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embedding-strategies

Select and optimize embedding models for semantic search and RAG applications

embedding-strategies is an agent skill from the wshobson/agents collection for choosing and tuning embedding models for semantic search and retrieval-augmented generation (RAG) applications. Embeddings turn text into vectors so that similar meanings sit close together, and the quality of that step decides how well a search or RAG system finds the right passages. The skill includes a comparison of embedding models as they stand in 2026 to help pick one for a given job, and it covers the decisions that shape results: how text is split into chunks, whether embeddings should be fine-tuned for a specific domain, how to compare the performance of candidate models, and how to reduce embedding dimensions to save storage and speed up search. It also covers multilingual content, where the choice of model matters more because a model may perform well in one language and poorly in another. The skill applies when choosing embedding models for RAG, optimizing chunking, fine-tuning embeddings for a domain, comparing model performance, reducing dimensions and handling multilingual content. It is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 11.2K installs and passing results from three security audits at the time of the scan.