Skill UI

Build and operate Retrieval-Augmented Generation (RAG) systems using vector stores, embedding pipelines, and hybrid search. This skill covers ingesting documents, generating embeddings, storing in vector databases, and serving grounded LLM responses. It includes setting up embedding pipelines, vector store infrastructure, and deploying hybrid search (dense + sparse) to reduce hallucinations and improve semantic search over large document collections.

This skill generates Robot Framework tests using keyword-driven syntax with Python. It supports SeleniumLibrary for web UI testing and RequestsLibrary for API testing. Users can define custom keywords and create data-driven test cases. Ideal for automation engineers needing structured test scripts for web applications and backend services. Supports cloud integration and CI/CD pipelines.

Provides an auditable framework for conditional asset locking, timelock boundary verifications, and multi-signature release triggers on Stellar, with standardized data models and verification schemas for integration into CI pipelines.

Prove data/AI workflows end-to-end on tiny samples, find cost-saving wins, then scale within budget using cost-gated steps. Ideal for multi-stage pipelines involving paid compute like LLM tokens or warehouse credits.

Learn how to configure xberg's --format and --content-format flags to optimize document extraction for different consumers. Covers LLM context, RAG indexing, JSON parsing, and token reduction strategies for efficient data pipelines.