domains / families / research / open-notebook
Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating multi-speaker podcasts from research, chatting with documents using context-aware AI, searching across materials with full-text and vector search, or running custom content transformations. Supports 16+ AI providers including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral with complete data privacy through self-hosting. Organizes research with the self-hosted Open Notebook alternative to NotebookLM. Supports source ingestion (PDFs, web pages, audio, video, and Office documents), cited document chat, text and vector search, notes, custom transformations, and multi-speaker podcasts. Use when automating Open Notebook through its REST API or configuring its local or cloud AI providers, including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral.
The two skills cover the same tool (Open Notebook) but diverge significantly in implementation details and API contracts. The K-Dense skill is more rigorous, pinning a specific version (v1.14.0), providing bundled Python helpers, and explicitly correcting common API misconceptions (e.g., model types are 'language' not 'llm'). The FreedomIntelligence skill is a broader overview that includes raw REST API examples for features like podcast generation and search, but it uses a potentially unstable 'main' branch reference and contains API calls that appear inconsistent with the more detailed K-Dense documentation (e.g., using 'model_ids' for registration vs 'models[]' with explicit types). Because the K-Dense skill contains critical safety/versioning constraints and helper usage that the other lacks, while the FreedomIntelligence skill provides raw endpoint examples for features (podcasts, search) that are truncated or less detailed in the K-Dense copy, neither is a complete superset. Combining them allows an agent to use the safe, versioned deployment and helper approach from K-Dense while referencing the raw API patterns from FreedomIntelligence for features not fully detailed in the K-Dense snippet.