embeddings plugins

by Jeremy Longshore

Professional AI/ML Engineering toolkit: Prompt engineering, LLM integration, RAG systems, AI safety with 12 expert plugins

by Intent Solutions

Knowledge base with semantic search, document storage, and automatic summarization. Perfect for domain-specific knowledge management.

by Jeremy Longshore

Firebase platform expert for Firestore, Auth, Functions, and Vertex AI integration

by Jeremy Longshore

Firebase platform expert for Firestore, Auth, Functions, and Vertex AI integration

by Jeremy Longshore

Professional AI/ML Engineering toolkit: Prompt engineering, LLM integration, RAG systems, AI safety with 12 expert plugins

by Intent Solutions

Knowledge base with semantic search, document storage, and automatic summarization. Perfect for domain-specific knowledge management.

by Jeremy Longshore

Professional AI/ML Engineering toolkit: Prompt engineering, LLM integration, RAG systems, AI safety with 12 expert plugins

by Intent Solutions

Knowledge base with semantic search, document storage, and automatic summarization. Perfect for domain-specific knowledge management.

by Jeremy Longshore

Firebase platform expert for Firestore, Auth, Functions, and Vertex AI integration

by Pixeltable

Build multimodal AI applications with Pixeltable.

by SwarmClaw AI

Local-first RAG knowledge vault. Compile raw sources into a durable markdown wiki with a knowledge graph and hybrid SQLite FTS plus embeddings. Ships an MCP server and slash commands for compile, query, and graph.

by Venice.ai

Official agent skills for the Venice.ai API — private-by-default inference for chat, images, video, audio, embeddings, characters, billing, wallet (x402), and crypto RPC.

by Matt Corbett

AI / RAG Engineering specialist team — agents (rag-architect-lead, retrieval-eval-analyst, ingestion-chunking-specialist, llm-serving-cost-specialist), skills, templates, commands, an advisory hook, best-practice rules, and a research-grounded knowledge bank. A RAG team for an ML engineer or AI product lead accountable for answer quality and serving cost — it fixes retrieval before generation, treats chunking as a retrieval decision, evals before it ships, and reads context-window and token economics. Inherits ravenclaude-core protocols.

by Matt Corbett

Recommendation-systems-engineering team — the RECSYS layer that turns interaction data into ranked recommendations. agents (recsys-architect, recsys-implementation-engineer) covering approach selection (popularity baseline, collaborative filtering, content-based, hybrid, two-tower/embedding retrieval, sequential), the candidate-generation → ranking → re-ranking pipeline, cold-start (new user/new item), offline evaluation (recall@k, nDCG, MAP, coverage/diversity) AND online A/B, feature stores with train/serve parity, low-latency serving, and feedback-loop/position bias. skills, Mermaid-backed knowledge docs, templates. Seams: training infra → ml-engineering; keyword/semantic search → search-relevance-engineering; A/B + stats → experimentation-growth-engineering + applied-statistics. House line: baseline before a neural net; offline wins must survive an online A/B. Requires ravenclaude-core@>=0.7.0.

by Matthew Badali

Local models on your own GPU via Ollama, in two halves: three MCP tools that index a repo or folder into an on-disk vector store, search it, and answer from it inside your current session; and a `localgpu` shell/proxy that launches a separate Claude Code session running entirely on a local chat model, through a proxy translating the Anthropic Messages API into Ollama's chat API. Six commands cover setup, health checks, indexing, search, ask, and an honest account of which crew roles a local 7B can and cannot take over. No hooks and no agents; nothing leaves 127.0.0.1.

by Somtech

Déploiement et documentation du Somtech RAG Service. Provisionne une instance par client × environnement (dev/staging/prod) sur Fly.io + Supabase, et fournit la connaissance d'architecture et d'usage.