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 ai-slop-cleanergit 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/ai-slop-cleaner)<a href="https://agentmods.dev/skills/yeachan-heo/oh-my-codex/ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/yeachan-heo/oh-my-codex/ai-slop-cleaner/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/ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/yeachan-heo/oh-my-codex/ai-slop-cleaner.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 | $0.00017 | $0.01020 |
| Opus 5 | $0.00009 | $0.00510 |
| Sonnet 5 | $0.00003 | $0.00204 |
| Haiku 4.5 | $0.00002 | $0.00102 |
Grade A, and why
ai-slop-cleaner 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 today.
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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Slop Cleaner Task Card
Use this bounded helper for cleanup/refactor/deslop work, not as a competing top-level
workflow. Shared operating invariants live in templates/AGENTS.md; this card defines
scope, smell taxonomy, passes, and evidence.
When to use and inputs
Use when working code is bloated, noisy, repetitive, over-abstracted, or AI-generated; the user requests cleanup/refactor/deslop; or a follow-up left duplicate/dead code, weak boundaries, missing tests, fallback-like paths, or wrappers. Inputs are the requested feature/files and behavior to preserve. A file list scope is valid; keep the pass bounded to it. Limit the pass to the calling task's changed files unless broader cleanup was requested.
Before editing
- Lock behavior with regression tests first: identify behavior to preserve, run/add the narrowest targeted tests, and cover both primary and preserved compatibility/fail-safe fallback paths.
- Create a cleanup plan before code: list scope and smells, include fallback findings/classifications/escalation, and order safest/highest-signal fixes first.
- Inventory fallback-like code in scope: quick hacks, temporary workaround, temporary fallback, just bypass, just skip, fallback if it fails, swallowed errors, silent defaults, broad compatibility shims, and duplicate alternate execution paths.
- Classify each fallback: Masking fallback slop hides evidence, bypasses the contract, suppresses validation, swallows failures, silently defaults, or adds untested paths; Grounded compatibility/fail-safe fallback is narrow at an external/version/fail-safe boundary, documents rationale, preserves failure evidence, and tests primary plus fallback.
- Prefer root-cause repair, deletion, boundary repair, or explicit failure behavior. For broad/ambiguous/cross-layer/architectural findings, invoke
$ralplanfor consensus resolution; when already inside ralplan, ultragoal, team, or another OMX workflow, do not spawn a nested$ralplan—attach the finding to the active handoff.
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.
- today Changed · -1 lines d81095f3480b
- 12d ago First seen · 74 lines · 17 tokens per session scan A 227c02cb4304
ai-slop-cleaner is a skill published in the GitHub repository Yeachan-Heo/oh-my-codex (33,102 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 1,020 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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