usd-reserve-loss-gold-revaluation

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<essential_principles>

<principle name="gold_anchor_hypothesis"> **黃金錨定假說**

本模型基於極端情境假設：若法定貨幣體系瓦解、黃金成為唯一錨定資產，則：

隱含金價 = 貨幣負債 ÷ 黃金儲備
這不是「預測」，而是「壓力測試」：資產負債表要撐得住需要多高的金價

典型論述來源：VanEck「$39k gold」分析（M0 + FX turnover 加權） </principle>

<principle name="two_aggregates"> **兩種貨幣口徑差異**

口徑	定義	隱含金價	解讀
M0 (Monetary Base)	央行直接負債（通貨 + 準備金）	~$39k	央行資產負債表壓力
M2 (Broad Money)	含銀行體系信用擴張	~$184k	全體信用體系壓力

關鍵洞察：兩者差距反映「信用乘數」的槓桿效應。 </principle>

<principle name="weighting_logic"> **加權方法的直覺**

方法	數據來源	直覺
fx_turnover	BIS 三年調查	外匯交易份額 ≈ 國際結算/儲備使用強度
reserve_share	IMF COFER	官方外匯儲備幣別佔比
equal	-	不考慮貨幣重要性差異
custom	用戶自訂	可配合特定情境分析

加權的直覺：份額越高的貨幣，在「重新錨定」時需吸收的負債壓力越大。 </principle>

<principle name="backing_ratio"> **黃金支撐率 (Backing Ratio)**

backing_ratio = (gold_oz × gold_spot) / money_base

解讀：

backing_ratio ≈ 3% → 黃金僅支撐 3% 的貨幣負債（高槓桿）
backing_ratio ≈ 60% → 黃金接近完全支撐（低槓桿）

貼文中「日本黃金只支撐約 3% 的 M0」即此概念。 </principle>

<principle name="data_access"> **資料取得方式**

本 skill 使用公開數據：

黃金儲備：World Gold Council / IMF IFS（tonnes）
貨幣量：各國央行 / FRED / IMF IFS（M0/M2）
FX Turnover：BIS Triennial Survey（每三年更新）
金價：Yahoo Finance / FRED（XAU/USD）

腳本位於

scripts/

目錄，可直接執行。 </principle>

</essential_principles>

<objective> 實作「美元失去儲備地位下的黃金重估」壓力測試模型：

數據整合：抓取各國 M0/M2、黃金儲備、匯率、FX turnover 權重
計算隱含金價：未加權與加權版本
計算黃金支撐率：衡量各國槓桿程度
計算缺口：需要再買多少黃金才能達到目標支撐率
排名輸出：誰最槓桿、誰最穩健

輸出：隱含金價、支撐率排名、缺口分析、敘事洞察。 </objective>

<quick_start>

最快的方式：執行預設情境分析

bash

cd skills/usd-reserve-loss-gold-revaluation
pip install pandas numpy requests yfinance  # 首次使用
python scripts/gold_revaluation.py --quick

輸出範例：

json

{
  "headline": {
    "implied_gold_price_m0_weighted": 39210.0,
    "implied_gold_price_m2_weighted": 184500.0,
    "interpretation": "壓力測試數字，非價格預測"
  },
  "ranking": [
    {"entity": "JPY", "backing_ratio": 0.03, "lever_multiple": 41.0},
    {"entity": "USD", "backing_ratio": 0.08, "lever_multiple": 12.5},
    {"entity": "ZAR", "backing_ratio": 0.60, "lever_multiple": 0.16}
  ]
}

完整情境分析：

bash

python scripts/gold_revaluation.py \
  --date 2026-01-07 \
  --entities USD,CNY,JPY,EUR,GBP \
  --aggregate M0 \
  --weighting fx_turnover \
  --output result.json

</quick_start>

<intake> 需要進行什麼操作？

快速計算 - 使用預設參數計算主要貨幣的隱含金價
完整分析 - 自訂參數進行情境分析（可選擇口徑、權重、實體）
比較分析 - 同時比較 M0 vs M2、不同加權方法的差異
監控模式 - 追蹤黃金支撐率的變化趨勢
方法論學習 - 了解計算邏輯與數據來源
視覺化圖表 - 生成分析結果的視覺化圖表

請選擇或直接提供分析參數。 </intake>

<routing> | Response | Action | |---------------------------------|-----------------------------------------------| | 1, "快速", "quick", "計算" | 執行 `python scripts/gold_revaluation.py --quick` | | 2, "完整", "full", "分析" | 閱讀 `workflows/analyze.md` 並執行 | | 3, "比較", "compare", "對比" | 閱讀 `workflows/compare.md` 並執行 | | 4, "監控", "monitor", "追蹤" | 閱讀 `workflows/monitor.md` 並執行 | | 5, "學習", "方法論", "why" | 閱讀 `references/methodology.md` | | 6, "圖表", "畫圖", "視覺化" | 執行 `python scripts/visualize_revaluation.py` | | 提供參數 (如實體清單) | 閱讀 `workflows/analyze.md` 並使用參數執行 |

