track-agri-hedge-fund-positioning

track-agri-hedge-fund-positioning is a skill for Claude Code, Codex from fatfingererr/macro-skills. It costs 75 tokens per session (3,090 once invoked), scanned A, original, MIT.

A tracker of weekly futures positions held by non-commercial traders, such as funds and speculators, in agricultural commodities. It combines these position changes with exports, USDA data, and movements in the dollar, oil, and metals.

In plain words
What is it for?
Track positioning across grains, oilseeds, meats, softs, fiber, and dairy; measure buying capacity; and combine fund flows with commodity and macroeconomic signals.
Why use it?
It shows whether funds are adding or reducing exposure and whether their positions still have room to grow, while filling the gap between weekly reports with market clues.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: reads .claude/ paths.

Good fit Track positioning across grains, oilseeds, meats, softs, fiber, and dairy; measure buying capacity; and combine fund flows with commodity and macroeconomic signals.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/fatfingererr/macro-skills/track-agri-hedge-fund-positioning
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add fatfingererr/macro-skills --skill track-agri-hedge-fund-positioning
Clone the repo
git clone --depth 1 https://github.com/fatfingererr/macro-skills

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin track-agri-hedge-fund-positioning/plugin install track-agri-hedge-fund-positioning after adding the marketplace above.

Wrote 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.

agentmods badge for track-agri-hedge-fund-positioning

README.md
[![agentmods](https://agentmods.dev/badge/skills/fatfingererr/macro-skills/track-agri-hedge-fund-positioning/github.svg)](https://agentmods.dev/skills/fatfingererr/macro-skills/track-agri-hedge-fund-positioning)
Your own site
<a href="https://agentmods.dev/skills/fatfingererr/macro-skills/track-agri-hedge-fund-positioning"><img src="https://agentmods.dev/badge/skills/fatfingererr/macro-skills/track-agri-hedge-fund-positioning/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.

agentmods 80×15 button for track-agri-hedge-fund-positioning

Your own site · 80×15
<a href="https://agentmods.dev/skills/fatfingererr/macro-skills/track-agri-hedge-fund-positioning"><img src="https://agentmods.dev/badge/skills/fatfingererr/macro-skills/track-agri-hedge-fund-positioning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,090 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 $0.00075 $0.03090
Opus 5 $0.00037 $0.01545
Sonnet 5 $0.00015 $0.00618
Haiku 4.5 $0.00007 $0.00309

Measured 12d ago against content hash 0ed7904450aa, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

track-agri-hedge-fund-positioning 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 12d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/analyze_positioning.py, scripts/fetch_cot_data.py, scripts/fetch_macro_data.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/track-agri-hedge-fund-positioning/SKILL.md · 259 lines

How it starts

The opening of the file, as written. The whole thing — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.

<essential_principles>

CFTC Commitments of Traders 報告是追蹤對沖基金農產品部位的核心資料:

  • 截止日:每週二收盤
  • 發布日:每週五下午 3:30 ET
  • 交易者分類:非商業(投機/基金)、商業(避險)、非報告

資金流 = 本週淨部位 - 上週淨部位(以合約口數計)

使用 CFTC 原生分組(commodity_subgroup_name):

群組 CFTC 分組名稱 包含商品
Grains GRAINS Corn, Wheat (SRW/HRW/HRS), Oats
Oilseeds OILSEED and PRODUCTS Soybeans, Soybean Oil/Meal, Canola
Meats LIVESTOCK/MEAT PRODUCTS Live Cattle, Lean Hogs, Feeder
Softs FOODSTUFFS/SOFTS Coffee, Sugar, Cocoa, OJ
Fiber FIBER Cotton
Dairy DAIRY PRODUCTS Milk, Butter, Cheese

