search-infralisted

Design and operate full-text and vector search infrastructure — inverted-index engines (Elasticsearch/OpenSearch, Meilisearch, Typesense), analyzers and tokenization, relevance tuning (BM25, boosting, synonyms), faceting, and semantic/vector search (embeddings, ANN indexes, hybrid retrieval). Use when adding a search box, choosing a search engine, designing an index mapping and analyzer chain, tuning relevance, building autocomplete, deciding keyword vs vector vs hybrid retrieval, or keeping a search index in sync with the source database. Boundary vs db-design — db-design owns the durable transactional source of truth (normalized PostgreSQL schema, known-key indexes, migrations, ACID); this skill owns the derived denormalized search index built FROM that source for ranked free-text/semantic retrieval, where the engine is eventually-consistent, rebuildable, and never the system of record. Defers cache concerns to redis and RAG prompt assembly to llm-patterns.
kouroshez/coding-os · ★ 4 · AI & Automation · score 76

Install: claude install-skill kouroshez/coding-os

# Search Infrastructure — Ranked Retrieval Done Right A practical guide to building search that returns the *relevant* result, not just a matching row. Covers classic full-text (inverted index, BM25) and modern semantic (embeddings, vector ANN) retrieval, and the index-sync discipline that keeps either honest. Stack-agnostic; recipes target Elasticsearch/OpenSearch, Meilisearch, Typesense, and pgvector/Qdrant as the reference engines. ## When to Use This Skill - Adding a search box, autocomplete, or "find similar" to a product. - Choosing a search engine — managed Elastic vs Meilisearch vs Typesense vs Postgres FTS vs a vector DB. - Designing an index mapping: which fields are searchable, which are filters, which analyzer. - Tuning relevance — results are "technically matching but useless", boosting, synonyms, typo tolerance. - Deciding keyword vs vector vs hybrid retrieval for a given query distribution. - Keeping the search index consistent with the database that owns the data. Skip when: the lookup is by exact key / known field on a small set — that is a database index (`WHERE id = ?`), see db-design, not a search engine. Search earns its complexity only for ranked, fuzzy, or free-text retrieval. ## The Index Is Derived, Never the Source of Truth The single most important rule: **the search index is a denormalized, rebuildable projection of data that lives authoritatively elsewhere** (the transactional DB). It is eventually consistent and disposable. - Never write u