By rohitg00
Generate vector embeddings from text data using OpenAI, Cohere, or local models, store them in a vector database with indexing, and perform semantic similarity searches to retrieve top-K matches with scores, metadata, re-ranking, and deduplication.
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npx claudepluginhub rohitg00/awesome-claude-code-toolkit --plugin embedding-managerPersistent memory for AI coding agents -- captures tool usage, compresses via LLM, injects context into future sessions. 12 hooks, 41 MCP tools, 4 skills, real-time viewer.
Complete AI coding workflow system. Self-correcting memory + persistent FTS5-indexed research wikis + auto-research loop + multi-LLM council on a single SQLite store. 33 skills, 8 agents, 22 commands, 37 hook scripts across 24 events. Cross-agent via SkillKit.
Complete developer toolkit for Claude Code
GitHub issue triage, creation, and management
Google Cloud Platform service configuration and deployment
Pinecone vector database integration. Streamline your Pinecone development with powerful tools for managing vector indexes, querying data, and rapid prototyping. Use slash commands like /quickstart to generate AGENTS.md files and initialize Python projects and /query to quickly explore indexes. Access the Pinecone MCP server for creating, describing, upserting and querying indexes with Claude. Perfect for developers building semantic search, RAG applications, recommendation systems, and other vector-based applications with Pinecone.
Google Gemini embeddings API (gemini-embedding-001) for RAG and semantic search. Use for vector search, Vectorize integration, or encountering dimension mismatches, rate limits, text truncation.
Agent skills for Qdrant vector search: scaling, performance optimization, search quality, monitoring, deployment, model migration, version upgrades, and SDK usage
Build Retrieval-Augmented Generation pipelines
Knowledge base with semantic search, document storage, and automatic summarization. Perfect for domain-specific knowledge management.
Weaviate plugin for Claude Coding