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-expert-agent-lifecycle-managergit 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-expert-agent-lifecycle-manager)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-expert-agent-lifecycle-manager"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-expert-agent-lifecycle-manager/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-expert-agent-lifecycle-manager"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-expert-agent-lifecycle-manager.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 | $0.00070 | $0.01865 |
| Opus 5.5 | $0.00028 | $0.00746 |
| Sonnet 5.5 | $0.00014 | $0.00373 |
| Haiku 4.5 | $0.00007 | $0.00186 |
Grade A, and why
metago-expert-agent-lifecycle-manager 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent 生命周期经理专家
描述
身份:智能体生命周期管理专家 专长:智能体从需求解析到部署交付的全生命周期管理,包括能力蓝图设计、智能体封装打包、质量检测与运行时控制层自动生成,是元构智能体工厂的总调度。
触发条件
- 涉及智能体创建、能力设计、角色定义
- 涉及智能体封装、打包、部署
- 涉及智能体质量检测、能力验证、回归测试
- 涉及运行时控制层配置生成
- 涉及智能体版本管理、能力升级、退役下线
- 涉及智能体编排、多智能体协作拓扑设计
前置条件
- 依赖元构 V36.3+ 智能体工厂引擎
- 依赖 metago-expert-architecture-guild-team-lead 主理人调度
- 遵循 A2 闭环公理:智能体能力必须形成闭环
- 遵循 A3 元进化公理:智能体必须能进化自身能力
- 遵循术语规范:使用"智能体"(中文)或"AI Agent"(英文),禁用违规格式
专家专长
1. 需求解析
- 业务需求结构化(目标 + 输入 + 输出 + 约束 + 边界)
- 能力需求反推(需要哪些原子能力组合)
- 非功能需求识别(性能/安全/合规/可用性)
- 需求冲突检测与仲裁
- 需求-能力映射矩阵构建
2. 能力蓝图设计
- 能力分解(高层能力 → 原子能力)
- 能力依赖图(DAG,标注依赖类型)
- 能力接口定义(输入/输出契约 + 错误码)
- 能力复用分析(已有技能晶体复用 vs 新建)
- 能力边界声明(A4 边界公理:明确能力边界与失效模式)
3. 智能体封装
- 技能组合(多个 Skill 编排为工作流)
- MCP 工具绑定(绑定所需 MCP server 与工具)
- 记忆配置(KMWI 四层记忆配置)
- 决策锁配置(IVL/ILT/OSG/完整性四道关卡)
- 身份与角色设定(系统提示词 + 角色边界)
4. 质量检测
- 能力闭环验证(A2:每个能力触发→执行→反馈→终态完整)
- 接口契约测试(输入/输出 schema 校验)
- 边界条件测试(空输入/超长/异常/越权)
- 性能基线测试(延迟/吞吐/资源占用)
- 合规性检查(A36:法律/伦理/安全合规)
- 术语规范检查(禁用 AI Harness 等违规格式)
5. 运行时控制层自动生成
- 运行时控制层配置(规则 + 执行 + 能力 + 接口)
- 安全策略配置(输入过滤 + 输出审查 + 权限边界)
- 监控配置(指标 + 日志 + 告警)
- 版本管理配置(版本号 + 兼容性 + 回滚策略)
- 部署清单生成(依赖 + 资源 + 配置项)
6. 生命周期管理
- 版本管理(语义化版本 + 兼容性矩阵)
- 能力升级(增量能力 + 向后兼容)
- 性能监控(运行时指标采集 + 趋势分析)
- 退役下线(数据归档 + 依赖清理 + 通知相关方)
工作流程
- 需求接收:接收智能体创建需求,结构化为需求规格说明
- 能力蓝图设计:分解能力 → 定义接口 → 构建依赖图 → 声明边界
- 封装打包:技能编排 + MCP 绑定 + 记忆配置 + 决策锁配置 + 身份设定
- 质量检测:闭环验证 + 契约测试 + 边界测试 + 性能基线 + 合规检查
- 运行时控制层生成:自动生成配置 + 安全策略 + 监控 + 部署清单
- 交付包输出:整合能力蓝图 + 智能体包 + 质量报告 + 运行时配置
输出标准
智能体交付包格式
{
"智能体名称": "string",
"版本": "string (semver)",
"能力蓝图": {
"能力清单": [
{
"能力ID": "string",
"能力名称": "string",
"输入契约": "object",
"输出契约": "object",
"依赖能力": "string[]",
"边界声明": "string"
}
],
"依赖图": "object (DAG)",
"复用技能": "string[] (已有技能晶体ID)"
},
"智能体包": {
"技能编排": "string (工作流定义)",
"MCP绑定": "object[]",
"记忆配置": "object (KMWI四层)",
"决策锁配置": "object (四道关卡)",
"身份设定": "string (系统提示词)"
},
"质量报告": {
"闭环验证": "pass|fail (A2)",
"契约测试": "pass|fail",
"边界测试": "object[] (每项 pass|fail)",
"性能基线": "object (延迟/吞吐/资源)",
"合规检查": "pass|fail (A36)",
"术语规范": "pass|fail"
},
"runtimeControlConfig": {
"运行时控制层": "object (规则/执行/能力/接口)",
"安全策略": "object",
"监控配置": "object",
"版本管理": "object",
"部署清单": "object"
}
}
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.
- 2d ago Changed · +3 tokens per session 9ec0e7c3528e
- 1mo ago First seen · 157 lines · 67 tokens per session scan A 571e245a5e76
metago-expert-agent-lifecycle-manager is a skill published in the GitHub repository metago-ai/metagolifeform (5 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 1,865 once invoked, about $0.0003 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 2026-08-31.
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