Databases · Retrieval-augmented generation

Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.

Orchestra-Research/AI-Research-SKILLs

Sets up FAISS for fast nearest-neighbor search over large collections of dense vectors, choosing between Flat, IVF, HNSW and product quantization indexes.

Orchestra-Research/AI-Research-SKILLs

Builds a retail product search agent on Google Cloud, from catalog ingestion into BigQuery and Vector Search to ADK scaffolding, evaluation and Cloud Run deployment.

google/adk-recipes

Search, query, and manage Weaviate vector database collections.

weaviate/agent-skills

Guides and best practices for working with Lakebase Postgres, the database behind Neon.

usenotra/notra

Shows how to use Pinecone, a managed vector database, for production RAG, semantic search and recommendations: indexes, upserts, queries, filters and namespaces.

Orchestra-Research/AI-Research-SKILLs

A skill your agent uses for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.

timescale/pg-aiguide

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

A skill your agent uses to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF).

timescale/pg-aiguide

Guides and best practices for working with Lakebase Postgres on Neon: connections, pooled vs direct, schema migrations, branching, autoscaling, scale-to-zero, instant restore, read replicas, IP…

neondatabase/agent-skills

Guides Qdrant search strategy selection. An agent skill from qdrant/skills.

qdrant/skills

Covers distance metrics, index types and tuning for similarity search on vector databases, from semantic search to RAG retrieval.

wshobson/agents

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

Python guidance for the Azure AI Search SDK covering vector, hybrid and semantic search, index management and indexers, with Entra ID authentication preferred over keys.

microsoft/skills

Use CortexDB for local-first AI memory, vector search, RAG, knowledge graphs, SPARQL/RDFS/SHACL, corpus-to-graph workflows, external structured-data import (CSV / SQL dumps), and MCP/tool calling.

liliang-cn/cortexdb

A skill your agent uses for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations.

timescale/pg-aiguide

Redis Search guidance covering FT.CREATE schema design, field type selection (TEXT, TAG, NUMERIC, GEO, GEOSHAPE, VECTOR, JSON path), DIALECT 2 query syntax, FT.SEARCH / FT.AGGREGATE / FT.HYBRID…

redis/agent-skills

Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions.

mongodb/agent-skills

Oracle Database guidance for SQL, PL/SQL, SQLcl, ORDS, Oracle Vector SDK, administration, app development, performance, security, migrations, and agent-safe database workflows.

oracle/skills

Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies

RightNow-AI/openfang

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

Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and…

redis/agent-skills

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

ancoleman/ai-design-components

Guides Qdrant search strategy selection. An agent skill from github/awesome-copilot.

github/awesome-copilot

A skill your agent uses when working with OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches.

cinience/alicloud-skills

Diagnoses and improves Qdrant search relevance. An agent skill from github/awesome-copilot.

github/awesome-copilot

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend.

github/awesome-copilot

Azure AI Search SDK for Python. An agent skill from aiskillstore/marketplace.

aiskillstore/marketplace

Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search.

google/skills

Agent RAG and long-term memory with Pinecone. An agent skill from Luciole-Studio/Misaka-Agent.

Luciole-Studio/Misaka-Agent

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

agentsope/SkillAlchemy

Managed vector DB for production RAG and search. An agent skill from Luciole-Studio/Misaka-Agent.

Luciole-Studio/Misaka-Agent

Vector search engine for production RAG systems. An agent skill from Luciole-Studio/Misaka-Agent.

Luciole-Studio/Misaka-Agent

A skill your agent uses when setting up Node 24+ built-in node:sqlite with the loadable sqlite-vec extension for vector / RAG storage in TypeScript without better-sqlite3, or when debugging vec0…

mizchi/skills

Build NodeTool document ingestion, vector indexing, retrieval, and RAG pipelines.

nodetool-ai/nodetool

Expanding the candidate pool via relevance feedback, as an alternative to reranking when a dense retriever is too weak.

qdrant/skills

Guides building on Qdrant Edge, the embedded in-process shard.

qdrant/skills

Explains hybrid search in Qdrant. An agent skill from qdrant/skills.

qdrant/skills

Fusing scores from multiple searches into a single ranked result (RRF, DBSF, custom fusion).

qdrant/skills

Constructing prefetch queries for hybrid retrieval, including sparse/dense and multi-field setups, and choosing a sparse embedding model.

qdrant/skills

Optimize accuracy for RAG (Retrieval-Augmented Generation) systems.

LeoYeAI/openclaw-master-skills

Diagnoses and improves Qdrant search relevance. An agent skill from qdrant/skills.

qdrant/skills

Provides configuration patterns for LangChain4J vector stores in RAG applications.

giuseppe-trisciuoglio/developer-kit

Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval.

aiskillstore/marketplace

Managed vector database for production RAG — serverless and pod-based deployment, hybrid search, namespaces, and metadata filtering.

AlexAI-MCP/hermes-CCC

MindsDB MCP服务器交互技能，用于通过自然语言查询和操作200+企业级数据源。当用户需要查询数据库、分析数据、创建AI模型、连接数据源（MySQL、PostgreSQL、MongoDB、Excel、CSV、Gmail、Slack等）、执行SQL查询、进行数据预测、构建知识库（RAG）、智能问答、文档检索或任何与数据库交互的任务时使用此技能。即使没有明确提到MindsDB，只要涉及数据库操…

LeoYeAI/openclaw-master-skills

Design RAG pipelines: chunking, retrieval evaluation, and architecture.

borghei/Claude-Skills