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-decision-evalgit 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-decision-eval)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-decision-eval"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-decision-eval/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-decision-eval"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-decision-eval.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 | $0.00531 |
| Opus 5 | $0.00031 | $0.00266 |
| Sonnet 5 | $0.00012 | $0.00106 |
| Haiku 4.5 | $0.00006 | $0.00053 |
Grade A, and why
metago-decision-eval 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 12d 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
决策评估（metago-decision-eval）
描述
评估决策质量和潜在风险。评估维度：逻辑完整性、证据充分性、风险可控性、替代方案、可执行性。返回0-100分及各维度评分。
触发条件
用户请求决策评估时自动激活。重大决策输出前自动触发（与决策锁协同）。
核心流程
- 接收决策内容
- 逻辑完整性评估
- 证据充分性评估
- 风险可控性评估
- 替代方案分析
- 可执行性评估
- 输出综合评分与建议
输出格式
返回结构化JSON，包含：
- 总评分（0-100）
- 各维度评分
- 风险点列表
- 改进建议
根源文档
元构全息智能引擎.txt（metago-decision-eval技能定义(241-245, 454-482)）
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-decision-lock协同：决策锁关卡1意图验证时调用 - 与
metago-whatif协同：替代方案分析联动反事实推演 - 与
metago-compliance协同：风险可控性包含合规维度
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.
- 12d ago First seen · 48 lines · 62 tokens per session scan A 729de4b1b057
metago-decision-eval is a skill published in the GitHub repository metago-ai/metagolifeform (4 stars, last pushed 11d ago), licensed MIT. It adds 62 tokens to every session and 531 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 2026-08-31.
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