usd-reserve-loss-gold-revaluation

<essential_principles>

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

Implied gold price = monetary liabilities ÷ gold reserves
This is not a "prediction" but a "stress test": how high the gold price must be for the balance sheet to hold up

Typical source of the argument: VanEck "$39k gold" analysis (M0 + FX turnover weighted)

Basis	Definition	Implied gold price	Interpretation
M0 (Monetary Base)	Direct central bank liabilities (currency + reserves)	~$39k	Central bank balance sheet stress
M2 (Broad Money)	Includes banking system credit expansion	~$184k	Stress on the entire credit system

Key insight: The gap between the two reflects the leverage effect of the "credit multiplier."

Method	Data source	Intuition
fx_turnover	BIS Triennial Survey	FX trading share ≈ intensity of international settlement/reserve use
reserve_share	IMF COFER	Currency composition of official foreign exchange reserves
equal	-	Does not account for differences in currency importance
custom	User-defined	Can be adapted for specific scenario analysis

Intuition behind weighting: the higher a currency's share, the greater the debt pressure it must absorb during "re-anchoring."

backing_ratio = (gold_oz × gold_spot) / money_base

Interpretation:

backing_ratio ≈ 3% → gold backs only 3% of monetary liabilities (high leverage)
backing_ratio ≈ 60% → gold nearly fully backs it (low leverage)

The post's statement that "Japan's gold backs only about 3% of M0" refers to this concept.

This skill usespublic data：

Gold reserves：World Gold Council / IMF IFS（tonnes）
Money supply: 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 script is located in the scripts/ directory and can be run directly.

</essential_principles>

Data integration: fetch each country's M0/M2, gold reserves, exchange rates, and FX turnover weights
Calculate implied gold price: unweighted and weighted versions
Calculate gold backing ratio: measure each country's leverage level
Calculate gap: how much more gold needs to be bought to reach the target backing ratio
Ranking output: who is most leveraged, who is most robust

Output: implied gold price, backing ratio rankings, gap analysis, narrative insights.

<quick_start>

Fastest way: run the preset scenario analysis

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

Example output:

{
  "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}
  ]
}

Full scenario analysis：

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>

Quick calculation - Calculate the implied gold price of major currencies using preset parameters
Full analysis - Conduct scenario analysis with custom parameters (can choose basis, weighting, entity)
Comparative analysis - Compare M0 vs M2 and the differences between weighting methods at the same time
Monitoring mode - Track trends in the gold backing ratio
Methodology learning - Understand the calculation logic and data sources
Visualization charts - Generate visualization charts of the analysis results

Please select or directly provide analysis parameters.

After routing, read the corresponding document and execute.

<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>

Methodology: references/methodology.md

Golden anchor hypothesis analysis
Implied gold price calculation formula
Backing ratio and leverage interpretation

Data sources: references/data-sources.md

Gold reserve data sources
Money supply 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	When to use
analyze.md	Complete scenario analysis	Need custom parameters for stress testing
compare.md	M0/M2 comparative analysis	Compare the differences in implied gold price under different measures
monitor.md	Continuous monitoring	Track the trend of 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 measures
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 charts
visualize_revaluation.py	`--mode all --output-dir DIR`	Generate all charts to the specified directory
</scripts_index>

<input_schema_summary>

Core parameters

Parameter	Type	Default value	Description
scenario_date	string	today	Scenario estimation base date
entities	array	Major currencies	Analysis target (country/currency code)
monetary_aggregate	string	M0	Money measure (M0/M2/MB/M1/M3)
weighting_method	string	fx_turnover	Weighting method
fx_base	string	USD	Denomination currency base

Advanced parameters

Parameter	Type	Default value	Description
liability_scope	string	broad_money	Liability measure
gold_reserve_unit	string	troy_oz	Gold unit (oz/tonnes)
gold_price_spot	float	auto	Base date gold price (can be fetched automatically)
fx_rates	object	auto	Exchange rate (can be fetched automatically)

For complete parameter definitions, see references/input-schema.md。

</input_schema_summary>

<output_schema_summary>

{
  "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": [...]
}

For the complete output structure, see templates/output-json.md。 </output_schema_summary>

<success_criteria> When execution succeeds, it should produce:

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

About this skill