AI & LLM Engineering · Qdrant · For developers

Set up, index, and manage SocratiCode codebase indexing. An agent skill from giancarloerra/SocratiCode.

giancarloerra/SocratiCode

Explore and understand codebases using SocratiCode semantic search, dependency graphs, and context artifacts.

giancarloerra/SocratiCode

Guide to using and contributing cognee community packages: database adapters, data-source connectors, custom tasks and retrievers, and Keywords AI observability.

topoteretes/cognee

Read and update global application settings: system prompts, thinking budget, IP filters, payload rules, combo defaults, and require-login…

diegosouzapw/OmniRoute

Explains how to run Qdrant, a Rust vector database, for RAG and semantic search, covering collections, points, distance metrics and filtered or batched queries.

Orchestra-Research/AI-Research-SKILLs

Diagnose, troubleshoot, and advise on any Qdrant deployment by loading the latest official Qdrant skills live from skills.qdrant.tech.

qdrant/skills

Mem0 memory bridge for AI Girlfriend — search/read/write long-term memories from Qdrant vector DB.

momori777/Artemis

Build retrieval-augmented generation systems: pick a vector database and embedding model, choose retrieval and reranking strategies, and start from a LangGraph pipeline.

wshobson/agents

Designs retrieval-augmented generation systems: document chunking, embeddings, vector store setup, hybrid search, reranking and retrieval evaluation, with checks at each step.

Jeffallan/claude-skills

Memory is the cornerstone of intelligent agents. An agent skill from omer-metin/skills-for-antigravity.

omer-metin/skills-for-antigravity

Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from…

elementalsouls/Claude-BugHunter

Vector database implementation for AI/ML applications, semantic search, and RAG systems.

ancoleman/ai-design-components

Security-first SOP for multi-tenant RAG systems. An agent skill from agentsope/SkillAlchemy.

agentsope/SkillAlchemy

Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications.

qdrant/skills

Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (v1.22.0+).

neo4j-contrib/neo4j-skills

RAG: embeddings, chunking, hybrid search (BM25+vector), reranking, CRAG, multi-hop.

softspark/ai-toolkit

A skill your agent uses when operating a vector store as a data layer — choosing or migrating between Pinecone, Qdrant, Weaviate and pgvector; designing a collection or index (distance metric…

ericrisco/rsc-harness

Provides Qdrant vector database integration patterns with LangChain4j.

giuseppe-trisciuoglio/developer-kit

High-performance vector search engine for production RAG — Rust-powered, horizontal scaling, hybrid dense+sparse search, metadata filtering.

AlexAI-MCP/hermes-CCC