stress_score = 100 × min(1.0,
    0.6 × divergence_severity +      # Divergence severity
    0.2 × decade_low_bonus +         # Decade-low bonus
    0.2 × ratio_extreme_bonus        # Extreme ratio bonus
)

cd skills/monitor-etf-holdings-drawdown-risk
pip install pandas numpy yfinance selenium webdriver-manager beautifulsoup4 matplotlib  # First-time use
python scripts/divergence_detector.py --etf SLV --quick

{
  "asof": "2026-01-20",
  "divergence": false,
  "price_return_window": 1.92,
  "inventory_change_window": 0.15,
  "inventory_decade_low": false,
  "stress_score_0_100": 20.0,
  "interpretations": ["Physical Tightness", "ETF Flow Hypothesis"]
}

undefined

**Output**:
- JSON analysis result: `result.json`
- Visualization report: `output/SLV_divergence_report_20260120.png`
- PDF report: `output/SLV_divergence_report_20260120.pdf`

</quick_start>

<intake>
What operation do you need to perform?

1. **Quick Check** - View the current divergence status and stress score of the specified ETF
2. **Full Analysis** - Perform a complete historical divergence analysis
3. **Cross-Validation** - Verify the authenticity of divergence signals using multiple indicators
4. **Monitoring Mode** - Set up continuous monitoring and divergence alerts
5. **Methodology Learning** - Understand divergence detection and dual hypothesis logic

**Please select or directly provide analysis parameters (e.g., ETF ticker symbol).**
</intake>

<routing>
| Response                        | Action                                        |
|---------------------------------|-----------------------------------------------|
| 1, "快速", "quick", "check"     | Execute `scripts/divergence_detector.py --quick` |
| 2, "分析", "analyze", "full"    | Read `workflows/analyze.md` and execute       |
| 3, "驗證", "validate", "cross"  | Read `workflows/cross-validate.md` and execute|
| 4, "監控", "monitor", "alert"   | Read `workflows/monitor.md` and execute       |
| 5, "學習", "方法論", "why"      | Read `references/methodology.md`              |
| Provide ETF ticker (e.g., SLV, GLD)     | Read `workflows/analyze.md` and execute with parameters |

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

<directory_structure>

</directory_structure>

<reference_index>

**Methodology**: references/methodology.md
- Divergence detection logic
- Dual hypothesis validation framework
- Stress score calculation

**Data Sources**: references/data-sources.md
- ETF official website inventory scraping (Selenium)
- Yahoo Finance price data
- Cross-validation data sources (COMEX, LBMA)

**Input Parameters**: references/input-schema.md
- Complete parameter definition
- Default values and recommended ranges

</reference_index>

<workflows_index>
| Workflow          | Purpose          | Usage Scenario               |
|-------------------|------------------|------------------------|
| analyze.md        | Full Divergence Analysis     | When complete historical analysis is needed     |
| monitor.md        | Continuous Status Monitoring     | For daily monitoring or alerts         |
| cross-validate.md | Cross-Validate Divergence Signals | When verifying the authenticity of divergence       |
</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              |
|-------------------------|-----------------------------------------|----------------------|
| divergence_detector.py  | `--etf SLV --quick`                     | Quick Divergence Status Check     |
| divergence_detector.py  | `--start DATE --end DATE --output FILE` | Complete Historical Analysis         |
| visualize_divergence.py | `--result result.json --output DIR`     | Generate Visualization Report       |
| fetch_etf_holdings.py   | `--etf SLV --output holdings.csv`       | Scrape ETF Inventory        |
| fetch_prices.py         | `--symbol SI=F --output prices.csv`     | Scrape Commodity Prices         |
</scripts_index>

<input_schema_summary>

**Core Parameters**

| Parameter               | Type   | Default Value     | Description                       |
|------------------------|--------|------------|----------------------------|
| etf_ticker             | string | (Required)     | ETF/trust ticker symbol (e.g., SLV)     |
| commodity_price_symbol | string | (Required)     | Commodity price symbol (e.g., XAGUSD)  |
| start_date             | string | 10 years ago     | Analysis start date                 |
| end_date               | string | today      | Analysis end date                 |

**Divergence Parameters**

| Parameter                       | Type  | Default | Description                   |
|----------------------------|-------|--------|------------------------|
| divergence_window_days     | int   | 180    | Divergence calculation window (days)     |
| decade_low_window_days     | int   | 3650   | Decade-low calculation window (days)     |
| min_price_return_pct       | float | 0.15   | Price rise threshold           |
| min_inventory_drawdown_pct | float | 0.10   | Inventory decline threshold           |

Complete parameter definition can be found in `references/input-schema.md`.

</input_schema_summary>

<output_schema_summary>
```json
{
  "skill": "monitor-etf-holdings-drawdown-risk",
  "asof": "2026-01-16",
  "inputs": {
    "etf_ticker": "SLV",
    "commodity_price_symbol": "XAGUSD"
  },
  "result": {
    "divergence": true,
    "price_return_window": 0.32,
    "inventory_change_window": -0.18,
    "inventory_decade_low": true,
    "inventory_to_price_ratio_z": -2.4,
    "stress_score_0_100": 78.5
  },
  "interpretations": [...],
  "next_checks": [...]
}