What social networks does Pulse MCP Stream support?

It currently focuses on the ATProto ecosystem, specifically Bluesky, utilizing its Firehose synchronization service for live data.

Can I use this skill to detect viral content?

Yes, the skill includes specialized views and logic to calculate engagement velocity, helping you identify viral posts based on interactions per minute.

Is it compatible with languages other than TypeScript?

Yes, while the core MCP server is written in TypeScript, the skill provides client examples and integrations for both Python and Ruby.

What is the role of MCP in this skill?

The Model Context Protocol (MCP) acts as the communication layer, allowing Claude to interact with the streaming server and local database seamlessly.

How is the ingested data stored?

All ingested social events and engagement metrics are stored locally in a DuckDB database, allowing for high-speed SQL queries and persistent storage.

Pulse MCP Stream

Name: Pulse MCP Stream
Author: plurigrid

byplurigrid

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Analytics & Monitoring

Monitors real-time social streams via Model Context Protocol (MCP) with high-performance DuckDB persistence.

About

Pulse MCP Stream provides a robust Layer 1 infrastructure for real-time social interaction monitoring, specifically optimized for the ATProto (Bluesky) ecosystem. By leveraging the Model Context Protocol (MCP) and DuckDB, it enables Claude and other AI agents to ingest live mentions, engagement changes, and trending topics into a high-speed local database. This skill is essential for developers building cognitive surrogates or automated monitoring systems that require low-latency data ingestion, velocity tracking, and historical persistence for complex social network analysis.

Key Features

2 GitHub stars
High-performance local persistence using DuckDB for rapid data storage
Automated engagement velocity tracking for likes, reposts, and replies
Multi-language support with TypeScript, Python, and Ruby client integrations
Real-time ATProto/Bluesky firehose subscription and event filtering
Trend detection and keyword frequency analysis within social networks

Use Cases

Analyzing trending topics and network patterns for automated market research
Building real-time alert systems for social media mentions and brand monitoring
Tracking the virality and engagement velocity of specific posts as they happen

About

Key Features

2 GitHub stars
High-performance local persistence using DuckDB for rapid data storage
Automated engagement velocity tracking for likes, reposts, and replies
Multi-language support with TypeScript, Python, and Ruby client integrations
Real-time ATProto/Bluesky firehose subscription and event filtering
Trend detection and keyword frequency analysis within social networks

Use Cases

Analyzing trending topics and network patterns for automated market research
Building real-time alert systems for social media mentions and brand monitoring
Tracking the virality and engagement velocity of specific posts as they happen