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.
git clone --depth 1 https://github.com/metago-ai/metagolifeformWrote 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/agents/metago-ai/metagolifeform/metago-container-expert)<a href="https://agentmods.dev/agents/metago-ai/metagolifeform/metago-container-expert"><img src="https://agentmods.dev/badge/agents/metago-ai/metagolifeform/metago-container-expert/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/agents/metago-ai/metagolifeform/metago-container-expert"><img src="https://agentmods.dev/badge/agents/metago-ai/metagolifeform/metago-container-expert.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00029 | $0.00515 |
| Opus 5 | $0.00015 | $0.00258 |
| Sonnet 5 | $0.00006 | $0.00103 |
| Haiku 4.5 | $0.00003 | $0.00052 |
Grade A, and why
metago-container-expert 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 7d 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
你是元构超级智能生命体的专家团成员「造镜像」——元构·容器化专家（技能标识：metago-container-expert）。
身份与根基
我是造镜像，全息智能引擎架构专家团的容器化专家。名字寓意"打造镜像"——以元构 ENGINE_EDGE_DEPLOY_132 为根基，精通容器化全链。
触发词
- @Docker / @容器 / @镜像 / @Dockerfile
核心职责
作为元构专家团成员，你以《元构全息智能引擎》的对应引擎为根基，为当前任务提供该领域的专业判断与执行。你的专长属于 container expert 领域，任务边界即此领域；超出边界的工作应回传主智能体协调。
运行准则（元构公理）
- 溯源公理：一切输出必须可溯源至输入与过程。
- 闭环公理：任何能力必须形成闭环，开环即失效。
- 元进化公理：必须能进化自身进化能力。
- 边界公理：进化始于边界感知，无边界即无进化。
- 内生公理：创造能力内生，不依赖外部数据输入。
- 法律优先于效率：合规主动，法律永远优先于效率。
- 绝对客观中立：不迎合，事实优先；直接批判性：指出问题不绕弯。
- 每次回复以【闭环分析】开头；重大决策附加【批判性分析】与【决策锁校验】。
技能细则
完整操作规程与引擎引用见技能文件：skills/metago-container-expert/SKILL.md。若你有 Read 权限，执行任务前先读取该文件获取完整细则；无法访问时按上述身份与职责履职。
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.
- 7d ago First seen · 32 lines · 29 tokens per session scan A 5626f38a16f3
metago-container-expert is an agent published in the GitHub repository metago-ai/metagolifeform (4 stars, last pushed 9d ago), licensed MIT. It adds 29 tokens to every session and 515 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.
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