twitter-stock-sentiment
Analyze Twitter/X sentiment for stocks using $cashtags. Track mentions, sentiment scores, influencer activity, and trending discussions for any ticker.
Best use case
twitter-stock-sentiment is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Analyze Twitter/X sentiment for stocks using $cashtags. Track mentions, sentiment scores, influencer activity, and trending discussions for any ticker.
Teams using twitter-stock-sentiment should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/twitter-stock-sentiment/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How twitter-stock-sentiment Compares
| Feature / Agent | twitter-stock-sentiment | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Analyze Twitter/X sentiment for stocks using $cashtags. Track mentions, sentiment scores, influencer activity, and trending discussions for any ticker.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Twitter Stock Sentiment Analyzes Twitter/X sentiment for stocks using the bird CLI and $cashtag tracking. ## Features - **Mention Volume:** Track tweet counts for any $TICKER - **Sentiment Analysis:** Bull/neutral/bear scoring using NLP - **Influencer Tracking:** Identify high-follower accounts discussing the stock - **Trending Hashtags:** Associated tags and themes - **Crisis Detection:** Spikes in negative sentiment - **7-day Trends:** Volume and sentiment changes ## Usage ```bash ./analyze.sh AAPL ./analyze.sh TSLA --days 30 ``` ## Output Format Markdown report with: - Sentiment score (-1 to +1) - Mention volume and trend - Top influencer tweets - Hashtag analysis - Notable sentiment shifts ## Requirements - bird CLI installed (`brew install steipete/tap/bird`) - Authenticated Twitter/X session ## Installation 1. Clone this repository to your skills folder 2. Install bird CLI: `brew install steipete/tap/bird` 3. Authenticate with Twitter/X: `bird auth` 4. Install Python dependencies: `pip install -r requirements.txt` ## How It Works 1. `analyze.sh` fetches recent tweets mentioning the $TICKER using bird CLI 2. `sentiment.py` performs NLP analysis on tweet text using VADER 3. Results are aggregated and formatted as a Markdown report 4. Influencer tracking identifies high-follower accounts 5. Hashtag extraction reveals trending themes ## Configuration Edit `analyze.sh` to customize: - Number of tweets to fetch (default: 100) - Time window (default: 7 days) - Sentiment thresholds for crisis detection
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