usd-reserve-loss-gold-revaluation

<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>

usd-reserve-loss-gold-revaluation

NPX Install

Tags

SKILL.md Content (Chinese)