Total Flow = Grains + Oilseeds + Meats + Softs + Fiber + Dairy

火力衡量基金是否還有加碼空間:

net_pos_percentile = rank(current_net_pos, past_N_weeks)
firepower = 1 - net_pos_percentile
  • 高火力(>0.6):部位處於歷史低檔,仍有大量買進空間
  • 低火力(<0.3):部位已接近歷史高檔,擁擠風險高

整合三個風險偏好指標:

指標 訊號解讀
美元 (DXY) 走弱(負報酬)= 利於商品
原油 (WTI) 走強(正報酬)= 風險偏好上升
金屬 走強(正報酬)= 循環需求樂觀

macro_tailwind_score = (DXY弱 + WTI強 + Metals強) / 3

COT 只到週二,週三~週五需用代理證據:

  1. 價格動能:農產品/代理指數 Wed-Fri 累積報酬
  2. 未平倉變化:OI 擴張 = 新倉(非純換手)
  3. 宏觀共振:與 USD↓、油價↑、金屬↑ 同時性

</essential_principles>

  1. 取得資料:COT 週報、宏觀指標(DXY/WTI/金屬)、基本面觸發(出口/USDA)
  2. 計算流量:淨部位週變化,分組彙總(Grains/Oilseeds/Meats/Softs/Total)
  3. 量化火力:用歷史分位數估算基金加碼空間
  4. 整合訊號:判斷「基金回來買」+ 「宏觀順風」+ 「基本面支持」
  5. 產出敘事:將圖表標註(如 Strong Corn Demand)轉為可重複的規則

輸出:週流量時序、最新狀態、火力分數、宏觀評分、可交易註解。

<quick_start>

快速開始:分析最新 COT 資料

cd .claude/skills/track-agri-hedge-fund-positioning/scripts
pip install pandas numpy requests yfinance pyarrow  # 首次使用
python analyze_positioning.py --start 2023-01-01

輸出範例(真實資料):

{
  "skill": "track-agri-hedge-fund-positioning",
  "as_of": "2026-01-20",
  "data_source": "CFTC Socrata API (real data)",
  "summary": {
    "call": "Funds continue selling",
    "all_signals": ["Funds continue selling", "Extreme short - watch for reversal", "Macro mood bullish"],
    "confidence": 0.90
  },
  "latest_metrics": {
    "flow_total_contracts": -24559,
    "flow_by_group_contracts": {"grains": -31279, "oilseeds": 11517, "meats": 18972, "softs": -23887, "fiber": 1607, "dairy": -1489},
    "buying_firepower": {"total": 0.86, "grains": 0.58, "oilseeds": 0.62, "meats": 0.31, "softs": 0.99, "fiber": 0.58, "dairy": 0.99},
    "macro_tailwind_score": 0.67
  }
}

視覺化圖表

python visualize_flows.py --weeks 52
# 輸出:output/agri_fund_positioning_YYYY-MM-DD.png

</quick_start>

  1. 快速檢查 - 查看最新一週的基金部位變化與狀態
  2. 完整分析 - 指定日期範圍的資金流向分析與回測
  3. 視覺化圖表 - 生成分組柱狀圖與火力時序圖
  4. 監控模式 - 設定週度更新與訊號警報
  5. 方法論學習 - 了解 COT 分析與火力計算邏輯

請選擇或直接提供分析參數。

Read the full file on GitHub · 259 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. 12d ago First seen · 259 lines · 75 tokens per session scan A 0ed7904450aa

Subscribe to this mod's changes

track-agri-hedge-fund-positioning is a skill published in the GitHub repository fatfingererr/macro-skills (3 stars, last pushed 7mo ago), licensed MIT. It adds 75 tokens to every session and 3,090 once invoked, about $0.0004 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.

Related

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.

HKUDS/Vibe-Trading · 39 tokens

strategy-pivot-designer

Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.

tradermonty/claude-trading-skills · 28 tokens

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…

himself65/finance-skills · 161 tokens

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.

questflowai/investorskills · 44 tokens

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.

kazukinagata/shinkoku · 102 tokens

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.

kazukinagata/shinkoku · 64 tokens