路由後，閱讀對應文件並執行。 </routing>

<directory_structure>

usd-reserve-loss-gold-revaluation/
├── SKILL.md                           # 本文件（路由器）
├── skill.yaml                         # 前端展示元數據
├── manifest.json                      # 技能元數據
├── workflows/
│   ├── analyze.md                     # 完整情境分析工作流
│   ├── compare.md                     # M0/M2 比較分析工作流
│   └── monitor.md                     # 持續監控工作流
├── references/
│   ├── data-sources.md                # 數據來源與獲取方式
│   ├── methodology.md                 # 方法論與計算邏輯
│   └── input-schema.md                # 完整輸入參數定義
├── templates/
│   ├── output-json.md                 # JSON 輸出模板
│   └── output-markdown.md             # Markdown 報告模板
└── scripts/
    ├── gold_revaluation.py            # 主計算腳本
    └── visualize_revaluation.py       # 視覺化腳本

</directory_structure>

<reference_index>

方法論: references/methodology.md

黃金錨定假說解析
隱含金價計算公式
支撐率與槓桿解讀

資料來源: references/data-sources.md

黃金儲備數據來源
貨幣量 M0/M2 數據來源
BIS FX Turnover 數據
匯率與金價來源

輸入參數: references/input-schema.md

完整參數定義
預設值與建議範圍

</reference_index>

<workflows_index>

Workflow	Purpose	使用時機
analyze.md	完整情境分析	需要自訂參數進行壓力測試
compare.md	M0/M2 比較分析	比較不同口徑的隱含金價差異
monitor.md	持續監控	追蹤支撐率變化趨勢
</workflows_index>

<templates_index>

Template	Purpose
output-json.md	JSON 輸出結構定義
output-markdown.md	Markdown 報告模板
</templates_index>

<scripts_index>

Script	Command	Purpose
gold_revaluation.py	`--quick`	快速計算主要貨幣
gold_revaluation.py	`--entities USD,CNY --agg M0`	自訂實體與口徑
gold_revaluation.py	`--compare-aggregates`	M0 vs M2 比較
visualize_revaluation.py	`--mode usd`	美元單一視覺化圖表
visualize_revaluation.py	`--mode multi`	多貨幣比較視覺化圖表
visualize_revaluation.py	`--mode all --output-dir DIR`	生成所有圖表至指定目錄
</scripts_index>

<input_schema_summary>

核心參數

參數	類型	預設值	說明
scenario_date	string	today	情境估算基準日期
entities	array	主要貨幣	分析對象（國家/貨幣代碼）
monetary_aggregate	string	M0	貨幣口徑（M0/M2/MB/M1/M3）
weighting_method	string	fx_turnover	加權方式
fx_base	string	USD	計價幣別基準

進階參數

參數	類型	預設值	說明
liability_scope	string	broad_money	負債口徑
gold_reserve_unit	string	troy_oz	黃金單位（oz/tonnes）
gold_price_spot	float	auto	基準日金價（可自動抓取）
fx_rates	object	auto	匯率（可自動抓取）

完整參數定義見

references/input-schema.md

。

</input_schema_summary>

<output_schema_summary>

json

{
  "skill": "usd-reserve-loss-gold-revaluation",
  "scenario_date": "2026-01-07",
  "assumptions": {
    "monetary_aggregate": "M0",
    "weighting_method": "fx_turnover",
    "fx_base": "USD",
    "gold_spot_usd_per_oz": 2050.0
  },
  "headline": {
    "implied_gold_price_weighted_usd_per_oz": 39210.0,
    "interpretation": "資產負債表壓力測算（非價格預測）"
  },
  "table": [...],
  "insights": [...]
}

完整輸出結構見

templates/output-json.md

。 </output_schema_summary>

<success_criteria> 執行成功時應產出：

隱含金價（未加權與加權版本）
各實體的黃金支撐率（backing_ratio）
槓桿倍數排名（lever_multiple_vs_spot）
黃金缺口分析（additional_gold_oz_needed）
敘事洞察（M0 vs M2 差異、槓桿解讀）
結果輸出為指定格式（JSON 或 Markdown）
視覺化圖表（可選，包含金價比較、支撐率、信用乘數等 6 個面板） </success_criteria>

