Content Curation Python Packages

textcurator-llm-py

A new package would process user-provided text inputs, such as headlines or short descriptions, and generate structured summaries or categorizations using an LLM. It would be particularly useful for c

cogni-summary

cogni-summary efficiently generates structured non-technical summaries for productivity and cognitive science content.

iflow-mcp-youtube-mcp-server

A comprehensive Model Context Protocol (MCP) server providing real-time YouTube Data API access for AI assistants. Features 14 functions including intelligent content evaluation with technology freshness scoring for knowledge base curation.

simulacra-summarizer

A new package would provide a structured, reliable way to analyze and summarize user-provided text on topics like the simulation hypothesis, using an LLM to generate concise, pattern-validated outputs

thematic-structurizer

A new package would process user-provided text input related to historical or thematic content—such as summaries, descriptions, or analyses of topics like persuasion techniques from antiquity—and retu