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-emotiongit 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-emotion)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-emotion"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-emotion/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-emotion"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-emotion.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 | $0.00062 | $0.00514 |
| Opus 5.5 | $0.00025 | $0.00206 |
| Sonnet 5.5 | $0.00012 | $0.00103 |
| Haiku 4.5 | $0.00006 | $0.00051 |
Grade A, and why
metago-emotion 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 1mo 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
情绪检测（metago-emotion）
描述
通过分析用户输入文本特征检测情绪状态。检测维度：词汇情绪词库、否定词与程度词处理、标点符号特征、长度与重复特征。返回emotion_state/confidence/intensity。
触发条件
用户请求情绪检测时自动激活。用户输入中包含明显情绪倾向时自动激活。
核心流程
- 接收用户输入文本
- 词汇情绪词库匹配
- 否定词与程度词处理
- 标点符号特征分析
- 长度与重复特征分析
- 输出情绪状态评估结果
输出格式
返回结构化JSON，包含：
{
"emotion_state": "情绪状态",
"confidence": 0.0-1.0,
"intensity": "low|medium|high"
}
根源文档
元构全息智能引擎.txt（metago-emotion技能定义(223-227, 371-393)）
注：完整蓝本未随公开仓库发布，以上为出处留存备查；本技能正文已自包含全部操作定义，运行时无需读取蓝本。
与其他技能的协同
- 与
metago-scene-adapt协同：情绪状态影响场景适配 - 与
metago-objectivity协同：情绪不影响客观性判定
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.
- 1mo ago First seen · 49 lines · 62 tokens per session scan A 3ddb1bbcb4e1
metago-emotion is a skill published in the GitHub repository metago-ai/metagolifeform (5 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 514 once invoked, about $0.0002 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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