qdrant-vector-search

21.8k

davila7

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

vector-database-engineer

18.0k

sickn33

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

qdrant-integration

376

a5c-ai

Qdrant vector database with filtering, payloads, and quantization support

mcp-tool-selection

349

Context Engine Ai Context Engine Mcp Tool Selection

Decision rules for when to use MCP Qdrant-Indexer semantic search vs grep/literal file tools. Use this skill when starting exploration, debugging, or answering "where/why" questions about code.

using-vector-databases

296

ancoleman

Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.

building-rag-systems

150

panaversity

Build production RAG systems with LangChain orchestration and Qdrant vector store. Covers 8 RAG architectures (Simple, HyDE, CRAG, Self-RAG, Agentic), document processing, semantic chunking, retrieval chains, and evaluation with LangSmith/RAGAS. Use when implementing RAG pipelines, semantic search, or AI knowledge systems. NOT for simple keyword search.

qdrant

128

giuseppe-trisciuoglio

Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.

vector-search

46

datadrivenconstruction

Implement semantic vector search for construction data. Build AI-powered search using embeddings and vector databases (Qdrant, ChromaDB) for intelligent querying of specifications, standards, and project documents.

cwicr-data-loader

46

datadrivenconstruction

Load and parse DDC CWICR construction cost database from multiple formats: Parquet, Excel, CSV, Qdrant snapshots. Foundation for all CWICR operations.

qdrant

40

vm0-ai

Qdrant vector database REST API via curl. Use this skill to store, search, and manage vector embeddings.

semantic-search

29

omer-metin

Build production-ready semantic search systems using vector databases, embeddings, and retrieval-augmented generation (RAG). Covers vector DB selection (Pinecone/Qdrant/Weaviate), embedding models (OpenAI/Voyage/Cohere), chunking strategies, hybrid search, and reranking for high-quality retrieval. Use when ", vector-search, embeddings, rag, pinecone, qdrant, weaviate, llama-index, langchain, hybrid-search, reranking" mentioned.

Vector Specialist

29

omer-metin

Embedding and vector retrieval expert for semantic search. Use when "vector search, embeddings, semantic search, qdrant, pgvector, similarity search, reranking, hybrid retrieval, embeddings, vector-search, qdrant, pgvector, semantic-search, retrieval, reranking, ml-memory" are mentioned.

building-rag-systems

22

mjunaidca

Build production-grade RAG systems with semantic chunking, incremental indexing, and filtered retrieval. Use when implementing document ingestion pipelines, vector search with Qdrant, or context-aware retrieval. Covers chunking strategies, change detection, payload indexing, and context expansion. Not intended for simple similarity searches without production requirements.

start-pixel-detective

15

rm2thaddeus

Start, stop, or check Pixel Detective services on this machine. Use when the user asks to start Pixel Detective, start backend only, start full stack, restart, stop, or check Docker, Qdrant, ML, UMAP, or frontend status, or to provide a specific Pixel Detective page URL.

pixel-detective-exports

15

rm2thaddeus

Export Pixel Detective UMAP projections with HDBSCAN and manage Qdrant collections via the ingestion API. Use when the user asks for latent space exports, UMAP clustering assets, or collection operations (list/create/select/delete/merge).

grepai-storage-qdrant

14

yoanbernabeu

Configure Qdrant vector database for GrepAI. Use this skill for high-performance vector search.

vector-db-setup

12

patricio0312rev

Sets up vector databases for semantic search including Pinecone, Chroma, pgvector, and Qdrant with embedding generation and similarity search. Use when users request "vector database", "semantic search", "embeddings storage", "Pinecone setup", or "similarity search".

vector-database-ops

10

BagelHole

Deploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.

qdrant

9

TerminalSkills

Qdrant is an open-source vector similarity search engine with advanced filtering. Learn to create collections, upsert points with payloads, query with filters, and deploy with Docker for AI and semantic search applications.

Vector Database Expert

7

willsigmon

Vector databases - Pinecone, Weaviate, Chroma, Qdrant for RAG and semantic search

vector-database-engineer

5

agent-skills-hub

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

qdrant-rag-implementation

5

MuhammedSuhaib

This skill provides guidance for implementing robust Qdrant vector database integration with RAG (Retrieval Augmented Generation) systems, including proper async client handling, error management, and performance optimization.

setup-guide

5

bug-ops

Zeph configuration reference. Use when the user asks about setup, configuration, environment variables, TOML settings, or how to enable specific features like Telegram, Qdrant, or A2A.

vector-database-engineer

4

ngxtm

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

inspecting-rag-catalog

4

gitwalter

Inspect and list the contents of the Qdrant RAG ebook library

n8n-legal-config

2

fbmoulin

Specialized n8n Cloud node configuration for legal AI workflows. Use when Claude needs AI Agent nodes, LangChain, OpenAI, Anthropic, Vector Stores (Pinecone, Supabase, Qdrant), HTTP Request, Code, Webhook, or PostgreSQL configured for legal automation. Includes optimized parameters for legal document processing, FIRAC analysis, case-law research, and RAG with Brazilian legislation.

vector-database-engineer

1

rootcastleco

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

qdrant-edge

1

gururaser

Expert guide for building applications with Qdrant Edge — the embedded, offline-capable vector search engine for edge devices (robots, kiosks, mobile phones, IoT, home assistants). Use this skill whenever the user mentions Qdrant Edge, qdrant-edge-py, EdgeShard, on-device vector search, offline vector search, embedded vector database, edge AI, or wants to synchronize Qdrant data between a device and a server. Also trigger when the user asks about running vector search without internet connectivity, on-device semantic search, or integrating FastEmbed with Qdrant on resource-constrained devices.

grepai-storage-qdrant

1

NNIIKKKKII

Configure Qdrant vector database for GrepAI. Use this skill for high-performance vector search.

qdrant-vector-search

AXGZ21

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

building-rag-systems

Asmayaseen

Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval. Use when implementing document ingestion pipelines, vector search with Qdrant, or context-aware retrieval. Covers chunking strategies, change detection, payload indexing, and context expansion. NOT when doing simple similarity search without production requirements.

cwicr-data-loader

jdmorag97-rgb

Load and parse DDC CWICR construction cost database from multiple formats: Parquet, Excel, CSV, Qdrant snapshots. Foundation for all CWICR operations.

vector-database-engineer

oki3505F

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

vector-database-engineer

haniakrim21

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

vector-search

jdmorag97-rgb

Implement semantic vector search for construction data. Build AI-powered search using embeddings and vector databases (Qdrant, ChromaDB) for intelligent querying of specifications, standards, and project documents.

Vector Database Engineer

reikiplanet

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