Agent skill · data analytics · tradermonty
market-breadth-analyzer
Quantifies market breadth health using TraderMonty's public CSV data. Generates a 0-100 composite score across 6 components (100 = healthy). No API key required. Use when user asks about market breadth, participation rate, advance-decline health, whether the rally is broad-based, or general market health assessment.
Why this skill is useful
Provides a quantitative scoring system for market breadth analysis using specific CSV data that the AI wouldn't generate on its own.
What it needs
Requires python installed locally. About 3k tokens when loaded. Last updated 2026-08-06. 2,581 stars on the source repository.
What this skill does
Market Breadth Analyzer Skill Purpose Quantify market breadth health using a data-driven 6-component scoring system (0-100). Uses TraderMonty's publicly available CSV data to measure how broadly the market is participating in a rally or decline. Score direction: 100 = Maximum health (broad participation), 0 = Critical weakness. No API key required - uses freely available CSV data from GitHub Pages. When to Use This Skill English: User asks "Is the market rally broad-based?" or "How healthy is market breadth?" User wants to assess market participation rate User asks about advance-decline indicators or breadth thrust User wants to know if the market is narrowing (fewer stocks participating) User asks about equity exposure levels based on breadth conditions Japanese: 「マーケットブレッドスはどうですか？」「市場の参加率は？」 「上昇は広がっている？」「一部の銘柄だけの上昇？」 ブレッドス指標に基づくエクスポージャー判断 市場の健康度をデータで確認したい Prerequisites Python 3.9+ with requests library (for fetching CSV data) Internet access to reach GitHub Pages URLs No API keys required - uses freely available public CSV data Difference from Breadth Chart Analyst Aspect Market Breadth Analyzer Breadth Chart Analyst -------- ------------------------ ---------------------- Data Source CSV (automated) Chart images (manual) API Required None None Output Quantitative 0-100 score Qualitative chart analysis Components 6 scored dimensions Visual pattern recognition Repeatability Fully reproducible Analyst-dependent --- Execution Workflow Phase 1: Execute Python Script Run the analysis script. If using a nested or date-stamped --output-dir in cron runs, create it first; the history writer expects the directory to already exist. For a simple ad-hoc run, omit --output-dir or use an existing directory. In scheduled cron runs from the repository root, prefer a repo-relative output directory such as reports/after-close-YYYY-MM-DD rather than an absolute path. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills tradermonty/market-breadth-analyzer