From fatfingererr-macro-skills
透過美國卡斯貨運指數 (CASS Freight Index) 的週期轉折,偵測美國通膨壓力是否進入放緩或反轉階段。用於判斷「通膨是否正在降溫」,並驗證市場對降息、通膨回落的宏觀敘事是否有實體經濟數據支撐。
npx claudepluginhub joshuarweaver/cascade-code-general-misc-1 --plugin fatfingererr-macro-skillsThis skill uses the workspace's default tool permissions.
<essential_principles>
examples/sample_output.jsonmanifest.jsonreferences/data-sources.mdreferences/historical-episodes.mdreferences/methodology.mdscripts/fetch_cass_freight.pyscripts/fetch_via_cdp.pyscripts/freight_inflation_detector.pyscripts/visualize_freight_cpi.pyskill.yamltemplates/output-json.mdtemplates/output-markdown.mdworkflows/analyze.mdworkflows/quick-check.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>
**CASS Freight Index 是最權威的貨運指標**CASS Freight Index 由 Cass Information Systems 編制,追蹤北美地區的貨運出貨量與支出:
| 指標 | 說明 | 用途 |
|---|---|---|
| Shipments Index | 出貨量指數 | 衡量實體經濟需求強度 |
| Expenditures Index | 運費支出指數 | 衡量物流成本壓力 |
| Shipments YoY | 出貨量年增率 | 偵測週期轉折(主要分析指標) |
| Expenditures YoY | 支出年增率 | 驗證成本傳導 |
數據來源:MacroMicro (透過 Highcharts 爬取)
**貨運量是通膨的領先指標**核心邏輯:
關鍵訊號不是單月變化,而是「週期轉折」:
當偵測到 CASS 週期轉折:
這是跨週期關係辨識:「物流需求動能 → 通膨方向」
**多指標交叉驗證**建議同時觀察四個 CASS 指標:
當 Shipments 和 Expenditures 同時轉負,訊號更為可靠。
</essential_principles>
偵測 CASS Freight Index 的週期轉折,判斷通膨是否正在放緩。輸出三層訊號:
<quick_start>
最快的方式:使用 Chrome CDP 抓取數據
Step 1:安裝依賴
pip install requests websocket-client pandas numpy
Step 2:啟動 Chrome 調試模式
# Windows
"C:\Program Files\Google\Chrome\Application\chrome.exe" ^
--remote-debugging-port=9222 ^
--remote-allow-origins=* ^
--user-data-dir="%USERPROFILE%\.chrome-debug-profile" ^
"https://www.macromicro.me/charts/46877/cass-freight-index"
Step 3:等待頁面完全載入(圖表顯示),然後執行
cd scripts
python fetch_cass_freight.py --cdp
Step 4:執行通膨訊號分析
python freight_inflation_detector.py --quick
Step 5:生成視覺化圖表
python visualize_freight_cpi.py \
--cache cache/cass_freight_cdp.json \
--output ../../output/freight_cpi_$(date +%Y-%m-%d).png \
--start 1995-01-01
輸出範例:
{
"signal": "inflation_easing",
"confidence": "high",
"freight_yoy": -7.46,
"cycle_status": "negative",
"indicator": "shipments_yoy",
"macro_implication": "通膨壓力正在放緩,未來 CPI 下行風險上升"
}
output/freight_cpi_2026-01-23.png備選方法(Selenium):
pip install selenium webdriver-manager
python scripts/fetch_cass_freight.py --selenium --no-headless
</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", "quick", "check" | 執行 `python scripts/freight_inflation_detector.py --quick` | | 2, "完整", "full", "analyze" | 閱讀 `workflows/analyze.md` 並執行 | | 3, "學習", "方法論", "why" | 閱讀 `references/methodology.md` | | 提供參數 (如日期範圍) | 閱讀 `workflows/analyze.md` 並使用參數執行 |路由後,閱讀對應文件並執行。
<directory_structure>
detect-freight-led-inflation-turn/
├── SKILL.md # 本文件(路由器)
├── skill.yaml # 前端展示元數據
├── manifest.json # 技能元資料