<essential_principles>

<principle name="gold_anchor_hypothesis"> **Gold Anchor Hypothesis**

This model is based on an extreme scenario assumption: if the fiat currency system collapses and gold becomes the only anchor asset, then:

Implied Gold Price = Monetary Liabilities ÷ Gold Reserves
This is not a "prediction", but a "stress test": how high a gold price the balance sheet can withstand

Typical source of argument: VanEck's "$39k gold" analysis (weighted by M0 + FX turnover) </principle>

<principle name="two_aggregates"> **Differences Between Two Monetary Aggregates**

Aggregate	Definition	Implied Gold Price	Interpretation
M0 (Monetary Base)	Direct liabilities of the central bank (currency + reserves)	~$39k	Balance sheet pressure on central banks
M2 (Broad Money)	Includes credit expansion by the banking system	~$184k	Pressure on the entire credit system

Key Insight: The gap between the two reflects the leverage effect of the "credit multiplier". </principle>

<principle name="weighting_logic"> **Intuition Behind Weighting Methods**

Method	Data Source	Intuition
fx_turnover	BIS Triennial Survey	FX trading share ≈ intensity of international settlement/reserve usage
reserve_share	IMF COFER	Share of official foreign exchange reserves by currency
equal	-	Ignore differences in currency importance
custom	User-defined	Can be used with specific scenario analysis

Intuition for weighting: Currencies with higher shares need to absorb more liability pressure during "re-anchoring". </principle>

<principle name="backing_ratio"> **Gold Backing Ratio**

backing_ratio = (gold_oz × gold_spot) / money_base

Interpretation:

backing_ratio ≈ 3% → Gold only supports 3% of monetary liabilities (high leverage)
backing_ratio ≈ 60% → Gold is close to full support (low leverage)

The statement in the post that "Japan's gold only supports about 3% of M0" refers to this concept. </principle>

<principle name="data_access"> **Data Acquisition Methods**

This skill uses public data:

Gold Reserves: World Gold Council / IMF IFS (tonnes)
Monetary Aggregates: Central banks of various countries / FRED / IMF IFS (M0/M2)
FX Turnover: BIS Triennial Survey (updated every three years)
Gold Price: Yahoo Finance / FRED (XAU/USD)

The scripts are located in the

scripts/

directory and can be run directly. </principle>

</essential_principles>

<objective> Implement a stress test model for "gold revaluation under US dollar reserve status loss":

Data Integration: Capture M0/M2, gold reserves, exchange rates, and FX turnover weights of various countries
Calculate Implied Gold Price: Unweighted and weighted versions
Calculate Gold Backing Ratio: Measure the leverage level of each country
Calculate Gap: How much more gold needs to be purchased to reach the target backing ratio
Output Ranking: Which entities are the most leveraged and which are the most stable

Output: Implied gold price, backing ratio ranking, gap analysis, narrative insights. </objective>

<quick_start>

Fastest Way: Run Default Scenario Analysis

bash

cd skills/usd-reserve-loss-gold-revaluation
pip install pandas numpy requests yfinance  # First-time use
python scripts/gold_revaluation.py --quick

Sample Output:

json

{
  "headline": {
    "implied_gold_price_m0_weighted": 39210.0,
    "implied_gold_price_m2_weighted": 184500.0,
    "interpretation": "Stress test figure, not a price prediction"
  },
  "ranking": [
    {"entity": "JPY", "backing_ratio": 0.03, "lever_multiple": 41.0},
    {"entity": "USD", "backing_ratio": 0.08, "lever_multiple": 12.5},
    {"entity": "ZAR", "backing_ratio": 0.60, "lever_multiple": 0.16}
  ]
}

Full Scenario Analysis:

bash

python scripts/gold_revaluation.py \
  --date 2026-01-07 \
  --entities USD,CNY,JPY,EUR,GBP \
  --aggregate M0 \
  --weighting fx_turnover \
  --output result.json

</quick_start>

<intake> What operation do you need to perform?

Quick Calculation - Calculate implied gold prices of major currencies using default parameters
Full Analysis - Customize parameters for scenario analysis (select aggregates, weights, entities)
Comparative Analysis - Compare differences between M0 vs M2, different weighting methods
Monitoring Mode - Track changes in gold backing ratio trends
Methodology Learning - Understand calculation logic and data sources
Visualization Charts - Generate visualization charts of analysis results

Please select or directly provide analysis parameters. </intake>

<routing> | Response | Action | |---------------------------------|-----------------------------------------------| | 1, "快速", "quick", "計算" | Execute `python scripts/gold_revaluation.py --quick` | | 2, "完整", "full", "分析" | Read `workflows/analyze.md` and execute | | 3, "比較", "compare", "對比" | Read `workflows/compare.md` and execute | | 4, "監控", "monitor", "追蹤" | Read `workflows/monitor.md` and execute | | 5, "學習", "方法論", "why" | Read `references/methodology.md` | | 6, "圖表", "畫圖", "visualize" | Execute `python scripts/visualize_revaluation.py` | | Provide parameters (e.g., entity list) | Read `workflows/analyze.md` and execute with parameters |

