embedding-set-inspectorlisted

When retrieval is poor, teams reach for a bigger model or a reranker before checking whether the embeddings themselves are sound. This skill inspects an embedding set for the failure modes that quietly wreck recall, so you fix the cause instead of layering patches on top. ## When to use this skill - Retrieval recall is low and you want to rule out the embeddings before tuning the retriever. - After re-embedding a corpus (new model, new chunking) and before promoting the index. - A subset of documents is "invisible" to search no matter the query. - Validating a freshly built index in CI before it ships. ## Instructions 1. **Confirm the basics.** Verify every vector has the **expected dimensionality** and that vectors are **normalized** if your distance metric assumes it (cosine vs. dot product vs. L2 mismatch is a classic silent bug). Flag any zero, NaN, or near-zero-norm vectors — usually empty or failed-to-embed chunks. 2. **Check for asymmetry handling.** If the model supports input types (document vs. query), confirm documents were embedded as documents and queries as queries. Mixing them degrades retrieval and is easy to get wrong. 3. **Profile the distribution.** Summarize pairwise similarity: if almost everything is highly similar to everything else, the embeddings are not discriminating (often over-large chunks or a domain mismatch). If clusters are extreme, check for duplicated or boilerplate content dominating the space. 4. **Find near-duplicates.** Detect chunks