Einstein Research — 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 requi...

Security Scan

The skill's code and behavior mostly match its market-breadth purpose, but the runtime instructions/documentation contain mismatches (non-existent script path, differing output names) and incomplete dependency guidance — these inconsistencies warrant caution before installation.

Purpose & Capability

The code files implement the stated 6-component breadth analysis using public CSVs from TraderMonty's GitHub Pages. The requested resources (public URLs) and file I/O are consistent with the described functionality; no unrelated cloud credentials, binaries, or config paths are requested.

Instruction Scope

SKILL.md tells the agent to run 'python3 skills/market-breadth/scripts/breadth_analyzer.py', but the bundle contains 'scripts/market_breadth_analyzer.py' (different path and filename). SKILL.md also describes output filenames ('breadth_report_YYYY-MM-DD.*') that differ from the actual script's outputs ('market_breadth_YYYY-MM-DD_HHMMSS.*'). These mismatches could cause a naive agent to fail, run the wrong command, or attempt ad-hoc fixes. Aside from that, the declared runtime actions (fetch public CSVs, compute scores, write JSON/MD reports, maintain a small local history file) stay within the stated purpose and do not request unrelated system data or secrets.

Install Mechanism

No install spec is provided (instruction-only from registry perspective) and the code is included in the bundle. No network downloads of arbitrary archives or external installers are required by the skill itself. This is low install risk. Note: the bundle includes Python scripts that will run if executed.

Credentials

The skill requests no environment variables or credentials, which is appropriate. However, SKILL.md and the header metadata do not list required runtime dependencies (Python version, pip packages). README mentions pandas and requests; the code clearly uses requests and standard csv; pandas may or may not be required by some omitted files (report generator). The missing explicit dependency listing in SKILL.md is an operational gap but not a credential risk.

Persistence & Privilege

always is false and the skill does not request elevated privileges. It writes a small JSON history file (market_breadth_history.json) to the configured output directory — this is proportional to its purpose. Autonomous invocation (disable-model-invocation false) is standard; by itself it is not a red flag here.

What to consider before installing

This skill appears to implement the advertised market-breadth analysis and fetches only public CSVs, but the runtime documentation is inconsistent with the contained code. Before installing or running: - Verify the correct entrypoint: run scripts/market_breadth_analyzer.py (or inspect the bundle) rather than the path in SKILL.md, which appears incorrect. Do not run unknown commands suggested by a mismatched README without checking. - Ensure Python 3.8+ and required packages (requests at minimum; README mentions pandas) are installed in a controlled environment (e.g., virtualenv) before executing. - Review any omitted files (report_generator, scorer) for additional dependencies or network calls; the truncated manifest indicates there are more files not shown in the prompt. - Note the skill writes a small history JSON file to the output directory — pick an output directory you control (not system dirs) to avoid accidental overwrites. These issues likely stem from documentation drift rather than malicious intent, but confirm the entrypoint and dependencies locally before running to avoid unexpected behavior.

Market Breadth Analyzer

Overview

This skill quantifies the health of market breadth using public data from TraderMonty's GitHub repository. It generates a composite score from 0-100 (100 = healthy) across six key components, providing a quick, data-driven assessment of market participation.

Key Features:

• Composite Score (0-100): Single, easy-to-understand metric for breadth health.
• 6-Component Analysis:
  1. % Stocks > 50-day MA
  2. % Stocks > 200-day MA
  3. 1-Month New Highs - New Lows
  4. Advance-Decline Line (ADL) Momentum
  5. % Bullish (AAII Sentiment)
  6. S&P 500 distance from 200-day MA
• No API Key Required: Uses a publicly available CSV, making it free and reliable.
• Historical Context: Compares the current score to its 3-month and 6-month moving averages.

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When to Use This Skill

Explicit Triggers:

• "What's the current market breadth?"
• "Is this rally broad-based?"
• "Analyze market participation."
• "Show me the advance-decline health."
• User asks about "market breadth," "A-D line," "% stocks above moving average."

Implicit Triggers:

• User is concerned about a narrow, top-heavy market rally (e.g., led by only a few mega-cap stocks).
• User is assessing the risk of a market downturn, as poor breadth is often a leading indicator.

When NOT to Use:

• For real-time, intraday breadth data (this is end-of-day).
• For individual stock analysis.
• For deep technical analysis of a single indicator (this skill provides a composite view).

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Workflow

Step 1: Execute the Analysis Script

The entire process is handled by a single Python script.

# Run the breadth analysis
python3 skills/market-breadth/scripts/breadth_analyzer.py

The script performs the following actions:

1. Downloads Data: Fetches the latest Market-Breadth-Data.csv from TraderMonty's public GitHub repo.
2. Calculates Components: For each of the 6 components, it calculates a normalized score (0-100) based on its current value relative to its 1-year range.
3. Computes Composite Score: A weighted average of the 6 component scores.
   • % > 50d MA: 25%
   • % > 200d MA: 25%
   • NH-NL: 20%
   • ADL Momentum: 15%
   • AAII Bullish: 10% (inverse scoring)
   • SPX distance from 200d MA: 5%
4. Generates Report: Outputs a JSON file and a human-readable Markdown summary.

Step 2: Analyze the Output

The script produces two files:

• breadth_report_YYYY-MM-DD.json
• breadth_report_YYYY-MM-DD.md

JSON Output:

{
  "composite_score": 78.5,
  "assessment": "Healthy",
  "trend": "Improving",
  "components": {
    "stocks_above_50d_ma": 85,
    "stocks_above_200d_ma": 90,
    "new_highs_lows": 75,
    "ad_line_momentum": 60,
    "aaii_bullish_inverse": 70,
    "spx_distance_from_200d_ma": 95
  },
  "moving_averages": {
    "3_month": 65.2,
    "6_month": 58.9
  }
}

Markdown Report:

• Overall Score: 78.5 / 100 (Healthy)
• Trend: Improving (Current > 3-Month MA)
• Component Breakdown: A table showing the score for each of the 6 components.
• Key Takeaway: A short, human-readable summary of the current breadth situation.

Step 3: Present Findings to User

Synthesize the Markdown report into a concise, clear answer.

Example Response: "Current market breadth is healthy, with a composite score of 78.5 out of 100. This is above the 3-month average of 65.2, indicating an improving trend.

• Strengths: A high percentage of stocks are trading above their 50-day (85/100) and 200-day (90/100) moving averages.
• Weakness: Advance-Decline Line momentum is only moderate (60/100).

Overall, this suggests the current market rally is broad-based and well-supported."

Interpretation Guide

• > 70 (Healthy): Strong participation. Rally is likely sustainable.
• 50-70 (Moderate): Decent participation, but some signs of narrowing.
• 30-50 (Weak): Narrow, selective market. High risk of reversal.
• < 30 (Very Weak): Extremely poor participation. Market is vulnerable.

A divergence (e.g., S&P 500 making new highs while the breadth score is falling) is a significant warning sign.