Localized RAG and Notebook injection via MCP
npx skills add https://github.com/LongLeo287/OmniClaw --skill notebooklm-mcpKopieren Sie diesen Befehl und fügen Sie ihn in Claude Code ein, um den Skill zu installieren
Multi-domain skill reference collection covering ai-integration, databases, finance, frontend, google-workspace, and POS systems. Each domain contains reference docs, best practices, and agent skill blueprints.
Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.
Build applications using Azure App Configuration SDK for JavaScript (@azure/app-configuration). Use when working with configuration settings, feature flags, Key Vault references, dynamic refresh, or centralized configuration management.
Assess and migrate cross-cloud workloads to Azure with migration reports and code conversion guidance. Supports AWS, GCP, and other providers. WHEN: migrate Lambda to Azure Functions, migrate AWS to Azure, Lambda migration assessment, convert AWS serverless to Azure, migration readiness report, migrate from AWS, migrate from GCP, cross-cloud migration.
Master Go concurrency with goroutines, channels, sync primitives, and context. Use when building concurrent Go applications, implementing worker pools, or debugging race conditions.
Master Godot 4 GDScript patterns including signals, scenes, state machines, and optimization. Use when building Godot games, implementing game systems, or learning GDScript best practices.
| name | NotebookLM MCP |
| description | Localized RAG and Notebook injection via MCP |
| tier | 3 |
| category | mcp-server |
| department | rd |
| status | active |
| source | Batch 04 (Vetting) |
| tags | ["mcp","notebooklm","local-rag","data-injection"] |
| ingested | "2026-04-05T00:00:00.000Z" |
Expose targeted local document clusters directly to the LLM agent via MCP endpoints.
Useful for quickly analyzing specific PDF/MD bundles without loading them into the global LightRAG cache.
