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.
npx skills add notque/vexjoy-agent --skill codexgit clone --depth 1 https://github.com/notque/vexjoy-agentWrote 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.
[](https://agentmods.dev/skills/notque/vexjoy-agent/codex)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/codex"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/codex/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/notque/vexjoy-agent/codex"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/codex.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Excessive Agency · line 91 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.01880 |
| Opus 5.5 | $0.00007 | $0.00752 |
| Sonnet 5.5 | $0.00004 | $0.00376 |
| Haiku 4.5 | $0.00002 | $0.00188 |
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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 121 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 |
|---|
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 |
Run deterministic work as scripts, not through Codex. The /do model policy selects the lane and passes model plus effort. Legacy GPT-5.5, all Luna choices, and the other non-default GPT-5.6 settings are manual-only; do not substitute them automatically. Luna max, for example, saves 0.44 USD versus Sol high but consumes 45k more output tokens and 65 more steps for two fewer Pass@1 points. Consult the canonical table in /do SKILL.md.
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.
- 1mo ago Changed · +68 lines · -11 tokens per session fb29220fdcc0
- 1mo ago Changed · -68 lines · +11 tokens per session 80aeb4ab990b
- 1mo ago Changed · -4 lines 74ae3109ddee
- 1mo ago First seen · 125 lines · 18 tokens per session scan A 7fc1b696b3fe
codex is a skill published in the GitHub repository notque/vexjoy-agent (443 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 1,880 once invoked, about $0.0001 per session on Opus 5.5. 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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