detect-freight-led-inflation-turn

detect-freight-led-inflation-turn is a skill for Claude Code, Codex from fatfingererr/macro-skills. It costs 102 tokens per session (3,217 once invoked), scanned A, original, MIT.

A financial analysis workflow that uses the CASS Freight Index, a measure of North American shipping activity and spending, to look for changes in inflation pressure.

In plain words
What is it for?
Use it to review four freight measures, compare their timing with year-over-year consumer-price inflation, and assess whether an inflation-easing signal is present.
Why use it?
It helps connect weakening freight demand and logistics costs with possible future easing of consumer-price inflation, rather than judging inflation from a single monthly change.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is --output ../../output/freight_cpi_$(date +%Y-%m-%d).png \.

Good fit Use it to review four freight measures, compare their timing with year-over-year consumer-price inflation, and assess whether an inflation-easing signal is present.

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Install

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.

Clone the repo
git clone --depth 1 https://github.com/fatfingererr/macro-skills
agentmods
npx agentmods add skills/fatfingererr/macro-skills/detect-freight-led-inflation-turn

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin detect-freight-led-inflation-turn/plugin install detect-freight-led-inflation-turn after adding the marketplace above.

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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.

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README.md
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<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>
Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,217 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash b9d7ba563fac, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/fetch_cass_freight.py, scripts/fetch_via_cdp.py, scripts/freight_inflation_detector.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/detect-freight-led-inflation-turn/SKILL.md · 317 lines

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:

  1. Shipments YoY(主要):需求端訊號
  2. Expenditures YoY:成本端訊號
  3. Shipments Index:絕對水準
  4. 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:

  1. Freight Status: CASS 各指標狀態與週期位置
  2. Lead Alignment: 與 CPI YoY 的領先對齊分析
  3. 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>

  1. 快速檢查 - 查看最新的 CASS 指標與通膨先行訊號
  2. 完整分析 - 執行完整的週期轉折偵測與領先性分析
  3. 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>

Read the full file on GitHub · 317 lines

Changes

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.

  1. 11d ago First seen · 317 lines · 102 tokens per session scan A b9d7ba563fac

Subscribe to this mod's changes

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.

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