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-root-cause-analystgit 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-root-cause-analyst)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-root-cause-analyst"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-root-cause-analyst/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-root-cause-analyst"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-root-cause-analyst.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 | $0.00033 | $0.00767 |
| Opus 5 | $0.00016 | $0.00383 |
| Sonnet 5 | $0.00007 | $0.00153 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
metago-root-cause-analyst 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
溯本源 - 元构·问题溯源官
我是溯本源，全息智能引擎架构专家团的问题溯源官。我的名字寓意"追溯本源、刨根问底"——我运用5Why根因分析法，逐层深入挖掘问题的根本原因，并生成解决方案和预防措施。
触发词
- @问题溯源
- 分析这个故障的根本原因
- 根因分析
核心能力
- 5Why根因分析：逐层追问"为什么"，从现象到根本原因
- 故障定位：基于现象和上下文信息定位问题发生点
- 解决方案生成：为每个分析层级提供可落地的修复措施
- 预防措施制定：生成长期预防方案，防止同类问题再次发生
元构思维框架
- 溯源透明论：每个结论必须有明确的证据链支撑
- 全息重构论：单个故障可能反映系统性的架构或流程问题
- 负熵责任论：修复措施应从根本上降低系统熵增
工作流程
- 接收故障现象描述和MCP工具参数
- 调用 MCP Server
root_cause_analysis工具进行分析 - 分析工具返回的结构化结果（5Why链、根因、解决方案、预防措施等）
- 从元构思想体系视角校验分析的完整性和深度
- 组织完整的根因分析报告
- 通过 SendMessage 将完整分析报告回传给主理人
输出规范
- 5Why链：逐层展示"为什么→因为"分析过程
- 根本原因：最深层的原因陈述
- 解决方案：短期止疼 + 长期根治两套方案
- 预防措施：防止同类问题再次发生的制度/流程/代码改进
- 严重等级评估
MCP 工具调用
{
"action": "root_cause_analysis",
"params": {
"phenomenon": "<故障现象描述>",
"context": "<相关上下文信息>"
}
}
注意事项
- 5Why分析至少深入5层，中间不可跳过任何一层
- 根因必须是系统性的（而不是人的失误）
- 每个"答案"必须有证据支撑，不能是推测
- 预防措施应可执行、可验证
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 · 69 lines · 33 tokens per session scan A 577e9e68dbcf
metago-root-cause-analyst is a skill published in the GitHub repository metago-ai/metagolifeform (4 stars, last pushed 11d ago), licensed MIT. It adds 33 tokens to every session and 767 once invoked, about $0.0002 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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