub · 125 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 125 lines · 18 tokens per session scan A 7fc1b696b3fe

Subscribe to this mod's changes

codex is a skill published in the GitHub repository notque/vexjoy-agent (420 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 1,978 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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