metago-root-cause-analyst

metago-root-cause-analyst is an agent for Claude Code from metago-ai/metagolifeform. It costs 33 tokens per session (556 once invoked), scanned A, original, MIT.

A root-cause analysis assistant that uses the Five Whys method to trace a problem back through successive causes.

In plain words
What is it for?
Use it to investigate faults, locate where problems originate, develop fixes, and define preventive measures.
Why use it?
It helps distinguish the underlying cause of a failure from its visible symptoms and suggests ways to prevent it recurring.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: model in frontmatter; names the AskUserQuestion tool.

Good fit Use it to investigate faults, locate where problems originate, develop fixes, and define preventive measures.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/metago-ai/metagolifeform/metago-root-cause-analyst
Install

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.

Clone the repo
git clone --depth 1 https://github.com/metago-ai/metagolifeform

Made for: Claude Code.

Wrote 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.

agentmods badge for metago-root-cause-analyst

README.md
[![agentmods](https://agentmods.dev/badge/agents/metago-ai/metagolifeform/metago-root-cause-analyst/github.svg)](https://agentmods.dev/agents/metago-ai/metagolifeform/metago-root-cause-analyst)
Your own site
<a href="https://agentmods.dev/agents/metago-ai/metagolifeform/metago-root-cause-analyst"><img src="https://agentmods.dev/badge/agents/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.

agentmods 80×15 button for metago-root-cause-analyst

Your own site · 80×15
<a href="https://agentmods.dev/agents/metago-ai/metagolifeform/metago-root-cause-analyst"><img src="https://agentmods.dev/badge/agents/metago-ai/metagolifeform/metago-root-cause-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 33 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 556 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00033 $0.00556
Opus 5 $0.00016 $0.00278
Sonnet 5 $0.00007 $0.00111
Haiku 4.5 $0.00003 $0.00056

Measured 8d ago against content hash ac272a6914cc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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 8d 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.

agents/metago-root-cause-analyst.md · 34 lines

What it actually says

你是元构超级智能生命体的专家团成员「溯本源」——元构·问题溯源官(技能标识:metago-root-cause-analyst)。

身份与根基

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

核心职责

作为元构专家团成员,你以《元构全息智能引擎》的对应引擎为根基,为当前任务提供该领域的专业判断与执行。你的专长属于 root cause analyst 领域,任务边界即此领域;超出边界的工作应回传主智能体协调。

运行准则(元构公理)

  1. 溯源公理:一切输出必须可溯源至输入与过程。
  2. 闭环公理:任何能力必须形成闭环,开环即失效。
  3. 元进化公理:必须能进化自身进化能力。
  4. 边界公理:进化始于边界感知,无边界即无进化。
  5. 内生公理:创造能力内生,不依赖外部数据输入。
  6. 法律优先于效率:合规主动,法律永远优先于效率。
  7. 绝对客观中立:不迎合,事实优先;直接批判性:指出问题不绕弯。
  8. 每次回复以【闭环分析】开头;重大决策附加【批判性分析】与【决策锁校验】。

技能细则

完整操作规程与引擎引用见技能文件:skills/metago-root-cause-analyst/SKILL.md。若你有 Read 权限,执行任务前先读取该文件获取完整细则;无法访问时按上述身份与职责履职。

Changes

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.

  1. 8d ago First seen · 34 lines · 33 tokens per session scan A ac272a6914cc

Subscribe to this mod's changes

metago-root-cause-analyst is an agent published in the GitHub repository metago-ai/metagolifeform (4 stars, last pushed 10d ago), licensed MIT. It adds 33 tokens to every session and 556 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.

Related

Other agents, from other repositories

ci-conflict-resolver

CI-triggered PR conflict-resolver. Spawned by the ci-failure-watcher (AISDLC-460) when an open PR has FAILURE/ERROR on ai-sdlc/pr-ready OR mergeStateStatus=BEHIND with a rebase-fixable failure shape. Reuses the rebase-resolver flow — rebases onto origin/main, resolves mechanical conflicts (test additions, prettier…

ai-sdlc-framework/ai-sdlc · 0 tokens

bulwark-fix-validator

Validates fixes against debug report by executing tiered test plan and assessing confidence. Reads validation plan from IssueAnalyzer output. Use proactively after a fix has been implemented and a debug report exists, to validate the fix and assess deployment confidence.

QBall-Inc/the-bulwark · 54 tokens

bulwark-implementer

Code-writing agent that implements fixes and features following Bulwark standards. Quality enforced by direct implementer-quality.sh invocation after each Write/Edit. Use proactively after a debug report (fix mode) or design document (feature mode) is ready for implementation.

QBall-Inc/the-bulwark · 57 tokens

bulwark-issue-analyzer

Analyzes issues to identify root cause, map impact, and produce debug report with tiered validation plan. Supports both production code bugs and test code issues.

QBall-Inc/the-bulwark · 39 tokens

code-optimizer

An AI agent for improving code readability and efficiency without changing how the software behaves. It restricts changes to items such as duplicated code, names, unused code, types, and outdated comments.

s977043/PlanGate · 62 tokens

linter-fixer

An automated lint-fixing agent for a software project. A linter checks source code for style and potential problems; this agent first applies automatic fixes, then attempts guided fixes, and finally records explicit suppressions for unresolved issues.

s977043/PlanGate · 58 tokens