Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/fatfingererr/macro-skillsnpx agentmods add skills/fatfingererr/macro-skills/detect-freight-led-inflation-turnWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fatfingererr/macro-skills/detect-freight-led-inflation-turn)<a href="https://agentmods.dev/skills/fatfingererr/macro-skills/detect-freight-led-inflation-turn"><img src="https://agentmods.dev/badge/skills/fatfingererr/macro-skills/detect-freight-led-inflation-turn/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fatfingererr/macro-skills/detect-freight-led-inflation-turn"><img src="https://agentmods.dev/badge/skills/fatfingererr/macro-skills/detect-freight-led-inflation-turn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00102 | $0.03217 |
| Opus 5 | $0.00051 | $0.01608 |
| Sonnet 5 | $0.00020 | $0.00643 |
| Haiku 4.5 | $0.00010 | $0.00322 |
Grade A, and why
detect-freight-led-inflation-turn scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 317 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<essential_principles>
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)
核心邏輯:
- 貨運量 ≈ 實體經濟需求強度
- 出貨量下降 → 終端需求減弱 → 定價能力下降
- 歷史上 CASS 指標對 CPI 具有約 4-6 個月的領先性
關鍵訊號不是單月變化,而是「週期轉折」:
- 年增率轉負 (turned negative)
- 創週期新低 (new cycle low)
當偵測到 CASS 週期轉折:
- 結論是「通膨壓力緩解」而非「通縮」
- 屬於 inflation easing / disinflation regime
- 支持市場對降息或政策轉向的預期
這是跨週期關係辨識:「物流需求動能 → 通膨方向」
建議同時觀察四個 CASS 指標:
- Shipments YoY(主要):需求端訊號
- Expenditures YoY:成本端訊號
- Shipments Index:絕對水準
- Expenditures Index:運費壓力
當 Shipments 和 Expenditures 同時轉負,訊號更為可靠。
</essential_principles>
輸出三層訊號:
- Freight Status: CASS 各指標狀態與週期位置
- Lead Alignment: 與 CPI YoY 的領先對齊分析
- Signal Assessment: 通膨緩解訊號判斷與信心水準
<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:
- JSON 分析結果:
{
"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"
}
- 視覺化圖表:
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>
- 快速檢查 - 查看最新的 CASS 指標與通膨先行訊號
- 完整分析 - 執行完整的週期轉折偵測與領先性分析
- 方法論學習 - 了解 CASS 指標與通膨的領先關係
請選擇或直接提供分析參數。
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>
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/sample_output.json 2.0 KB
- manifest.json 2.3 KB
- references/data-sources.md 10 KB
- references/historical-episodes.md 6.8 KB
- references/methodology.md 2.2 KB
- scripts/fetch_cass_freight.py 19 KB runs code
- scripts/fetch_via_cdp.py 4.6 KB runs code
- scripts/freight_inflation_detector.py 19 KB runs code
- scripts/visualize_freight_cpi.py 11 KB runs code
- skill.yaml 15 KB
- templates/output-json.md 6.9 KB
- templates/output-markdown.md 7.1 KB
- workflows/analyze.md 12 KB
- workflows/quick-check.md 4.4 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 317 lines · 102 tokens per session scan A b9d7ba563fac
detect-freight-led-inflation-turn is a skill published in the GitHub repository fatfingererr/macro-skills (3 stars, last pushed 7mo ago), licensed MIT. It adds 102 tokens to every session and 3,217 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
furusato
A Japanese hometown-tax donation manager for furusato nozei, a system where donations to municipalities can qualify for an income-tax or local-tax deduction. It reads donation receipts, stores donation records, and calculates deduction limits.
reading-receipt
An image-reading workflow for extracting structured information from receipts, invoices, and hometown-tax donation certificates. It can first extract text from PDFs and otherwise read their images.