compute-precious-miner-gross-margin

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Calculate the 'Gross Margin Proxy for Gold/Silver Mining Industry' using public metal prices and mining cost metrics (AISC/All-in Cost/Cash Cost), and determine whether the current level is in the historical high/low range.

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Sourcefatfingererr/macro-skills

Added on2026-02-09

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npx skill4agent add fatfingererr/macro-skills compute-precious-miner-gross-margin

SKILL.md Content (Chinese)

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<essential_principles>

<principle name="margin_proxy_definition"> **Gross Margin Proxy Definition**

The Mining Gross Margin Proxy uses a simplified formula:

gross_margin_proxy = (metal_price - unit_cost) / metal_price

Where:

metal_price: Spot price of the metal or near-month futures price
unit_cost: AISC (All-In Sustaining Cost), Cash Cost (C1), or All-In Cost

This indicator is not equivalent to the gross margin in financial statements, but can quickly capture marginal changes in the price-cost relationship. </principle>

<principle name="cost_metric_hierarchy"> **Cost Metric Hierarchy**

Metric	Included Items	Applicable Scenario
Cash Cost (C1)	On-site mining + processing + on-site administration	Cash flow stress test
AISC	C1 + sustaining capital expenditure + exploration + administration	Industry Standard (defined by WGC)
All-In Cost (AIC)	AISC + growth capital expenditure	Complete economic cost

It is recommended to prioritize AISC due to its best comparability and high data availability. </principle>

<principle name="aggregation_logic"> **Basket Aggregation Logic**

Method	Formula	Intuition
equal_weight	Σ margin_i / N	Simple average, equal weight for each company
production_weighted	Σ (margin_i × prod_i) / Σ prod_i	Production-weighted, reflects "industry gross margin"
marketcap_weighted	Σ (margin_i × mcap_i) / Σ mcap_i	Market cap-weighted, reflects "equity exposure"

It is recommended to use production_weighted to more accurately reflect the overall gross margin structure of the industry. </principle>

<principle name="data_frequency_alignment"> **Data Frequency Alignment**

Metal Prices: Daily frequency or monthly average price
Mining Costs: Quarterly (most companies only disclose AISC in quarterly reports)
Alignment Methods:
- Forward-fill costs to each period within the quarter
- Or use the same-quarter average price (cleaner)

This Skill recommends using quarterly frequency as the baseline to avoid over-smoothing. </principle>

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

This skill uses public data:

Gold Price: LBMA Gold Price / COMEX Near-Month Futures
Silver Price: LBMA Silver Price / COMEX Near-Month Futures
AISC: Company IR investor presentations / Quarterly Report MD&A / Press releases
Production Volume: Same as above, unit in oz / GEO / AgEq oz

Scripts are located in the

scripts/

directory and can be executed directly. </principle>

</essential_principles>

<objective> Implement the calculation model for the 'Precious Metals Mining Gross Margin Proxy':

Data Integration: Fetch metal price series and mining cost/production volume data
Gross Margin Proxy Calculation: Single company level + basket aggregation
Historical Percentiles: Determine the current level's position in the historical range
Driver Decomposition: Distinguish between price-driven vs cost-driven changes
Signal Generation: Mark extreme high/low range intervals

Output: Gross margin time series, historical percentiles, driver decomposition, trading/research links. </objective>

<quick_start>

Fastest Way: Execute Default Scenario Analysis

bash

cd skills/compute-precious-miner-gross-margin
pip install pandas numpy requests yfinance beautifulsoup4 lxml  # First-time use
python scripts/margin_calculator.py --quick --metal gold

Sample Output:

json

{
  "skill": "compute_precious_miner_margin_proxy",
  "metal": "gold",
  "frequency": "quarterly",
  "cost_metric": "AISC",
  "basket": {
    "miners": ["NEM", "GOLD", "AEM"],
    "aggregation": "production_weighted"
  },
  "latest": {
    "date": "2025-Q4",
    "metal_price_usd_oz": 2650.0,
    "unit_cost_proxy_usd_oz": 1320.0,
    "gross_margin_proxy": 0.502,
    "history_percentile": 0.78,
    "regime_label": "high_margin"
  }
}

Full Scenario Analysis:

bash

python scripts/margin_calculator.py \
  --metal silver \
  --miners CDE,HL,AG \
  --start-date 2015-01-01 \
  --frequency quarterly \
  --cost-metric AISC \
  --aggregation production_weighted \
  --output result.json

</quick_start>

<intake> What operation do you need to perform?

