From fatfingererr-macro-skills
用公開資料量化「銅供應是否過度集中、主要產地是否結構性衰退、替代增量是否依賴少數國家」,並輸出可行的中期供應風險結論與情境推演。
npx claudepluginhub joshuarweaver/cascade-code-general-misc-1 --plugin fatfingererr-macro-skillsThis skill uses the workspace's default tool permissions.
<essential_principles>
examples/concentration_analysis.jsonexamples/full_report.mdmanifest.jsonreferences/chile-supply-dynamics.mdreferences/concentration-metrics.mdreferences/data-sources.mdreferences/failure-modes.mdreferences/geopolitics-risk.mdreferences/methodology.mdreferences/replacement-countries.mdscripts/copper_concentration_analyzer.pyscripts/fetch_copper_production.pyscripts/visualize_copper_concentration.pyskill.yamltemplates/config.yamltemplates/output-json.mdtemplates/output-markdown.mdworkflows/analyze-chile-trend.mdworkflows/analyze-concentration.mdworkflows/analyze-replacement.mdCreates new Angular apps using Angular CLI with flags for routing, SSR, SCSS, prefixes, and AI config. Follows best practices for modern TypeScript/Angular development. Use when starting Angular projects.
Generates Angular code and provides architectural guidance for projects, components, services, reactivity with signals, forms, dependency injection, routing, SSR, ARIA accessibility, animations, Tailwind styling, testing, and CLI tooling.
Executes ctx7 CLI to fetch up-to-date library documentation, manage AI coding skills (install/search/generate/remove/suggest), and configure Context7 MCP. Useful for current API refs, skill handling, or agent setup.
<essential_principles>
**敘事轉指標(Narrative to Metrics)**市場敘事必須可量化驗證。三大命題對應三組指標:
| 命題 | 核心問題 | 量化指標 |
|---|---|---|
| A. 集中度 | 供應是否過度集中? | CR4, CR5, 份額排名 |
| B. 結構衰退 | 智利是否結構性衰退? | 峰值年份、峰值回撤 |
| C. 替代依賴 | 是否依賴秘魯/DRC? | 秘魯+DRC 合計份額 vs 智利份額 |
注意:由於 MacroMicro 只提供 5 個國家的細分數據,HHI 指標不適用於本分析。
**數據來源:MacroMicro (WBMS)**唯一主要來源,使用 Chrome CDP 全自動抓取 Highcharts 圖表數據。
</essential_principles>
分析全球銅供應的國家集中度與結構性風險。輸出兩層分析:
<quick_start>
全自動執行(無需手動操作 Chrome)
Step 1:安裝依賴
pip install requests websocket-client pandas numpy matplotlib
Step 2:一鍵抓取數據(自動啟動/關閉 Chrome)
cd scripts
python fetch_copper_production.py
腳本會自動:
cache/copper_production.csvStep 3:生成 Bloomberg 風格視覺化圖表
python visualize_copper_concentration.py
輸出:output/copper_concentration.png
</quick_start>
需要進行什麼分析?請選擇或直接提供分析參數。
| Response | Action | |----------|--------| | 1, "快速", "圖表", "chart" | `python scripts/fetch_copper_production.py && python scripts/visualize_copper_concentration.py` | | 2, "完整", "trend", "1970" | 抓取數據後輸出完整年度數據表 | | 3, "智利", "chile" | 分析智利份額趨勢與峰值 | | 4, "替代", "replacement", "秘魯", "drc" | 分析 Peru+DRC 是否已超越智利 |路由後,執行對應命令。
<directory_structure>
analyze-copper-supply-concentration-risk/
├── SKILL.md # 本文件(路由器)
├── skill.yaml # 前端展示元數據
├── scripts/
│ ├── fetch_copper_production.py # 全自動 CDP 數據爬蟲
│ └── visualize_copper_concentration.py # Bloomberg 風格視覺化
├── cache/
│ ├── copper_production.csv # 數據快取
│ └── copper_production_cache.json # 原始 JSON 快取
└── output/
└── copper_concentration.png # 輸出圖表
</directory_structure>
<scripts_index>
| Script | Command | Purpose |
|---|---|---|
| fetch_copper_production.py | python fetch_copper_production.py | 全自動 CDP 抓取(自動啟動/關閉 Chrome) |
| fetch_copper_production.py | --force-refresh | 強制重新抓取(忽略快取) |
| fetch_copper_production.py | --start-year 1970 | 指定起始年份 |
| visualize_copper_concentration.py | python visualize_copper_concentration.py | 生成 Bloomberg 風格圖表 |
| visualize_copper_concentration.py | --output path/to/output.png | 指定輸出路徑 |
| </scripts_index> |
視覺化輸出:Bloomberg 風格銅供應集中度儀表板
包含兩張圖(上下排列):
配色:Bloomberg 深色主題
#1a1a2e#ff6b35 (橙紅)#00bfff (天藍)#00ff88 (綠)#00d4aa (青綠)快速繪圖:
cd scripts
python visualize_copper_concentration.py
輸出路徑:output/copper_concentration.png
<output_example> 2023 年關鍵指標:
| 國家 | 份額 |
|---|---|
| Chile | 23.5% |
| Peru + DRC | 25.2% |
| China | 7.5% |
| US | 5.0% |
關鍵發現:
<success_criteria> 分析成功時應產出: