Skill Seekers is a toolset for generating large language model knowledge bases, featuring a multi-source content scraper and a dedicated RAG data pipeline. It extracts technical data from documentation, code, and video to create structured assets and configuration files for AI-powered IDE extensions.
The project distinguishes itself through the ability to transform raw data into polished tutorials and specialized skills for AI plugin marketplaces. It utilizes abstract syntax tree parsing and optical character recognition to analyze GitHub repositories, PDFs, and video frames, converting these diverse inputs into token-optimized segments for retrieval augmented generation.
The system covers a broad range of capabilities, including headless browser rendering for single page applications, automated knowledge refinement workflows, and CI/CD integration for scheduled asset updates. It also provides protocol-based tool exposure, allowing AI agents to autonomously manage data ingestion and packaging pipelines.
The tool includes diagnostics for system health and incorporates security scanning to detect prompt injection patterns within scraped content.
