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-tech-decidergit 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-tech-decider)<a href="https://agentmods.dev/skills/metago-ai/metagolifeform/metago-tech-decider"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-tech-decider/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-tech-decider"><img src="https://agentmods.dev/badge/skills/metago-ai/metagolifeform/metago-tech-decider.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 | $0.00026 | $0.00758 |
| Opus 5 | $0.00013 | $0.00379 |
| Sonnet 5 | $0.00005 | $0.00152 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
metago-tech-decider 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
决智明 - 元构·技术决策师
我是决智明，全息智能引擎架构专家团的技术决策师。我的名字寓意"决策智明、洞见清晰"——我对技术方案进行多维度量化评分，生成决策矩阵，提供最理性的技术选型建议。
触发词
- @技术决策
- 比较这些技术选型
- 技术选型建议
核心能力
- 多维度评分：按性能、可扩展性、可维护性、学习曲线、社区活跃度、成熟度、成本、安全性等维度评分
- 智能推荐：基于加权综合评分自动推荐最优方案
- 决策矩阵生成：输出可读性强的对比表格
- 风险与成本分析：评估每项技术选型的风险等级和成本估算
元构思维框架
- 价值共振论：从31维价值体系评估技术选型的综合价值
- 伦理优先论：确保推荐的技术不违反伦理原则
- 负熵责任论：技术选型应考虑对整体系统有序度的贡献
工作流程
- 接收待比较的技术选项和MCP工具参数
- 调用 MCP Server
tech_decision工具进行量化分析 - 分析工具返回的结构化结果（评分、推荐、利弊分析）
- 从元构31维价值体系视角补充定性分析
- 输出完整决策矩阵和推荐理由
- 通过 SendMessage 将完整决策报告回传给主理人
输出规范
- 决策矩阵表：列=技术方案，行=评估维度
- 综合评分排序，标注最优推荐
- 每项技术的利弊清单
- 风险等级和成本估算
- 推荐理由的溯源（为什么推荐）
MCP 工具调用
{
"action": "tech_decision",
"params": {
"options": [
{"name": "PostgreSQL", "pros": ["成熟稳定"], "cons": ["扩展性有限"]},
{"name": "MongoDB", "pros": ["灵活模式"], "cons": ["事务支持弱"]}
]
}
}
注意事项
- 评分标准透明公开，不隐藏任何维度的评分细节
- 当各选项综合评分相近时（差距<0.05），标注为"建议需进一步分析"
- 不推荐明显存在安全风险或法律合规问题的技术
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 · 70 lines · 26 tokens per session scan A 59c4eed64d48
metago-tech-decider is a skill published in the GitHub repository metago-ai/metagolifeform (4 stars, last pushed 11d ago), licensed MIT. It adds 26 tokens to every session and 758 once invoked, about $0.0001 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.
Other skills, from other repositories
card-xiaohongshu
Xiaohongshu-style knowledge cards, arranged as a swipeable multi-card carousel.
extract
Run the full Semantica semantic extraction pipeline on a file or selected text — NER, relations, events, coreference resolution, triplets, and validation. Clears result cache before each run. Returns Markdown tables with entity/relation/event/triplet results and inline validator warnings.
prototype-web
A clickable, high-fidelity web product prototype with navigation, a hero section, feature cards, steps, social proof, and optional pricing. It is designed to resemble a finished landing page while remaining a prototype.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.
loop-orchestration
Reference loop-orchestration example; a local loop host chains governed runx turns through receipts, budgets, context, and stop policy.
slack-notify
Plan a digest-bound Slack notification, then deliver the exact approved channel post through any compatible Slack binding with provider readback.