name: twitter-intel
description: Real-time X/Twitter intelligence - analyze accounts, track topics, and monitor keywords using live data. Use when you need current social media insights, competitor monitoring, or audience research.

Twitter Intel

Get real-time X/Twitter intelligence without API keys. Analyze accounts, track trending topics, and monitor keywords with live data from X.

When to Use This Skill

• Analyzing a Twitter/X account's recent activity and engagement
• Tracking what people are saying about a topic or hashtag
• Monitoring brand mentions or competitor activity
• Researching audience sentiment and trends
• Getting real-time social data for market research
• Finding influencers or key voices on a topic

What This Skill Does

1. Account Analysis (@username): Analyzes recent posts, engagement patterns, content style, and audience interactions
2. Topic Tracking (#topic): Monitors trending discussions, popular posts, and sentiment around hashtags
3. Keyword Monitoring ("keyword"): Tracks brand mentions, competitor activity, and industry discussions
4. Engagement Insights: Provides metrics on likes, replies, and viral potential

How to Use

Basic Usage

/twitter-intel @elonmusk

/twitter-intel #AI

/twitter-intel "artificial intelligence startups"

Natural Language

You can also use natural language:

What's @blockrunai posting about lately?

What's trending about AI agents on X?

Check Twitter for mentions of "Claude Code"

Advanced Usage

Combine multiple analyses:

/twitter-intel @competitor1 @competitor2 - compare their content strategies

/twitter-intel #Web3 - focus on posts from the last 24 hours with high engagement

Instructions

When a user requests Twitter/X intelligence, follow these steps:

1. Install Dependencies (First Time Only)

If the BlockRun SDK is not installed, install it:

pip install blockrun-llm

2. Initialize the Client

from blockrun_llm import setup_agent_wallet

client = setup_agent_wallet()

If this is the first time, the client will display a QR code for funding the wallet. The user needs to add USDC on Base network ($1-5 is enough for many queries).

3. Execute the Query

For Account Analysis (@username):

response = client.chat(
    "xai/grok-3",
    f"Analyze @{username}'s recent X/Twitter activity. Include: recent posts, engagement patterns, content themes, posting frequency, and notable interactions.",
    search_parameters={
        "mode": "on",
        "sources": [
            {
                "type": "x",
                "included_x_handles": [username],
                "post_favorite_count": 5
            }
        ],
        "max_search_results": 15,
        "return_citations": True
    }
)

For Topic/Hashtag Tracking (#topic):

response = client.chat(
    "xai/grok-3",
    f"What are people saying about #{topic} on X/Twitter right now? Include: trending discussions, popular posts, key voices, and overall sentiment.",
    search_parameters={
        "mode": "on",
        "sources": [{"type": "x", "post_favorite_count": 50}],
        "max_search_results": 20,
        "return_citations": True
    }
)

For Keyword Monitoring ("keyword"):

response = client.chat(
    "xai/grok-3",
    f"Search X/Twitter for mentions of '{keyword}'. Include: recent discussions, sentiment, key influencers mentioning this, and notable posts.",
    search_parameters={
        "mode": "on",
        "sources": [{"type": "x", "post_favorite_count": 10}],
        "max_search_results": 15,
        "return_citations": True
    }
)

4. Format the Output

Present results in a clear, actionable format:

# Twitter Intel: @username

## Overview
- **Account**: @username
- **Recent Activity**: [Summary of posting frequency]
- **Primary Topics**: [Main themes they discuss]

## Recent Highlights
1. **[Post summary]** - [engagement metrics]
   > Quote or key excerpt

2. **[Post summary]** - [engagement metrics]
   > Quote or key excerpt

## Content Analysis
- **Tone**: [Professional/Casual/Technical/etc.]
- **Engagement Rate**: [High/Medium/Low based on follower count]
- **Best Performing Content**: [What type of posts get most engagement]

## Key Insights
- [Insight 1]
- [Insight 2]
- [Insight 3]

## Sources
[Links to referenced posts]

5. Report Costs

After each query, show the cost:

spending = client.get_spending()
print(f"Query cost: ${spending['total_usd']:.4f}")

Pricing

• Per source retrieved: $0.025
• Typical query (10-20 sources): $0.25-0.50
• Account analysis: ~$0.38 (15 sources)
• Topic tracking: ~$0.50 (20 sources)

Examples

Example 1: Account Analysis

User: /twitter-intel @pmarca

Output:

# Twitter Intel: @pmarca

## Overview
- **Account**: @pmarca (Marc Andreessen)
- **Recent Activity**: Very active, 5-10 posts daily
- **Primary Topics**: AI, startups, tech policy, venture capital

## Recent Highlights
1. **Thread on AI regulation** - 2.5K likes, 400 replies
   > "The AI moment is different because..."

2. **Startup advice post** - 1.8K likes
   > "The best founders I've met..."

## Content Analysis
- **Tone**: Intellectual, contrarian, long-form threads
- **Engagement Rate**: Extremely high (10K+ avg likes)
- **Best Performing**: Controversial takes and founder advice

## Key Insights
- Consistently bullish on AI despite regulatory concerns
- Engages heavily with tech policy debates
- High influence on VC/startup community sentiment

Query cost: $0.38

Example 2: Topic Tracking

User: /twitter-intel #AIAgents

Output:

# Twitter Intel: #AIAgents

## Trending Now
- Discussions around autonomous coding assistants
- Debate on agent safety and sandboxing
- New tool launches getting attention

## Top Posts (Last 24h)
1. @developer: "Just built an agent that..." - 500 likes
2. @researcher: "The problem with current agents..." - 320 likes

## Sentiment Analysis
- **Overall**: Excited but cautious
- **Main concerns**: Safety, costs, reliability
- **Main enthusiasm**: Productivity gains, automation

## Key Voices
- @karpathy - Technical deep dives
- @swyx - Developer tooling focus
- @anthropic - Safety-focused takes

Query cost: $0.50

Example 3: Keyword Monitoring

User: /twitter-intel "Claude Code"

Output:

# Twitter Intel: "Claude Code"

## Mention Summary
- **Volume**: Moderate, growing steadily
- **Sentiment**: Very positive
- **Context**: Mostly developer reviews and tips

## Notable Mentions
1. @dev_influencer: "Claude Code just saved me 3 hours..." - 200 likes
2. @techreview: "Comparing Cursor vs Claude Code..." - 150 likes

## Common Themes
- Praise for code understanding
- Questions about pricing
- Comparisons to Cursor, Copilot

## Recommendations
- Engage with comparison discussions
- Address pricing questions proactively
- Amplify positive developer testimonials

Query cost: $0.38

Tips

• Reduce costs: Use max_search_results: 5 for quick checks
• Increase depth: Use max_search_results: 30 for comprehensive analysis
• Filter by engagement: Increase post_favorite_count to focus on viral content
• Date filtering: Add from_date and to_date for time-specific analysis

Requirements

• BlockRun SDK: pip install blockrun-llm
• Wallet: Auto-created on first use, fund with USDC on Base
• Minimum balance: $0.50 recommended for a few queries

Related Use Cases

• Competitive intelligence gathering
• Influencer identification for marketing campaigns
• Real-time crisis monitoring
• Product launch sentiment tracking
• Industry trend analysis