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 ralplangit 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/ralplan)<a href="https://agentmods.dev/skills/yeachan-heo/oh-my-codex/ralplan"><img src="https://agentmods.dev/badge/skills/yeachan-heo/oh-my-codex/ralplan/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/ralplan"><img src="https://agentmods.dev/badge/skills/yeachan-heo/oh-my-codex/ralplan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 26 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 | $0.00021 | $0.03541 |
| Opus 5.5 | $0.00008 | $0.01416 |
| Sonnet 5.5 | $0.00004 | $0.00708 |
| Haiku 4.5 | $0.00002 | $0.00354 |
Grade A, and why
ralplan 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralplan (Consensus Planning Alias)
Ralplan is the canonical consensus-planning stage used by Autopilot between $deep-interview and $ultragoal. It drives Planner, Architect, and Critic planning and records their review lifecycle with RALPLAN-DR structured deliberation (short mode by default, deliberate mode for high-risk work). Local lifecycle evidence is not host-issued security authority, but ordinary progression to Ultragoal must remain reachable after the execution-ready plan and sequential review evidence are durable; missing host provenance must not terminalize Ralplan or block cancel, clear, or recovery.
Usage
$ralplan "task description"
Standalone advisory planning is explicitly opt-in:
$ralplan --advisory "task description"
Advisory runs the same sequential Planner → Architect → Critic review lifecycle, binds the plan and both review artifacts to exact bytes and one tracker-backed iteration, then returns to the caller with active:false. It is a cooperative workflow pause, not a security fence or permission system. It never emits a PreToolUse allow/block decision, never completes the host consensus gate, never authorizes execution, and never suppresses unrelated host behavior. Terminal state must carry explicit false values for the consensus gate, host verification, and execution handoff rather than omitting them. A later concrete affirmative execution request may produce non-authoritative routing context, but it does not persist a permission, rewrite terminal evidence, or create an automatic handoff; quotations, code, examples, documentation, questions, modal requests, negations, and vague approval remain classifier negatives. approved+proven requires complete lifecycle digests plus post-write revalidation. Administrative abandonment is append-only, idempotent for prepared or committed journals, and records a separate byte-bound admin event without rewriting the original closeout journal. Real enforcement requires an explicit host-issued, host-verified receipt or capability on a non-user-mintable surface; local Advisory files, prompts, session/thread fields, tracker records, and HERDR observability cannot substitute for it. On Darwin, each Advisory evidence artifact is limited to 128 KiB by the pinned-directory reader; other supported platforms allow up to 8 MiB.
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 · -65 lines 1d17d7d478ad
- 1mo ago Changed · +9 lines c5bbe67859e1
- 1mo ago First seen · 206 lines · 21 tokens per session scan A 89b891a37002
ralplan is a skill published in the GitHub repository Yeachan-Heo/oh-my-codex (33,629 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 3,541 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
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
component-fixtures
Use when creating or updating component fixtures for screenshot testing or their shared infrastructure, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, validation, and common pitfalls.