social-media-intelligence

Name: social-media-intelligence
Availability: InStock
Author: hxhyyy

Collect and analyze financial sentiment from Twitter, Telegram, Discord, and Reddit.

Updated Jul 8, 2026

Search Tags:#social media #sentiment analysis #trading #quantitative #market signals #financial intelligence

Ready to run

npx skills add https://github.com/hxhyyy/Vibe-Trading --skill social-media-intelligence-hxhyyy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: social-media-intelligence
Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/social-media-intelligence
Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill social-media-intelligence-hxhyyy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tweepy, ntscraper, telethon, discord.py, praw, vaderSentiment, transformers, torch, pandas, numpy, scipy, scikit-learn.

What problem does it solve?

This skill solves the challenge of monitoring fragmented, high-volume social media discussions across multiple platforms to identify actionable financial signals and market sentiment.

Core Features & Use Cases

Multi-Platform Intelligence: Collects and analyzes data from Twitter/X, Telegram, Discord, and Reddit to provide a unified view of market sentiment.
Sentiment Quantification: Employs VADER, FinBERT, and LLM-based analysis to score text sentiment and detect market extremes like fear and greed.
Use Case: A trader can use this skill to monitor r/wallstreetbets and Twitter cashtags to detect abnormal buzz or sentiment shifts that precede significant price volatility.

Quick Start

Use the social-media-intelligence skill to fetch the latest sentiment analysis for the ticker AAPL from Twitter and Reddit.

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 sentiment signals from social media platforms like Twitter and Reddit?▼

To extract financial sentiment signals from social media, this skill collects discussions from Twitter, Telegram, Discord, and Reddit, then applies VADER and FinBERT NLP models to score text and detect market extremes like fear and greed.

Can I use FinBERT and VADER together for quantitative trading sentiment analysis?▼

Yes, you can use FinBERT and VADER together for quantitative trading sentiment analysis. This skill employs both models alongside LLM-based analysis to quantify text sentiment and identify abnormal buzz preceding significant price volatility.

What's the best way to monitor r/wallstreetbets and Twitter cashtags for market signals?▼

The best way to monitor r/wallstreetbets and Twitter cashtags for market signals is using this skill's multi-platform intelligence. It aggregates high-volume social media discussions to provide a unified view of real-time market sentiment shifts.

Does this sentiment analysis skill require platform-specific APIs for Telegram and Discord data collection?▼

Yes, this sentiment analysis skill requires integration with platform-specific APIs for Telegram and Discord data collection. It leverages libraries like telethon and discord.py to fetch fragmented, high-volume financial discussions across multiple platforms.

How do I quantify fear and greed market extremes from social media discussions?▼

You quantify fear and greed market extremes from social media discussions by applying the skill's sentiment scoring mechanisms. It uses transformers and LLM-based analysis on collected text to detect sentiment shifts that precede significant price volatility.

What Python dependencies do I need to run social media sentiment analysis for trading?▼

To run social media sentiment analysis for trading, you need Python dependencies including tweepy, ntscraper, telethon, discord.py, praw, vaderSentiment, transformers, torch, pandas, numpy, scipy, and scikit-learn.