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-12-agentive-osgit 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-12-agentive-os)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-thought-12-agentive-os"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-thought-12-agentive-os/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-12-agentive-os"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-thought-12-agentive-os.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 | $0.00909 |
| Opus 5 | $0.00028 | $0.00454 |
| Sonnet 5 | $0.00011 | $0.00182 |
| Haiku 4.5 | $0.00006 | $0.00091 |
Grade A, and why
metago-thought-12-agentive-os 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
Agentive OS 元思想 V32.0
描述
将操作系统重新定义为智能体——OS 不再是被动资源管理者，而是主动的智能体，通过评估 OS 的智能化程度，指导下一代操作系统的架构设计。
触发条件
- 评估操作系统的智能化程度
- 设计智能操作系统架构
- 对比传统 OS 与智能 OS
- 操作系统级别的 AI 集成
前置条件
- 明确的操作系统或 OS 级平台
- 可识别的资源管理与调度机制
元思想核心
核心命题：
OS = Agent
操作系统不再是资源管理者，而是智能体
Agentive OS 五特征：
| 特征 | 传统 OS | Agentive OS | 评估维度 |
|---|---|---|---|
| 主动性 | 被动响应请求 | 主动预测与优化 | Proactivity |
| 感知力 | 无环境感知 | 感知用户/硬件/网络状态 | Perception |
| 决策力 | 规则调度 | 基于世界模型的智能决策 | Decision |
| 学习力 | 无学习 | 从使用模式中学习优化 | Learning |
| 交互力 | 命令行/API | 自然语言/多模态交互 | Interaction |
Agentive 度公式：
A = (Proactivity + Perception + Decision + Learning + Interaction) / 5
Agentive 度判定：
- A >= 0.8：强 Agentive OS
- 0.5 <= A < 0.8：过渡型 OS
- A < 0.5：传统 OS
OS-Agent 融合三阶：
- 一阶（OS with Agent）：OS 内嵌 AI 助手（如 Copilot）-> A 0.2-0.4
- 二阶（OS as Agent）：OS 核心功能由 Agent 驱动 -> A 0.5-0.7
- 三阶（OS = Agent）：OS 本质就是 Agent -> A 0.8-1.0
推理框架
步骤 1：五特征评估（0-1 分）
- Proactivity：OS 是否主动预测用户需求？
- Perception：OS 是否感知环境状态？
- Decision：OS 调度是否基于智能决策？
- Learning：OS 是否从使用中学习？
- Interaction：OS 交互是否支持自然语言/多模态？
步骤 2：Agentive 度计算
- A = 五维均值
- 判定 OS 类型
步骤 3：融合阶次判定
- 一阶：AI 作为 OS 的附属工具
- 二阶：AI 驱动 OS 的核心功能
- 三阶：OS 与 AI 本质合一
步骤 4：架构差距分析
- 识别从当前阶次到下一阶次的差距
- 分析每个差距的技术可行性
步骤 5：演进路线设计
- 短期：提升最低维度
- 中期：跨越融合阶次
- 长期：达到 OS = Agent
验证方法
- 五特征评估是否有 OS 架构层面的证据
- A 值计算是否正确
- 融合阶次判定是否与实际 OS 行为一致
- 演进路线是否可行（非空中楼阁）
- 用用户场景测试：用户需求是否由 OS 主动满足（而非用户主动操作）
- 用性能指标验证：Agentive 特性是否带来实际效率提升
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.
- 9d ago First seen · 95 lines · 57 tokens per session scan A 98ac4ad50776
metago-thought-12-agentive-os 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 909 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.
Other skills, from other repositories
saleae-mcp-debugger
Use this skill for hardware-in-the-loop firmware capture and decode workflows through the Saleae Logic 2 MCP server.
jetson-diagnostic
Read-only Jetson health snapshot for identity, memory, GPU, thermal, power, storage, services, and top processes.
offensive-z-wave
Z-Wave attack methodology — sniffing with Z-Force / EZ-Wave / RTL-SDR + ZniffMobile, S0 (legacy) network-key derivation flaw and key reuse, S2 (modern) ECDH commissioning analysis, replay/injection on unauthenticated nodes, default-key brute-force on test deployments, and home-automation hub pivots. Use when targeting…
rtl-property-inference
Automatically infer formal correctness properties from Verilog/SystemVerilog RTL code and generate SystemVerilog Assertions (SVA). Identifies control-flow invariants (mutual exclusion, valid-ready handshakes, pipeline ordering, safety properties), liveness expectations, and temporal properties. Use when working with…
unifly
This skill should be used when the user asks to "manage UniFi devices", "configure UniFi networks", "create a VLAN", "provision an SSID", "create firewall rules", "reorder firewall policies", "create a NAT rule", "set up port forwarding", "configure masquerade NAT", "add DNS records", "manage traffic matching lists"…
esp32-development
Build, configure, flash, test, debug, and recover firmware for ESP32-family boards, including ESP-IDF C/C++, Arduino/PlatformIO, MicroPython, CircuitPython, ESPHome, Zephyr, Rust, and NuttX. Use when identifying an ESP32 board, choosing a framework, wiring GPIO or peripheral buses, integrating sensors or actuators…