Analyzing Market Sentiment
SkillCommerce & financeLets your agent check crypto market mood using the Fear & Greed Index and news.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Analyzing Market Sentiment skill
About this capability
'Analyze cryptocurrency market sentiment using Fear & Greed Index, news
What this skill tells your AI
The instructions your AI receives, as published by jeremylongshore/tons-of-skills-marketplace in skills/.curated/analyzing-market-sentiment/SKILL.md and read by ahel’s review.
Overview
Cryptocurrency market sentiment analysis combining Fear & Greed Index, news keyword analysis, and price/volume momentum into a composite 0-100 score.
Prerequisites
- Python 3.8+ installed
- Dependencies:
pip install requests - Internet connectivity for API access (Alternative.me, CoinGecko)
- Optional:
crypto-news-aggregatorskill for enhanced news analysis
Instructions
-
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
-
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 -
Export results for trading models or analysis:
python ${CLAUDE_SKILL_DIR}/scripts/sentiment_analyzer.py --format json --output sentiment.json -
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
| Error | Cause | Solution |
|---|---|---|
| Fear & Greed unavailable | API down | Uses cached value with warning |
| News fetch failed | Network issue | Reduces weight of news component |
| Invalid coin | Unknown symbol | Proceeds 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
Signals
- GitHub stars
- 3k
- Forks
- 396
- Last commit
- Sep 2026
Advanced
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analyzing-market-sentiment- Source
- github.com/jeremylongshore/tons-of-skills-marketplace