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-thought-14-ambient-intelligencegit 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-thought-14-ambient-intelligence)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-thought-14-ambient-intelligence"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-thought-14-ambient-intelligence/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-thought-14-ambient-intelligence"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-thought-14-ambient-intelligence.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 | $0.00055 | $0.00870 |
| Opus 5.5 | $0.00022 | $0.00348 |
| Sonnet 5.5 | $0.00011 | $0.00174 |
| Haiku 4.5 | $0.00006 | $0.00087 |
Grade A, and why
metago-thought-14-ambient-intelligence 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.
What it actually says
境生智能元理论 V5卷
描述
以"感官复归"和"环境即智能"为核心的环境智能理论，通过评估环境对智能的自然嵌入程度，指导智能空间与嵌入式智能系统的设计。
触发条件
- 评估环境/空间的智能化程度
- 设计智能空间或嵌入式智能
- 需要让智能"消失"在环境中
- 对比"显式智能"与"隐式智能"
前置条件
- 明确的物理环境或空间
- 可部署的感知与计算资源
元思想核心
核心命题：
环境即智能，智能即环境
最高境界的智能是用户感知不到的智能
感官复归五原则：
| 原则 | 内容 | 评估维度 |
|---|---|---|
| 隐没性 | 智能设备融入环境，不显眼 | Invisibility |
| 自然性 | 交互方式符合人类本能 | Naturalness |
| 即时性 | 响应无需用户等待 | Immediacy |
| 涌现性 | 智能从环境交互中涌现 | Emergence |
| 适应性 | 环境自适应用户需求 | Adaptivity |
环境智能度公式：
A = (Invisibility * Naturalness * Immediacy * Emergence * Adaptivity)^(1/5)
环境智能度判定：
- A >= 0.8：境生智能（智能完全融入环境）
- 0.5 <= A < 0.8：嵌入智能（智能部分融入）
- A < 0.5：外挂智能（智能与环境分离）
感官复归度：
SR = 1 - (显式交互次数 / 总交互次数)
- SR -> 1：几乎所有交互都是隐式的
- SR -> 0：几乎所有交互都需要显式操作
推理框架
步骤 1：五原则评估（0-1 分）
- Invisibility：智能设备是否可见？
- Naturalness：交互是否需要学习？
- Immediacy：响应延迟是否可感知？
- Emergence：智能是否从环境数据中涌现？
- Adaptivity：环境是否适应用户？
步骤 2：感官复归度评估
- 统计显式交互 vs 隐式交互比例
- 计算 SR
步骤 3：环境智能度计算
- A = (五维几何平均)
- 判定智能等级
步骤 4：隐没性优化
- 识别仍需显式交互的场景
- 设计替代的隐式交互方案
步骤 5：环境智能设计
- 感知层：环境传感器布局
- 计算层：边缘计算部署
- 交互层：自然交互设计
- 涌现层：环境数据驱动的智能涌现
验证方法
- 五原则评估是否有环境层面的证据
- SR 计算是否有交互日志支撑
- A 值是否正确（几何平均）
- 用用户调研验证：用户是否"感知不到"智能的存在
- 用场景测试：新用户是否无需学习即可使用
- 对比传统智能设备：A 值提升是否带来体验提升
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 First seen · 97 lines · 55 tokens per session scan A 2702c29490fd
metago-thought-14-ambient-intelligence is a skill published in the GitHub repository metago-ai/metagolifeform (5 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 870 once invoked, about $0.0002 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 2026-09-03.
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