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 duolongworld/AI_Renaissance --skill layer2_5_hub_variablegit clone --depth 1 https://github.com/duolongworld/AI_RenaissanceWrote 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/duolongworld/ai_renaissance/layer2_5_hub_variable)<a href="https://agentmods.dev/skills/duolongworld/ai_renaissance/layer2_5_hub_variable"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/layer2_5_hub_variable/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/duolongworld/ai_renaissance/layer2_5_hub_variable"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/layer2_5_hub_variable.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.01369 |
| Opus 5.5 | $0.00014 | $0.00548 |
| Sonnet 5.5 | $0.00007 | $0.00274 |
| Haiku 4.5 | $0.00004 | $0.00137 |
Grade A, and why
layer2_5_hub_variable 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.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Layer 2.5: 枢纽变量分析——汇率与大宗商品传导
执行方式
- 数值计算
- 智能分析（需要LLM解读汇率驱动因素、大宗商品信号含义）
- 混合模式
说明：本层需要 LLM 进行智能判断：
- 解读USD/CNH方向的多重驱动因素
- 判断大宗商品比值异常的宏观含义
- 确认全球宏观三角定位
适用范围
分析汇率与大宗商品的枢纽传导作用，为 Layer 4 预期差信号引擎提供输入。
输入数据规范
必填数据
| 数据项 | 来源 | 字段名 | 说明 |
|---|---|---|---|
| USD/CNH即期汇率 | HKMA | usd_cnh | 日频 |
| 中美10Y利差 | 自算 | cn_us_10y_spread | 日频 |
| 中国贸易顺差 | 海关 | trade_surplus | 月频 |
| CNH-CNY价差 | HKMA | cnh_cny_spread | 日频 |
| 外储月度变化 | SAFE | forex_reserve_change | 月频 |
| LME铜价 | Bloomberg | copper_price | 日频 |
| COMEX黄金价格 | Bloomberg | gold_price | 日频 |
| DCE铁矿石价格 | DCE | iron_ore_price | 日频 |
| CBOT大豆/玉米价格 | CBOT | soybean_corn_ratio | 日频 |
可选数据
| 数据项 | 来源 | 用途 |
|---|---|---|
| USD/CNH 1Y远期点 | HKMA | 人民币贬值预期 |
| 人民币期权波动率微笑 | Bloomberg | 尾部风险 |
| WTI原油价格 | Bloomberg | 通胀预期 |
| 央行中间价偏离度 | PBOC | 政策意图 |
分析步骤
子模块A: USD/CNH汇率分析
驱动力权重：
- 利差驱动 0.30：中美10Y利差
- 经常账户 0.20：中国贸易顺差
- 风险偏好 0.20：VIX、CNH波动率
- 政策意图 0.30：中间价、外储变化
方向判定：
- 得分 > +1.0σ：升值趋势确认
- 得分 < -1.0σ：贬值趋势确认
- 得分在±0.5σ之间：震荡/无方向
子模块B: 大宗商品比值信号
| # | 比值 | 计算方式 | 宏观含义 |
|---|---|---|---|
| 1 | 铜金比 | LME铜/现货黄金 | 铜金比上升=全球增长乐观 |
| 2 | 油金比 | 布伦特/现货黄金 | 油金比上升=通胀压力上行 |
| 3 | 铁矿石/铜比 | DCE铁矿石/LME铜 | 比值上升=中国地产/基建相对更强 |
| 4 | 大豆/玉米比 | CBOT大豆/CBOT玉米 | 比值异常=供给冲击或天气风险 |
| 5 | 黄金vs实际利率 | 现货黄金/10Y TIPS收益率 | 背离=避险/去美元化 |
| 6 | 南华vs全球PMI | 南华工业品指数同比/全球PMI | 背离=中国独立定价逻辑 |
子模块C: 全球宏观三角
| 宏观环境 | 美元 | 大宗 | 美债收益率 | 最优资产 |
|---|---|---|---|---|
| 全球紧缩 | 强 | 弱 | 高 | 中债长端、防御股 |
| 全球宽松 | 弱 | 强 | 低 | 周期股、大宗股、港股 |
| 滞胀型 | 强 | 强 | 高 | 黄金、短债 |
| 通缩型 | 弱 | 弱 | 低 | 利率债、高股息 |
标准输出
{
"layer_name": "layer2_5",
"timestamp": "2026-05-15T00:00:00",
"analysis_result": {
"cnh_direction": {
"score": 0.8,
"direction": "偏升值",
"components": {
"rate_score": 0.3,
"ca_score": -0.2,
"risk_score": 0.1,
"policy_score": 0.6
}
},
"commodity_signals": [
{"id": 1, "name": "铜金比", "z_score": 1.2, "macro_meaning": "全球工业需求偏强"}
],
"macro_triangle": {
"triangle": "global_easing",
"usd_strength": -1,
"commodity_score": 1,
"us_rate_level": -1,
"best_assets": ["周期股", "大宗股", "港股"]
},
"channel_alerts": [
{"channel_id": 2, "status": "active", "direction": "positive"}
]
},
"direction": "bullish",
"confidence": 0.7,
"reasoning": "全球宽松三角确认，美元弱势+大宗强势"
}
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 127 lines · 36 tokens per session scan A f69416bcae8a
layer2_5_hub_variable is a skill published in the GitHub repository duolongworld/AI_Renaissance (61 stars, last pushed 22d ago), licensed Apache-2.0. It adds 36 tokens to every session and 1,369 once invoked, about $0.0001 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 2026-08-30.
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