After routing, read the corresponding file and execute. </routing>

<directory_structure>

usd-reserve-loss-gold-revaluation/
├── SKILL.md                           # This file (router)
├── skill.yaml                         # Frontend display metadata
├── manifest.json                      # Skill metadata
├── workflows/
│   ├── analyze.md                     # Full scenario analysis workflow
│   ├── compare.md                     # M0/M2 comparative analysis workflow
│   └── monitor.md                     # Continuous monitoring workflow
├── references/
│   ├── data-sources.md                # Data sources and acquisition methods
│   ├── methodology.md                 # Methodology and calculation logic
│   └── input-schema.md                # Complete input parameter definitions
├── templates/
│   ├── output-json.md                 # JSON output template
│   └── output-markdown.md             # Markdown report template
└── scripts/
    ├── gold_revaluation.py            # Main calculation script
    └── visualize_revaluation.py       # Visualization script

</directory_structure>

<reference_index>

Methodology: references/methodology.md

Analysis of Gold Anchor Hypothesis
Calculation formula for implied gold price
Interpretation of backing ratio and leverage

Data Sources: references/data-sources.md

Gold reserve data sources
Monetary aggregate M0/M2 data sources
BIS FX Turnover data
Exchange rate and gold price sources

Input Parameters: references/input-schema.md

Complete parameter definitions
Default values and recommended ranges

</reference_index>

<workflows_index>

Workflow	Purpose	Use Case
analyze.md	Full scenario analysis	Need to customize parameters for stress testing
compare.md	M0/M2 comparative analysis	Compare differences in implied gold prices across aggregates
monitor.md	Continuous monitoring	Track trends in backing ratio changes
</workflows_index>

<templates_index>

Template	Purpose
output-json.md	JSON output structure definition
output-markdown.md	Markdown report template
</templates_index>

<scripts_index>

Script	Command	Purpose
gold_revaluation.py	`--quick`	Quick calculation for major currencies
gold_revaluation.py	`--entities USD,CNY --agg M0`	Custom entities and aggregates
gold_revaluation.py	`--compare-aggregates`	M0 vs M2 comparison
visualize_revaluation.py	`--mode usd`	USD single visualization chart
visualize_revaluation.py	`--mode multi`	Multi-currency comparison visualization chart
visualize_revaluation.py	`--mode all --output-dir DIR`	Generate all charts to specified directory
</scripts_index>

<input_schema_summary>

Core Parameters

Parameter	Type	Default Value	Description
scenario_date	string	today	Base date for scenario estimation
entities	array	Major currencies	Analysis targets (country/currency codes)
monetary_aggregate	string	M0	Monetary aggregate (M0/M2/MB/M1/M3)
weighting_method	string	fx_turnover	Weighting method
fx_base	string	USD	Base currency for pricing

Advanced Parameters

Parameter	Type	Default Value	Description
liability_scope	string	broad_money	Liability scope
gold_reserve_unit	string	troy_oz	Gold unit (oz/tonnes)
gold_price_spot	float	auto	Spot gold price on base date (can be automatically fetched)
fx_rates	object	auto	Exchange rates (can be automatically fetched)

Complete parameter definitions can be found in

references/input-schema.md

</input_schema_summary>

<output_schema_summary>

json

{
  "skill": "usd-reserve-loss-gold-revaluation",
  "scenario_date": "2026-01-07",
  "assumptions": {
    "monetary_aggregate": "M0",
    "weighting_method": "fx_turnover",
    "fx_base": "USD",
    "gold_spot_usd_per_oz": 2050.0
  },
  "headline": {
    "implied_gold_price_weighted_usd_per_oz": 39210.0,
    "interpretation": "Balance sheet stress test figure (not a price prediction)"
  },
  "table": [...],
  "insights": [...]
}

Complete output structure can be found in

templates/output-json.md

. </output_schema_summary>

<success_criteria> When execution is successful, the following should be produced:

Implied gold price (unweighted and weighted versions)
Gold backing ratio (backing_ratio) for each entity
Leverage multiple ranking (lever_multiple_vs_spot)
Gold gap analysis (additional_gold_oz_needed)
Narrative insights (differences between M0 vs M2, leverage interpretation)
Results output in specified format (JSON or Markdown)
Visualization charts (optional, including 6 panels such as gold price comparison, backing ratio, credit multiplier, etc.) </success_criteria>