Crypto Sentiment Monitor

Real-time crypto market sentiment analysis. Aggregates Twitter/X, Reddit, Google Trends, and exchange data. Detects FOMO/FUD cycles and whale movements. Trig...

Security Scan

The skill's instructions reference external tools, scripts, and APIs needed for crypto sentiment collection but the package declares no binaries, no code files, and no credentials — this mismatch makes the skill incomplete and potentially risky to run as-is.

Purpose & Capability

The SKILL.md describes aggregation from Twitter/X, Reddit, Google Trends, Whale Alert, and exchanges — which normally requires specific binaries/tools (e.g., an X/Twitter client), API keys, or scraping code. However the skill metadata claims no required binaries, no env vars, and there are no code files provided. That is inconsistent: a monitoring skill would legitimately need client tools or credentials, but none are declared or provided.

Instruction Scope

Runtime instructions tell the agent to run 'xreach' and a python script (analyze_sentiment.py) and call functions like get_google_trends() and get_whale_alerts(), but those scripts/functions are not included and no guidance is given for authentication or how to obtain these tools. The instructions are porous and open-ended (references to Telegram channels, Reddit hot posts) without specifying required credentials, endpoints, or allowed data sources — giving the agent broad, ambiguous discretion.

Install Mechanism

No install spec (instruction-only), which minimizes direct install risk. However the instructions rely on external binaries/tools (xreach) and Python scripts that are not provided or documented; installing those would be left to the user/agent without provenance, which raises risk if the agent tries to fetch or run unknown third-party code.

Credentials

The skill declares no required environment variables or primary credential, yet the described integrations (Twitter/X API, Whale Alert, Google Trends, exchange APIs, Telegram) normally require API keys or tokens. The absence of declared credential requirements is a mismatch and could lead the agent or user to supply credentials without clear justification or to attempt scraping/unauthenticated access.

Persistence & Privilege

The skill does not request 'always: true' or other elevated persistence/privileges and is user-invocable with default autonomous invocation allowed (normal). It does not declare any configuration changes to other skills or system-wide settings.

What to consider before installing

Do not install or run this skill as-is. It is incomplete and ambiguous: the SKILL.md calls binaries (xreach) and scripts (analyze_sentiment.py) that are not included, and it does not declare the API keys or install steps needed for Twitter/X, Whale Alert, Google Trends, or exchange data. Before proceeding, ask the publisher for: (1) source code or links to the missing scripts; (2) explicit install instructions and trusted sources for any third-party tools; (3) a list of required credentials and the minimum-scoped permissions needed (prefer read-only, scoped API keys or OAuth rather than broad tokens); (4) a homepage or repository to verify provenance. If you must test it, run in an isolated environment (sandbox/container) with dummy/scoped credentials and audit network calls. Avoid providing high-privilege or long-lived keys until you can review the code and confirm the exact endpoints and data handling practices.

Real-time cryptocurrency market sentiment analysis combining social media, search trends, and exchange data.

Features

• Social Sentiment: Twitter/X, Reddit, Telegram channel analysis
• Search Trends: Google Trends, Baidu Index for crypto keywords
• Exchange Data: Funding rates, open interest, whale transactions
• FOMO/FUD Detection: Fear & Greed index calculation

Usage

Twitter Sentiment

xreach search "$BTC OR #Bitcoin OR $ETH" -n 50 --json | \
  python3 analyze_sentiment.py

Fear & Greed Index

def calculate_fear_greed():
    """Calculate Crypto Fear & Greed Index (0-100)"""
    components = {
        "volatility": get_volatility(),      # 25%
        "market_momentum": get_momentum(),  # 25%
        "social_volume": get_social_vol(),  # 15%
        "dominant": get_btc_dominance(),    # 10%
        "trends": get_google_trends(),      # 10%
        "whale_ratio": get_whale_ratio(),   # 15%
    }
    score = sum(c["weight"] * c["value"] 
                for c in components.values())
    
    if score < 25: return "Extreme Fear 😱"
    elif score < 45: return "Fear 😰"
    elif score < 55: return "Neutral 😐"
    elif score < 75: return "Greed 😊"
    else: return "Extreme Greed 🤑"

Whale Alert Detection

def detect_whale_movements():
    """Detect large wallet transactions"""
    alerts = get_whale_alerts(min_usd=1000000)
    for alert in alerts:
        if alert["amount_usd"] > 10000000:
            print(f"🐋 ${alert['amount_usd']/1e6:.1f}M moved: "
                  f"{alert['from']} → {alert['to']}")

Sources

• Twitter/X: xreach tool
• Reddit: r/CryptoCurrency and r/Bitcoin hot posts
• Google Trends: crypto, bitcoin, ethereum
• Whale Alert: whale-alert.io (free API)

Tags

crypto bitcoin sentiment trading fear-greed whale-alert twitter