What is the Knowledge Base Builder skill?

It is a specialized capability for Claude Code that automates the creation and optimization of knowledge bases for elizaOS agents using RAG and embeddings.

Can I measure the performance of my knowledge base?

Yes, it includes Phase 8: Quality Metrics, which helps assess total chunks, embedding coverage, query latency, and search relevance.

Which file formats does it support?

The skill supports document ingestion from various formats including Markdown, PDF, and plain text files for agent knowledge.

How does it handle document chunking?

It provides multiple strategies: Fixed Size for simplicity, Semantic for context-aware splitting, and Sliding Window for comprehensive overlapping coverage.

Does it integrate directly with elizaOS characters?

Yes, it generates character configuration code including knowledge paths, embedding settings, and necessary elizaOS plugins.

Knowledge Base Builder

Name: Knowledge Base Builder
Author: Dexploarer

byDexploarer

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데이터 과학 및 ML

Creates and optimizes elizaOS knowledge bases using RAG, smart chunking strategies, and semantic search integration.

소개

The Knowledge Base Builder skill streamlines the creation of production-ready Retrieval-Augmented Generation (RAG) systems for elizaOS agents. It automates the entire knowledge lifecycle, from ingesting Markdown, PDF, and text documents to configuring vector storage and optimizing retrieval settings like TopK and similarity thresholds. By providing specialized workflows for semantic chunking and embedding generation, this skill ensures AI agents possess deep, domain-specific expertise with high-performance retrieval and organized knowledge structures.

주요 기능

Advanced semantic, fixed-size, and sliding-window chunking strategies
Automated embedding generation and vector storage configuration
Knowledge quality assessment with latency and relevance metrics
Multi-format document ingestion including Markdown, PDF, and text
2 GitHub stars
Native elizaOS character integration and plugin setup

사용 사례

Implementing semantic search capabilities for technical documentation
Building a domain-specific RAG system for an autonomous AI agent
Optimizing agent retrieval performance using pre-computed embeddings

소개

주요 기능

Advanced semantic, fixed-size, and sliding-window chunking strategies
Automated embedding generation and vector storage configuration
Knowledge quality assessment with latency and relevance metrics
Multi-format document ingestion including Markdown, PDF, and text
2 GitHub stars
Native elizaOS character integration and plugin setup

사용 사례

Implementing semantic search capabilities for technical documentation
Building a domain-specific RAG system for an autonomous AI agent
Optimizing agent retrieval performance using pre-computed embeddings