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-13-industrial-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-13-industrial-intelligence)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-thought-13-industrial-intelligence"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-thought-13-industrial-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-13-industrial-intelligence"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-thought-13-industrial-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 | $0.00057 | $0.00917 |
| Opus 5 | $0.00028 | $0.00458 |
| Sonnet 5 | $0.00011 | $0.00183 |
| Haiku 4.5 | $0.00006 | $0.00092 |
Grade A, and why
metago-thought-13-industrial-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 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
新型工业化智能观 V31.0
描述
通过"模数共振"（模型与数据共振）理念和五大原理，评估工业系统的智能化水平，指导新型工业化的数字化转型与智能制造方案设计。
触发条件
- 评估工业系统的智能化水平
- 设计智能制造方案
- 工业数字化转型规划
- 评估工业互联网/工业 AI 方案
前置条件
- 明确的工业系统或制造场景
- 可获取的产线/设备/流程数据
元思想核心
模数共振核心命题：
工业智能 = 模型(Model) × 数据(Data) × 共振(Resonance)
- 模型与数据不是单向流动，而是双向共振
- 模型从数据中学习，数据被模型优化采集
五大原理：
| 原理 | 内容 | 评估维度 |
|---|---|---|
| P1 感知全息 | 产线全要素数字化感知 | Sensing |
| P2 数据流动 | 数据跨工序/跨系统自由流动 | Flow |
| P3 模型自治 | AI 模型自主决策与优化 | Autonomy |
| P4 人机协同 | 人与智能系统的协同分工 | Collaboration |
| P5 闭环进化 | 系统从运行中持续学习进化 | Evolution |
工业智能化度公式：
I = (Sensing * Flow * Autonomy * Collaboration * Evolution)^(1/5)
- 几何平均：任一原理为 0，整体智能化度为 0
- I >= 0.7：高度智能化
- 0.4 <= I < 0.7：中度智能化
- I < 0.4：初级智能化
模数共振度：
R = min(Model_Driven_Data, Data_Driven_Model)
- R >= 0.7：强共振（模型与数据双向驱动）
- R < 0.4：弱共振（单向流动）
推理框架
步骤 1：五原理评估（0-1 分）
- Sensing：全要素感知覆盖率与精度
- Flow：数据跨系统流动的通畅度
- Autonomy：模型自主决策的比例
- Collaboration：人机协同的分工合理性
- Evolution：系统从运行中学习的闭环度
步骤 2：模数共振度评估
- Model_Driven_Data：模型是否指导数据采集？
- Data_Driven_Model：数据是否驱动模型更新？
- R = min(两者)
步骤 3：智能化度计算
- I = (五维几何平均)
- 判定智能化等级
步骤 4：短板识别与优化
- 找出最低维度
- 设计提升方案
步骤 5：数字化转型路线
- 短期：补齐感知与流动短板
- 中期：提升自治与协同
- 长期：实现闭环进化
验证方法
- 五原理评估是否有产线层面的数据支撑
- I 计算是否正确（几何平均）
- 模数共振度 R 是否有双向驱动的证据
- 短板识别是否准确
- 用产线效率指标回测：I 值提升是否与效率提升一致
- 用工业 4.0 标杆案例对比：I 值排序是否与行业评价一致
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 · 97 lines · 57 tokens per session scan A fdec3059db9b
metago-thought-13-industrial-intelligence is a skill published in the GitHub repository metago-ai/metagolifeform (4 stars, last pushed 11d ago), licensed MIT. It adds 57 tokens to every session and 917 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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