Guide ML practitioners through analyzing, improving, and integrating embedding spaces using the EmbedKit library. Use this skill whenever the user has embedding arrays (NumPy/PyTorch), wants to assess embedding quality, diagnose geometric pathologies (hubness, anisotropy, distance concentration, intrinsic dimensionality mismatch), choose improvement strategies, apply contrastive learning to refine embeddings, or plug EmbedKit into their own ML workflows (HuggingFace, PyTorch training loops, scikit-learn pipelines, RAG systems, retrieval stacks). Trigger on: "embeddings", "hubness", "intrinsic dimension", "isotropy", "embedding space", "refine embeddings", "EmbedKit", sentence/image/word embedding quality, kNN quality, representation learning diagnostics, retrieval quality, semantic similarity, embedding geometry.
2026-05-11