oh-my-codex is a workflow layer for OpenAI Codex CLI that adds prompts, agent teams, skills, hooks, HUDs, and other runtime assistance while leaving Codex as the execution engine. It is for people who use Codex CLI and want structured workflows and additional help as tasks become larger. The catalogue entries are its skills, hooks, and MCP integrations for those Codex workflows.
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 Yeachan-Heo/oh-my-codex --skill autopilotgit clone --depth 1 https://github.com/Yeachan-Heo/oh-my-codexWrote 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/yeachan-heo/oh-my-codex/autopilot)<a href="https://agentmods.dev/skills/yeachan-heo/oh-my-codex/autopilot"><img src="https://agentmods.dev/badge/skills/yeachan-heo/oh-my-codex/autopilot/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/yeachan-heo/oh-my-codex/autopilot"><img src="https://agentmods.dev/badge/skills/yeachan-heo/oh-my-codex/autopilot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00028 | $0.01742 |
| Opus 5 | $0.00014 | $0.00871 |
| Sonnet 5 | $0.00006 | $0.00348 |
| Haiku 4.5 | $0.00003 | $0.00174 |
Grade A, and why
autopilot 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 10d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autopilot
$deep-interview -> $ralplan -> $ultragoal
The chain is not a list of optional hints and Autopilot is not an alias for direct execution. Autopilot supervises each child stage in one recoverable session, carries durable artifacts forward, and continues through implementation verification until the requested outcome is complete or a genuine blocker is recorded.
<Use_When>
- The user explicitly invokes
$autopilot. - The user asks for end-to-end autonomous delivery from an idea, issue, or requirements seed.
- The work needs requirements clarification, consensus planning, durable execution, and evidence-backed completion as one supervised workflow. </Use_When>
<Default_Chain> Run or resume these stages in order:
deep-interview— clarify intent, scope, non-goals, constraints, acceptance criteria, and unresolved decisions. Produce a durable requirements/specification handoff. Deep Interview is a real stage; do not replace it with a one-question check or skip it merely because the task looks actionable.ralplan— turn the clarified requirements into an execution-ready consensus plan with architecture, sequencing, test, and verification guidance. Preserve review evidence as lifecycle evidence, not host-issued security authority.ultragoal— execute the approved plan through durable goals and ledger receipts, implementation, focused verification, cleanup, review, and terminal evidence.
$team, $code-review, and $ultraqa may be used inside the supervised execution when the active Ultragoal plan or verification boundary requires them. They do not replace or weaken the defining three-stage chain.
When review or QA proves the requirements or plan wrong, keep Autopilot active, return its supervised phase to ralplan, attach the findings, and continue through ultragoal again. Implementation-only review fixes may remain within Ultragoal's blocker/review loop.
</Default_Chain>
<Execution_Policy>
- Autopilot MUST begin at
deep-interviewfor a new run and MUST preserve the phase orderdeep-interview -> ralplan -> ultragoal. - Child stages are supervised phases, not peer workflow activations. Keep
mode:"autopilot"active and updatecurrent_phaserather than replacing Autopilot with standalone child state. - Use the current CLI state SSOT (
omx state ... --json) and the current session-scoped state root. Do not create a second writer or revive legacy root-state authority. - Local artifacts, prompts, trackers, transcripts, environment values, and role labels are lifecycle evidence only. Do not reintroduce the retired unrecoverable host-receipt lock or terminalize the workflow because host provenance is unavailable.
- Authority-decreasing operations are always recoverable:
$cancel, state clear, hook disable/uninstall recovery, and stale-state repair must remain available without completion receipts or child-stage approval. - Continue automatically through safe, reversible stage transitions. Stop only for an explicit user cancellation, a human-only dependency, or a verified terminal result. </Execution_Policy>
<State_Management> Autopilot state is session-scoped and owned by the canonical state writer. A new run records at least:
{
"mode": "autopilot",
"active": true,
"current_phase": "deep-interview",
"iteration": 1,
"phase_cycle": ["deep-interview", "ralplan", "ultragoal"],
"handoff_artifacts": {
"deep_interview": null,
"ralplan": null,
"ultragoal": null
},
"return_to_ralplan_reason": null
}
Start or update state only through the CLI-first state surface:
omx state write --input '{"mode":"autopilot","active":true,"current_phase":"deep-interview","state":{"phase_cycle":["deep-interview","ralplan","ultragoal"],"handoff_artifacts":{"deep_interview":null,"ralplan":null,"ultragoal":null},"return_to_ralplan_reason":null}}' --json
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.
- 10d ago First seen · 129 lines · 28 tokens per session scan A c26fc9f4742f
autopilot is a skill published in the GitHub repository Yeachan-Heo/oh-my-codex (33,050 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 1,742 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 2026-08-30.
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