Individual skill
embeddings-search
Use when choosing an embedding model, chunk size, or query form, when semantic search returns irrelevant results, when adding hybrid BM25+vector or a reranker, or when a retrieval change needs a number (recall@k, nDCG, MRR). NOT operating the store — index tuning, quantization (that is `vector-db`) — nor the retrieve-to-answer loop (that is `rag`).
skills/embeddings-search/SKILL.md
Source commit d9d6bb338f66cc1e23998585e580a0ea45bbcb82
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- Last checked
- Nov 6, 2026
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Repository context
- Repository stars (not skill ratings)
- 551
- Repository last updated
- Nov 4, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- ai-coding Repository topic
- claude-code Repository topic
- cli Repository topic
- cline Repository topic
- codex Repository topic
- coding-assistant Repository topic
- cursor Repository topic
- embeddings Declared skill tag
- semantic-search Declared skill tag
- chunking Declared skill tag
- hybrid-search Declared skill tag
- reranking Declared skill tag
- rrf Declared skill tag
- retrieval-eval Declared skill tag
- mteb Declared skill tag
- matryoshka Declared skill tag
- engineering Derived category
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SKILL.mdcore · 10,810 bytesSKILL.mdcore · 10,810 bytesSKILL.mdcore · 10,810 bytesSKILL.mdcore · 10,810 bytesevals/README.mddoc · 725 bytesevals/cases.yamlasset · 3,430 bytesreferences/evaluation.mddoc · 3,240 bytesreferences/models.mddoc · 3,508 bytesscripts/verify.shruns · 5,360 bytes
Read excerpt
# embeddings-search — make and judge the vectors
You own the **embedding technique layer**: turn a corpus into searchable vectors, turn a
question into a good retrieval, and **measure whether that retrieval is any good**. You stop the
moment the right chunks come back, measured by a number. You do not assemble a prompt or
generate an answer.
Route the adjacent surfaces away:
- **Operating the store** — collection schema, HNSW/IVFFlat tuning, metadata-filter path,
quantization, `ef_search` recall knobs → [`../vector-db/SKILL.md`](../vector-db/SKILL.md).
You decide *what vectors go in and how to query*; vector-db decides *how the store holds and
serves them*.
- **The full retrieve → rerank → prompt → generate → answer loop** and its groundedness /