Agent skill

backsolve-miner-vs-metal-ratio-with-fundamentals

從網路自動抓取礦業公司財務報表與營運揭露(產量、成本、資本支出),回算「礦業股/金屬本體比率」的基本面解釋與區間門檻(如 1.2/1.7),並輸出可重現的估值拆解(成本因子 / 槓桿因子 / 倍數因子 / 稀釋因子)。

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill backsolve-miner-vs-metal-ratio-with-fundamentals --agent claude-code

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

Facts
Files in the skill folder: 2
SKILL.md size: 11 KB
Bundled scripts: none
Path: skills/analysis/backsolve-miner-vs-metal-ratio-with-fundamentals/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_decomposition"> **比率拆解核心公式** 礦業股/金屬價格比率可分解為四大基本面因子: ``` R_t ≈ K × M_t × (1-L_t) × C_t × D_t ``` 其中: - **K**: 校準常數(由觀測值估計) - **M_t**: 倍數因子(EV/EBITDA) - **(1-L_t)**: 槓桿因子(1 - NetDebt/EV) - **C_t**: 成本因子(1 - AISC/S_t) - **D_t**: 稀釋因子(Shares_base / Shares_t) 此拆解讓「比率變動」有可歸因的量化解釋。 </principle> <principle name="aisc_extraction"> **AISC 抽取優先順序** 全維持成本(AISC)是礦業股估值的核心驅動: | 優先級 | 來源 | 方法 | |--------|-----------------|---------------------------------------------| | 1 | MD&A / 財報附註 | 關鍵字抽取:「AISC」「all-in sustaining」 | | 2 | 年報簡報 PDF | 解析表格:$/oz 或 $/ounce | | 3 | Proxy 回算 | (OpCost + SustCapex + G&A - Byproduct) / Oz | 當直接揭露不可得時,以 proxy 回算補缺;記錄 `aisc_method` 以標註來源。 </principle> <principle name="backsolve_logic"> **反推邏輯(Backsolve)** 目標:給定目標比率 R*(如歷史頂部 1.7),反推需要哪些因子條件。 **單因子反推**:假設其他因子不變,只調整單一因子 ``` M* = M_now × (R*/R_now) # 需要的倍數 (1-L*) = (1-L_now) × (R*/R_now) # 需要的去槓桿 C* = C_now × (R*/R_now) # 需要的成本改善 → 反推 AISC* D* = D_now × (R*/R_now) # 需要的稀釋折扣 ``` **雙因子組合**:以網格列舉可行組合(如倍數 +20% + 白銀 -15%)。 </principle> <principle name="event_study"> **事件研究方法** 識別「比率落入底部分位」的歷史事件,回看事件當期的四大因子狀態: 1. **AISC 是否上升**:成本壓力 2. **NetDebt/EV 是否惡化**:槓桿壓力 3. **EV/EBITDA 是否壓縮**:倍數

What's inside
Ships with 1 file
  • metadata.json
Commands it runs
cd skills/backsolve-miner-vs-metal-ratio-with-fundamentals
pip install pandas numpy yfinance matplotlib  # 首次使用
python scripts/fundamental_analyzer.py --quick
python scripts/fundamental_analyzer.py \
python scripts/visualize_factors.py --quick --output output/
python scripts/scenario_path_simulator.py --quick --output output/
python scripts/scenario_path_simulator.py \
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About this skill
What does the backsolve-miner-vs-metal-ratio-with-fundamentals skill do?

從網路自動抓取礦業公司財務報表與營運揭露(產量、成本、資本支出),回算「礦業股/金屬本體比率」的基本面解釋與區間門檻(如 1.2/1.7),並輸出可重現的估值拆解(成本因子 / 槓桿因子 / 倍數因子 / 稀釋因子)。

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill backsolve-miner-vs-metal-ratio-with-fundamentals --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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