Agent Skills › AlphaGBM/skills › alphagbm-unusual-activity

alphagbm-unusual-activity

GitHub

检测异常期权活动与聪明钱信号，监控成交量/持仓量比、大单交易及净溢价流。支持按股票代码扫描或市场范围扫描，输出交易分类、情绪分析及历史准确率，辅助追踪机构动向。

skills/alphagbm-unusual-activity/SKILL.md AlphaGBM/skills

Trigger Scenarios

unusual options activity smart money AAPL large trades NVDA who's buying TSLA puts options flow block trades sweep orders unusual volume dark pool activity whale trades

Install

npx skills add AlphaGBM/skills --skill alphagbm-unusual-activity -g -y

More Options

Use without installing

npx skills use AlphaGBM/skills@alphagbm-unusual-activity

指定 Agent (Claude Code)

npx skills add AlphaGBM/skills --skill alphagbm-unusual-activity -a claude-code -g -y

安装 repo 全部 skill

npx skills add AlphaGBM/skills --all -g -y

预览 repo 内 skill

npx skills add AlphaGBM/skills --list

SKILL.md

Frontmatter

{
    "name": "alphagbm-unusual-activity",
    "globs": [
        "mock-data\/unusual-activity\/**"
    ],
    "description": "Detects unusual options activity and smart money signals. Monitors volume\/OI ratio spikes,\nlarge block trades, unusual strike\/expiry combinations, and net premium flow.\nTriggers: \"unusual options activity\", \"smart money AAPL\", \"large trades NVDA\",\n\"who's buying TSLA puts\", \"options flow\", \"block trades\", \"sweep orders\",\n\"unusual volume\", \"dark pool activity\", \"whale trades\"\n"
}

AlphaGBM Unusual Options Activity

Detects unusual options activity and classifies smart money signals to help you follow institutional positioning.

What This Skill Does

Concept	Description
Volume/OI Ratio	When today's volume far exceeds open interest, it signals new positioning
Block Trade	A single large transaction (typically 100+ contracts) executed at one price
Sweep Order	Aggressive order that sweeps across multiple exchanges to get filled fast — indicates urgency
Premium Flow	Net dollar amount of call vs put premium — shows directional conviction
Sentiment Classification	Categorizes activity as bullish sweep, bearish block, hedging, or earnings positioning
Historical Accuracy	How often past unusual activity correctly predicted direction

How to Use

Input: A ticker symbol or market-wide scan request.

Output:

Unusual activity list: timestamp, strike, expiry, type (call/put), volume, OI, premium, trade classification
Sentiment classification per trade (bullish sweep, bearish block, hedging, earnings positioning)
Net premium flow (calls vs puts in dollar terms)
Historical accuracy: how often similar signals preceded the expected move
Aggregated smart money score

Example Queries:

unusual options activity — Market-wide scan of today's most unusual trades
smart money AAPL — Institutional flow signals for Apple
large trades NVDA — Block and sweep orders for NVIDIA
who's buying TSLA puts — Bearish flow analysis for Tesla
options flow SPY — Net premium flow for S&P 500 ETF

Mock Data

Mock data files are located in mock-data/unusual-activity/ and include:

aapl-unusual-trades.json — Recent unusual trades for AAPL
market-wide-scan.json — Top 20 unusual activity signals across all tickers
flow-summary.json — Aggregated premium flow by sector

API Endpoint

GET /api/options/unusual-activity/{symbol}
GET /api/options/unusual-activity/scan

Query parameters:

min_premium (int, default 100000) — Minimum trade premium in dollars
min_vol_oi_ratio (float, default 3.0) — Minimum volume-to-OI ratio
trade_type (string) — Filter: "sweep", "block", "all"
sentiment (string) — Filter: "bullish", "bearish", "all"

Response fields: trades[], net_premium_flow, sentiment_summary, smart_money_score, historical_accuracy

Related Skills

Skill	Relevance
alphagbm-options-score	Combines unusual activity into the overall options score
alphagbm-market-sentiment	Market-wide context for interpreting flow signals