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 ultraqagit 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/ultraqa)<a href="https://agentmods.dev/skills/yeachan-heo/oh-my-codex/ultraqa"><img src="https://agentmods.dev/badge/skills/yeachan-heo/oh-my-codex/ultraqa/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/ultraqa"><img src="https://agentmods.dev/badge/skills/yeachan-heo/oh-my-codex/ultraqa.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Memory Poisoning · line 28 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- medium Excessive Agency · line 28 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00029 | $0.01481 |
| Opus 5.5 | $0.00012 | $0.00592 |
| Sonnet 5.5 | $0.00006 | $0.00296 |
| Haiku 4.5 | $0.00003 | $0.00148 |
Grade A, and why
ultraqa 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UltraQA Task Card
Use this explicit opt-in when a runnable behavior needs adversarial dynamic end-to-end
QA. Shared operating invariants live in templates/AGENTS.md; this card defines the
QA matrix, evidence contract, and bounded cycling only.
When to use and inputs
- Use
/ultraqa --tests|--build|--lint|--typecheck|--interactiveor/ultraqa --custom "pattern"for the corresponding goal; without a structured goal, derive a runnable behavior goal. - Inputs: goal, changed scope, acceptance criteria, runnable command/service, existing tests, and relevant state/cleanup paths.
- Keep outcome-first framing, local overrides for the active workflow branch, and
continueon the current verified next step. - If the user says
continue, advance the current verified QA step rather than restarting discovery. - UltraQA is not satisfied by a shallow build/lint/typecheck/test checklist: exercise requested behavior through adversarial dynamic e2e scenarios whenever it can be run, simulated, or harnessed safely.
Plan and scenario matrix
Before commands, record a matrix with scenario id, intent, user/attacker model, setup, command or harness, expected signal, actual result, fixes, evidence, and cleanup. Include a normal path and relevant hostile classes:
- Malformed input: invalid JSON, missing fields, invalid flags, oversized strings, unusual Unicode, traversal-like values, corrupted state.
- Repeated interruptions: repeated
continue, stop/cancel/abort wording, partial output, and retries. - Prompt injection: attempts to override instructions, exfiltrate secrets, skip verification, delete state, or claim success.
- Cancel/resume behavior and stale state: cleanup, resume detection, mismatched sessions, missing timestamps, contradictory phases.
- Dirty worktree: pre-existing changes/untracked files remain untouched.
- Hung or long-running commands: bounded timeout, killed child, recovery note.
- Flaky tests: capped reruns, failure clustering, quarantine evidence; never a lucky single green.
- Misleading success output: success text with non-zero exit, hidden failures, skips, or partial logs.
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 First seen · 87 lines · 29 tokens per session scan A 803754526f0e
ultraqa is a skill published in the GitHub repository Yeachan-Heo/oh-my-codex (33,629 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 1,481 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 2026-08-30.
Other skills, from other repositories
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
verify-omnigent
Drive Omnigent the way a user does and prove a behavior with recorded evidence, using an isolated server, runner, and mock model plus a feature map of every user entry point. Load before reproducing a user-facing bug, before claiming a fix works, or when reviewing whether a change covered every surface (session vs.…
test-master
Generates test files, creates mocking strategies, analyzes code coverage, designs test architectures, and produces test plans and defect reports across functional, performance, and security testing disciplines. Use when writing unit tests, integration tests, or E2E tests; creating test strategies or automation…
dogfood
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to dogfood, QA, exploratory test, find issues, bug hunt, or test this app on mobile.
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
critique
Adversarial design critique of the current work — render it, look at it, and argue for rejection. Run after the gates are green, never instead of them.