OpenSkillIndex← back to search
claude-code

Skillshub analyzing-market-sentiment

install
source · Clone the upstream repo
git clone https://github.com/ComeOnOliver/skillshub
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/jeremylongshore/claude-code-plugins-plus-skills/analyzing-market-sentiment" ~/.claude/skills/comeonoliver-skillshub-analyzing-market-sentiment && rm -rf "$T"
manifest: skills/jeremylongshore/claude-code-plugins-plus-skills/analyzing-market-sentiment/SKILL.md
tags
#crypto#analyzing-market
source content

Analyzing Market Sentiment

Overview

Cryptocurrency market sentiment analysis combining Fear & Greed Index, news keyword analysis, and price/volume momentum into a composite 0-100 score.

Prerequisites

  1. Python 3.8+ installed
  2. Dependencies: 
    pip install requests
  3. Internet connectivity for API access (Alternative.me, CoinGecko)
  4. Optional: 
    crypto-news-aggregator
    skill for enhanced news analysis

Instructions

  1. Assess user intent - determine what analysis is needed:

    • Overall market: no specific coin, general sentiment
    • Coin-specific: extract symbol (BTC, ETH, etc.)
    • Quick vs detailed: quick score or full component breakdown

  2. Run sentiment analysis with appropriate options:

    # Quick market sentiment check
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py

# Coin-specific sentiment
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --coin BTC

# Detailed breakdown with all components
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --detailed

# Custom time period
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --period 7d --detailed

  3. Export results for trading models or analysis:

    python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --format json --output sentiment.json

  4. Present results to the user:

    • Show composite score and classification prominently
    • Explain what the sentiment reading means
    • Highlight extreme readings (potential contrarian signals)
    • For detailed mode, show component breakdown with weights

Output

Composite sentiment score (0-100) with classification and weighted component breakdown. Extreme readings serve as contrarian indicators:

==============================================================================
  MARKET SENTIMENT ANALYZER                         Updated: 2026-01-14 15:30  # 2026 - current year timestamp
==============================================================================

  COMPOSITE SENTIMENT
------------------------------------------------------------------------------
  Score: 65.5 / 100                         Classification: GREED

  Component Breakdown:
  - Fear & Greed Index:  72.0  (weight: 40%)  -> 28.8 pts
  - News Sentiment:      58.5  (weight: 40%)  -> 23.4 pts
  - Market Momentum:     66.5  (weight: 20%)  -> 13.3 pts

  Interpretation: Market is moderately greedy. Consider taking profits or
  reducing position sizes. Watch for reversal signals.

==============================================================================

Error Handling

ErrorCauseSolution
Fear & Greed unavailableAPI downUses cached value with warning
News fetch failedNetwork issueReduces weight of news component
Invalid coinUnknown symbolProceeds with market-wide analysis

See 

${CLAUDE_SKILL_DIR}/references/errors.md
for comprehensive error handling.

Examples

Sentiment analysis patterns from quick checks to custom-weighted deep analysis:

# Quick market sentiment
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py

# Bitcoin-specific sentiment
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --coin BTC

# Detailed analysis with component breakdown
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --detailed

# Custom weights emphasizing news
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --weights "news:0.5,fng:0.3,momentum:0.2"

# Weekly sentiment trend
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --period 7d --detailed

Resources

  • ${CLAUDE_SKILL_DIR}/references/implementation.md
    - CLI options, classifications, JSON format, contrarian theory
  • ${CLAUDE_SKILL_DIR}/references/errors.md
    - Comprehensive error handling
  • ${CLAUDE_SKILL_DIR}/references/examples.md
    - Detailed usage examples
  • Alternative.me Fear & Greed: https://alternative.me/crypto/fear-and-greed-index/
  • CoinGecko API: https://www.coingecko.com/en/api
  • ${CLAUDE_SKILL_DIR}/config/settings.yaml
    - Configuration options