skills.rest
NigarumOvum
NigarumOvumCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

social-media-intelligence

Analyze financial sentiment from social media platforms and compute fear and greed indices.

Updated May 25, 2026
Search Tags:#social media#market analysis#sentiment analysis#trading#financial signals
One-click install
npx skills add https://github.com/NigarumOvum/AutoTrading --skill social-media-intelligence-nigarumovum
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: social-media-intelligence
Source: https://github.com/NigarumOvum/AutoTrading/tree/main/Vibe-Trading/agent/src/skills/social-media-intelligence
Command: npx skills add https://github.com/NigarumOvum/AutoTrading --skill social-media-intelligence-nigarumovum

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tweepy, telethon, discord.py, praw, transformers, scikit-learn, pandas, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the process of gathering and analyzing financial signals from social media platforms, enabling traders to make informed decisions.

Core Features & Use Cases

  • Data Collection: Collects data from Twitter, Telegram, Discord, and Reddit for sentiment analysis.
  • Sentiment Analysis: Applies various sentiment scoring methods to gauge market sentiment.
  • Data Aggregation: Aggregates and computes buzz metrics and fear/greed indicators.
  • Use Case: For example, it can monitor market sentiment around a particular asset in real-time to inform trading strategies.

Quick Start

Use the social-media-intelligence skill to analyze the sentiment of Twitter posts related to the stock with ticker 'AAPL'.

Frequently Asked Questions about social-media-intelligence

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract financial signals from social media for trading analysis?▼

To extract financial signals from social media, you can collect data from Twitter, Telegram, Discord, and Reddit, and apply sentiment scoring to gauge market sentiment for your trading strategies.

How does sentiment analysis work for calculating market fear and greed indices?▼

Sentiment analysis calculates fear and greed indices by aggregating social media data from platforms like Twitter and Reddit, applying scoring methods to gauge market sentiment, and computing buzz metrics into time-series data.

Do I need API access to collect data from Twitter, Telegram, Discord, and Reddit for market analysis?▼

Yes, you need API access to collect data from Twitter, Telegram, Discord, and Reddit for market analysis. The Skill requires social media APIs and libraries like tweepy, telethon, discord.py, and praw to function.

Can I use transformers and scikit-learn for financial sentiment analysis on social media data?▼

Yes, you can use transformers and scikit-learn for financial sentiment analysis on social media data. The Skill leverages these libraries alongside pandas and numpy to process data and compute time-series metrics.

What's the best way to monitor real-time market sentiment for a specific stock ticker?▼

The best way to monitor real-time market sentiment for a specific stock ticker is to aggregate posts from platforms like Twitter using this Skill, which applies sentiment scoring to inform your trading strategies.

Related Skills

Explore More Finance & Accounting Skills→
DaddyElonMusk69DaddyElonMusk69

social-media-intelligence

Extract real-time financial sentiment signals from social media platforms.

Community
Advanced
0xZKnw0xZKnw

sentiment-analysis

Synthesize fear and greed indices, options data, and social sentiment into market indicators.

Community
Advanced
hxhyyyhxhyyy

event-driven

Generate sentiment-based trading signals from financial news and macro events.

Community
Advanced
OpenSuckerOpenSucker
15

social-media-intelligence

Extract financial signals from Twitter, Reddit, Discord, and Telegram for sentiment-driven trading.

Official
Intermediate