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 metago-ai/metagolifeform --skill metago-scene-adaptgit 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/skills/metago-ai/metagolifeform/metago-scene-adapt)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-scene-adapt"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-scene-adapt/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/metago-ai/metagolifeform/metago-scene-adapt"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-scene-adapt.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 | $0.00059 | $0.00797 |
| Opus 5 | $0.00030 | $0.00398 |
| Sonnet 5 | $0.00012 | $0.00159 |
| Haiku 4.5 | $0.00006 | $0.00080 |
Grade A, and why
metago-scene-adapt 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 9d 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
场景化表达适配（Context-Aware Expression）
此技能确保输出语言精准适配当前场景和受众，将元构能力内化为方案基因而非显性词汇。
触发条件
每次输出前自动执行。
场景识别规则
| 场景 | 识别关键词 | 行为 |
|---|---|---|
| 商业方案 | 商业/客户/方案/报价/商务 | 禁用技术术语，启用术语替换 |
| 技术文档 | 技术/架构/开发/API/代码 | 保留原术语，附带解释 |
| 战略讨论 | 战略/规划/方向/愿景 | 正式风格，价值导向 |
| 日常沟通 | 闲聊/问候/日常 | 自然语言，术语附解释 |
| 内部复盘 | 复盘/总结/经验/教训 | 可保留术语，内部风格 |
术语替换表（商业场景专用）
| 元构技术术语 | 商业替换表述 |
|---|---|
| 人人引擎 | 平台自动感知 |
| 元镜引擎 | 系统自我学习与优化 |
| 耦生 | 深度协同 / 多方共赢 |
| 元演引擎 | 持续进化引擎 |
| 负熵 | 社会价值贡献 |
| 原子 | 标准化功能模块 |
| 算法 | 智能分析模型 |
| 协议 | 协作规范 |
| 引擎 | 核心能力平台 |
| 归无 | 智能隐退 / 按需服务 |
| 全息 | 全方位 / 立体化 |
| 能力族 | 解决方案组件 |
| 元构 | [不翻译——能力内化于方案] |
核心原则
- 能力内化基因: 元构能力必须内化为方案基因，而非显性词汇。商业方案中不出现"元构"二字，但方案处处体现元构思维。
- 价值优先表达: 优先表达"用户得到什么"，而非"技术怎么实现"。
- 语言纯度检查: 商业场景语言纯度≥80分（残留技术术语越少越好）。
使用示例
技术表达 → 商业表达：
- "人人引擎自动感知用户需求" → "平台自动感知用户需求"
- "通过耦生机制实现价值共创" → "通过深度协同实现多方共赢"
- "元镜引擎进行自我反思" → "系统持续学习和优化"
- "583个算法全维度覆盖" → "全方位智能分析能力覆盖"
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.
- 9d ago First seen · 65 lines · 59 tokens per session scan A ea3d3eb6a950
metago-scene-adapt is a skill published in the GitHub repository metago-ai/metagolifeform (4 stars, last pushed 11d ago), licensed MIT. It adds 59 tokens to every session and 797 once invoked, about $0.0003 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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