Quick Calculation - Calculate the gross margin proxy for major mining baskets using default parameters
Full Analysis - Perform scenario analysis with custom parameters (select metal, miners, cost metric)
Data Research - Learn how to obtain AISC / cost data (crawler design)
Signal Generation - Convert gross margin into trading/research signals
Methodology Learning - Understand calculation logic and data sources

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

<routing> | Response | Action | |-----------------------------|----------------------------------------------------| | 1, "quick", "fast", "calculate" | Execute `python scripts/margin_calculator.py --quick` | | 2, "full", "complete", "analyze" | Read `workflows/analyze.md` and execute | | 3, "data", "research", "crawler" | Read `workflows/data-research.md` | | 4, "signal", "trading", "generate" | Read `workflows/signal-generation.md` and execute | | 5, "learn", "methodology", "why" | Read `references/methodology.md` | | Provide parameters (e.g., miner list) | Read `workflows/analyze.md` and execute with parameters |

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

<directory_structure>

compute-precious-miner-gross-margin/
├── SKILL.md                           # This file (router)
├── skill.yaml                         # Frontend display metadata
├── manifest.json                      # Skill metadata
├── workflows/
│   ├── analyze.md                     # Full scenario analysis workflow
│   ├── data-research.md               # Data source research and crawler design
│   └── signal-generation.md           # Signal generation workflow
├── references/
│   ├── data-sources.md                # Data sources and acquisition methods
│   ├── methodology.md                 # Methodology and calculation logic
│   └── input-schema.md                # Complete input parameter definition
├── templates/
│   ├── output-json.md                 # JSON output template
│   └── output-markdown.md             # Markdown report template
├── scripts/
│   └── margin_calculator.py           # Main calculation script
└── examples/
    └── sample-output.json             # Sample output

</directory_structure>

<reference_index>

Methodology: references/methodology.md

Gross margin proxy definition
Cost metric hierarchy analysis
Aggregation methods and intuition
Historical percentile calculation

Data Sources: references/data-sources.md

Gold and silver price data sources
AISC / Cash Cost data sources
Production volume data sources
Crawler design guidelines

Input Parameters: references/input-schema.md

Complete parameter definition
Default values and recommended ranges
Default miner baskets

</reference_index>

<workflows_index>

Workflow	Purpose	Application Scenario
analyze.md	Full scenario analysis	Need to calculate gross margin with custom parameters
data-research.md	Data source research	Learn how to obtain cost data
signal-generation.md	Signal generation	Convert gross margin into trading/research signals
</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
margin_calculator.py	`--quick --metal gold`	Quick calculation for gold mining industry
margin_calculator.py	`--quick --metal silver`	Quick calculation for silver mining industry
margin_calculator.py	`--miners NEM,GOLD --freq Q`	Custom miners and frequency calculation
margin_calculator.py	`--decompose`	Driver decomposition analysis
</scripts_index>

<input_schema_summary>

Core Parameters

Parameter	Type	Default Value	Description
metal	string	gold	Target metal (gold/silver)
miners	array	Default basket	List of miner tickers
start_date	string	10 years ago	Calculation start date (YYYY-MM-DD)
end_date	string	today	Calculation end date
frequency	string	quarterly	Frequency (daily/weekly/monthly/quarterly)
cost_metric	string	AISC	Cost metric
aggregation	string	production_weighted	Aggregation method

Advanced Parameters

Parameter	Type	Default Value	Description
price_series	string	spot	Price metric
fx_mode	string	none	FX handling
outlier_rule	string	winsorize_1_99	Outlier processing
history_window_years	int	20	Historical percentile window

Complete parameter definition can be found in

references/input-schema.md

</input_schema_summary>

<output_schema_summary>

json

{
  "skill": "compute_precious_miner_margin_proxy",
  "metal": "silver",
  "frequency": "quarterly",
  "cost_metric": "AISC",
  "basket": {
    "miners": ["CDE", "HL", "AG"],
    "aggregation": "production_weighted"
  },
  "latest": {
    "date": "2025-Q4",
    "metal_price_usd_oz": 31.50,
    "unit_cost_proxy_usd_oz": 6.30,
    "gross_margin_proxy": 0.80,
    "history_percentile": 0.94,
    "regime_label": "extreme_high_margin"
  },
  "decomposition": {
    "last_3m_price_change_pct": 0.12,
    "last_3m_cost_change_pct": -0.03,
    "driver": "mostly_price_up"
  },
  "notes": [
    "gross_margin_proxy uses (price - AISC)/price as an approximation; not equivalent to the gross margin in financial statements.",
    "If cost data is quarterly, it has been aligned via forward-fill within the quarter / same-quarter average price."
  ],
  "recommended_next_checks": [
    "Compare 3/6/12-month forward returns of SIL/SILJ or individual stocks using the same margin proxy (event study)",
    "Check for rising capital expenditure/M&A activity, or cost re-inflation (diesel/wages/reagents) leading to gross margin decline"
  ]
}

Complete output structure can be found in

templates/output-json.md

. </output_schema_summary>

<success_criteria> Successful execution should produce:

Gross margin proxy time series data
Unit cost and gross margin for each miner
Basket aggregated gross margin
Historical percentiles and range labels (extreme_high/high/neutral/low/extreme_low)
Driver decomposition (price-driven vs cost-driven)
Results output in specified format (JSON or Markdown)
Follow-up research recommendations </success_criteria>

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SKILL.md Content (Chinese)