llm-pipeline
CommunityTransform messages into structured knowledge with AI.
Data & Analytics#embeddings#llm#rag#semantic-search#message-analysis#knowledge-extraction#pydantic-ai
Authordjimontyp
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill solves the challenge of extracting meaningful, structured knowledge from unstructured message streams like Telegram chats, turning conversational data into actionable insights.
Core Features & Use Cases
- Intelligent Message Scoring: Automatically evaluates message importance using multiple factors to filter noise.
- Knowledge Extraction: Uses Pydantic-AI agents to identify topics and atomic knowledge units from relevant content.
- Semantic Search: Builds RAG-enabled context for intelligent information retrieval.
- Use Case: Imagine monitoring a busy team chat channel. Use this Skill to automatically identify important discussions, extract key insights, and build a searchable knowledge base from the most valuable conversations.
Quick Start
Use the llm-pipeline skill to analyze the latest 50 messages from our team channel and extract key topics and insights.
Dependency Matrix
Required Modules
pydantic-aiopenaiollama
Components
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: llm-pipeline Download link: https://github.com/djimontyp/task-tracker/archive/main.zip#llm-pipeline Please download this .zip file, extract it, and install it in the .claude/skills/ directory.