├── workflows/
│ ├── analyze.md # 完整分析工作流
│ └── quick-check.md # 快速檢查工作流
├── references/
│ ├── data-sources.md # CASS 數據來源與爬蟲說明
│ ├── methodology.md # 領先性方法論解析
│ └── historical-episodes.md # 歷史案例對照
├── templates/
│ ├── output-json.md # JSON 輸出模板
│ └── output-markdown.md # Markdown 報告模板
├── scripts/
│ ├── fetch_cass_freight.py # MacroMicro CASS 爬蟲
│ ├── fetch_via_cdp.py # Chrome CDP 爬蟲模組
│ ├── freight_inflation_detector.py # 主分析腳本
│ └── visualize_freight_cpi.py # CASS vs CPI 領先性視覺化
└── examples/
└── sample_output.json # 範例輸出
</directory_structure>
<reference_index>
方法論: references/methodology.md
資料來源: references/data-sources.md
歷史案例: references/historical-episodes.md
</reference_index>
<workflows_index>
| Workflow | Purpose | 使用時機 |
|---|---|---|
| analyze.md | 完整週期轉折分析 | 需要深度分析時 |
| quick-check.md | 快速檢查訊號 | 日常監控或快速回答 |
| </workflows_index> |
<templates_index>
| Template | Purpose |
|---|---|
| output-json.md | JSON 輸出結構定義 |
| output-markdown.md | Markdown 報告模板 |
| </templates_index> |
<scripts_index>
| Script | Command | Purpose |
|---|---|---|
| fetch_cass_freight.py | --cdp | 使用 CDP 爬取(推薦) |
| fetch_cass_freight.py | --selenium --no-headless | 使用 Selenium 爬取(備選) |
| freight_inflation_detector.py | --quick | 快速檢查最新訊號 |
| freight_inflation_detector.py | --start DATE --indicator X | 完整分析 |
| visualize_freight_cpi.py | --lead-months 6 --start DATE | 繪製 CASS vs CPI 領先圖 |
| </scripts_index> |
視覺化輸出:CASS vs CPI 領先性對比圖
核心特徵(參考 Bloomberg/Refinitiv 風格):
快速繪圖:
cd scripts
python visualize_freight_cpi.py \
--cache cache/cass_freight_cdp.json \
--output ../../output/freight_cpi_YYYY-MM-DD.png \
--start 1995-01-01 \
--lead-months 6
輸出路徑:output/freight_cpi_YYYY-MM-DD.png(根目錄)
圖表解讀:
<input_schema>
**Type**: string (ISO YYYY-MM-DD) **Description**: 分析起始日期 **Example**: "2010-01-01" **Type**: string (ISO YYYY-MM-DD) **Description**: 分析結束日期 **Type**: string **Options**: `shipments_index` | `expenditures_index` | `shipments_yoy` | `expenditures_yoy` **Description**: CASS 指標選擇 - `shipments_yoy`: 出貨量年增率(推薦,主要分析指標) - `expenditures_yoy`: 支出年增率 - `shipments_index`: 出貨量指數 - `expenditures_index`: 支出指數 **Type**: integer **Description**: 領先 CPI 的月份數 **Range**: 3-12 **Type**: float **Description**: 年增率警戒門檻(如 0 表示轉負)</input_schema>
<output_schema>
參見 templates/output-json.md 的完整結構定義。
摘要:
{
"signal": "inflation_easing | inflation_rising | neutral",
"confidence": "high | medium | low",
"freight_yoy": -2.9,
"cycle_status": "new_cycle_low | negative | positive",
"indicator": "shipments_yoy",
"macro_implication": "通膨壓力正在放緩,未來 CPI 下行風險上升",
"all_indicators": {
"shipments_index": {...},
"expenditures_index": {...},
"shipments_yoy": {...},
"expenditures_yoy": {...}
}
}
</output_schema>
<success_criteria> 分析成功時應產出: