Agent skill · Databases

vector-database-engineer

Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill vector-database-engineer-dexsdevelopers-site-financeiro --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 2 KB
Bundled scripts: none
Path: skills/ai-llm/vector-database-engineer-dexsdevelopers-site-financeiro/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Vector Database Engineer Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems. ## Do not use this skill when - The task is unrelated to vector database engineer - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Capabilities - Vector database selection and architecture - Embedding model selection and optimization - Index configuration (HNSW, IVF, PQ) - Hybrid search (vector + keyword) implementation - Chunking strategies for documents - Metadata filtering and pre/post-filtering - Performance tuning and scaling ## Use this skill when - Building RAG (Retrieval Augmented Generation) systems - Implementing semantic search over documents - Creating recommendation engines -

What's inside
Steps it walks through
  1. Do not use this skill when
  2. Instructions
  3. Capabilities
  4. Use this skill when
  5. Workflow
  6. Best Practices
Ships with 1 file
  • metadata.json
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About this skill
What does the vector-database-engineer skill do?

Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill vector-database-engineer-dexsdevelopers-site-financeiro --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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