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 agentmods add commands/mturac/everything-openai-codex/docsgit clone --depth 1 https://github.com/mturac/everything-openai-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/commands/mturac/everything-openai-codex/docs)<a href="https://agentmods.dev/commands/mturac/everything-openai-codex/docs"><img src="https://agentmods.dev/badge/commands/mturac/everything-openai-codex/docs.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00022 | $0.00509 |
| Opus 5 | $0.00011 | $0.00254 |
| Sonnet 5 | $0.00004 | $0.00102 |
| Haiku 4.5 | $0.00002 | $0.00051 |
Grade A, and why
docs 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 2d 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.
What it actually says
/docs
目的
ライブラリ、フレームワーク、または API の最新ドキュメントを検索し、関連するコードスニペットを含む要約された回答を返します。Context7 MCP（resolve-library-id と query-docs）を使用するため、回答はトレーニングデータではなく最新のドキュメントを反映しています。
使い方
/docs [library name] [question]
複数の単語からなる引数には、単一のトークンとして解析されるよう引用符を使用してください。例：/docs "Next.js" "How do I configure middleware?"
ライブラリまたは質問が省略された場合、ユーザーに入力を求めます：
- ライブラリまたは製品名（例：Next.js、Prisma、Supabase）。
- 具体的な質問またはタスク（例：「ミドルウェアの設定方法は？」、「認証方法」）。
ワークフロー
- ライブラリ ID を解決する — Context7 ツール
resolve-library-idをライブラリ名とユーザーの質問とともに呼び出し、Context7 互換のライブラリ ID（例：/vercel/next.js）を取得する。 - ドキュメントをクエリする — そのライブラリ ID とユーザーの質問を使って
query-docsを呼び出す。 - 要約する — 簡潔な回答を返し、取得したドキュメントから抽出した関連コード例を含める。ライブラリ（関連する場合はバージョンも含めて）に言及する。
出力
ユーザーは、最新のドキュメントに基づいた簡潔で正確な回答と、役立つコードスニペットを受け取ります。Context7 が利用できない場合は、その旨を説明し、トレーニングデータに基づいて回答しますが、ドキュメントが古い可能性があることを注記します。
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.
- 2d ago First seen · 33 lines · 22 tokens per session scan A dc2b8762e530
docs is a command published in the GitHub repository mturac/everything-openai-codex (89 stars, last pushed 12d ago), licensed MIT. It adds 22 tokens to every session and 509 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 2026-09-03.
Other commands, from other repositories
update-faq
could you please update DESIGNFAQ & DXFAQ based on the changes we are trying to achieve.
feature
Orchestrate a complete feature through discovery, spec, implementation, and review.
research
Research a technical or product question.
dev-planner
Generate or update DEV-PLAN.md with phased development plan from Product-Spec.md.
verify
Spawn a fresh-context verifier subagent to check completed work against its specification before trusting it.
weave
Improve active harnesses based on real use, failures, and corrections.