Agent skill

analyze-silver-miner-metal-ratio

以「銀礦股價格 ÷ 白銀價格」的相對比率衡量礦業股板塊相對於金屬本體的估值區間(偏貴/偏便宜),並用歷史分位數與類比區間推導「底部/頂部」訊號與情境推演。

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill analyze-silver-miner-metal-ratio --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 9 KB
Bundled scripts: none
Path: skills/analysis/analyze-silver-miner-metal-ratio/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

<essential_principles> <principle name="ratio_definition"> **比率定義與意義** 礦業股/金屬比率(Miner-to-Metal Ratio): ``` ratio_t = miner_price_t / metal_price_t ``` 其中: - **miner_price**:銀礦股代表(ETF 如 SIL/SILJ,或自建礦業股指數) - **metal_price**:白銀價格(期貨 SI=F、現貨 XAGUSD、ETF SLV) 此比率衡量「礦業股相對於金屬本體」的估值水位: - **比率高**:礦業股相對白銀偏貴(可能過度樂觀、槓桿溢價高) - **比率低**:礦業股相對白銀偏便宜(可能被低估、或反映成本/股權稀釋風險) </principle> <principle name="quantile_interpretation"> **分位數解讀邏輯** 使用歷史分位數(Percentile Rank)判斷當前比率位置: | 分位數區間 | 標籤 | 直覺 | |------------|----------------|----------------------------| | ≤ 20% | bottom (底部) | 礦業股相對白銀歷史上很便宜 | | 20-40% | low (偏低) | 礦業股相對估值偏低 | | 40-60% | neutral (中性) | 歷史中位區間 | | 60-80% | high (偏高) | 礦業股相對估值偏高 | | ≥ 80% | top (頂部) | 礦業股相對白銀歷史上很貴 | **底部區間不等於白銀必漲**:可能是礦業股因成本/稀釋被合理定價。 </principle> <principle name="divergence_signal"> **背離訊號的意義** 當出現「比率低 + 白銀高」的組合: - **比率處於底部區**:礦業股相對白銀偏便宜 - **白銀處於高位**:金屬價格已在歷史高檔 此「背離」意味著: 1. 礦業股可能有追趕空間(均值回歸邏輯) 2. 或礦業股正確反映了結構性問題(成本、稀釋、地緣風險) 需結合基本面交叉驗證,而非盲目視為買入訊號。 </principle> <principle name="scenario_math"> **情境推演計算** 目標:若比率要回到歷史頂部(或中位),需要什麼條件? 假設當前比率 = 1.14,目標比率(頂部門檻)= 2.45: **情境 A:白銀不變,礦業股需漲多少?** ``` miner_multiplier = target_ratio / current_ratio = 2.45 / 1.14 = 2.15x (需漲 115%) ``` **情境 B:

What's inside
Ships with 1 file
  • metadata.json
Commands it runs
cd skills/analyze-silver-miner-metal-ratio
pip install pandas numpy yfinance matplotlib  # 首次使用
python scripts/ratio_analyzer.py --quick
python scripts/ratio_plotter.py --quick --output-dir ../../output
python scripts/ratio_plotter.py --comprehensive --start-date 2010-01-01 --output-dir ../../output
python scripts/ratio_analyzer.py \
More from claude-skill-registry
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About this skill
What does the analyze-silver-miner-metal-ratio skill do?

以「銀礦股價格 ÷ 白銀價格」的相對比率衡量礦業股板塊相對於金屬本體的估值區間(偏貴/偏便宜),並用歷史分位數與類比區間推導「底部/頂部」訊號與情境推演。

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill analyze-silver-miner-metal-ratio --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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