open-notebook
by K-Dense-AIOpen Notebook is a self-hosted, open-source research workspace for document analysis, notes, chat with sources, search, and podcast-style summaries. Use the open-notebook skill to organize notebooks, ingest PDFs, web pages, audio, video, and Office files, and support private, API-first workflows for Data Analysis.
This skill scores 78/100, which means it is a solid listing candidate for directory users. The repository shows a real, self-hosted research workflow with clear triggers, API-backed operations, and enough implementation detail for an agent to act with less guesswork than a generic prompt, though installation still requires external setup knowledge.
- Strong triggerability: the frontmatter clearly says when to use it, including notebooks, source ingestion, summaries, chat, search, and podcast generation.
- Good operational depth: the repo includes a detailed REST API reference plus example scripts for notebook management, source ingestion, and chat interactions.
- Useful install decision value: it documents self-hosting, multiple AI providers, and privacy-oriented behavior, helping users judge fit quickly.
- No install command in SKILL.md, so users must figure out deployment and wiring themselves from the supporting docs.
- The skill is infrastructure-heavy and depends on Docker, SurrealDB, and environment configuration, which may be too much for lighter-weight use cases.
Overview of open-notebook skill
What open-notebook does
The open-notebook skill helps you set up and use a self-hosted research workspace for document analysis, note generation, chat with sources, search, and podcast-style summaries. It is best for users who want NotebookLM-style workflows without sending material to a third-party SaaS.
Who should install it
Install the open-notebook skill if you manage research-heavy workflows, need private handling of PDFs, web pages, audio, video, or Office files, or want an API-first system that can be automated. It fits technical users, research teams, and builders who care about data control and repeatable ingestion.
Why it stands out
The main differentiators are self-hosting, a REST API, and broad model support across providers like OpenAI, Anthropic, Google, Ollama, Groq, and Mistral. For open-notebook for Data Analysis, the value is not just chat: it is organizing evidence into notebooks, then querying and transforming that evidence with full-text and vector search.
How to Use open-notebook skill
Install and read the right files first
For open-notebook install, add the skill in your Claude skill workflow, then start with SKILL.md. Next read references/configuration.md, references/api_reference.md, references/examples.md, and references/architecture.md. If you plan to automate, inspect scripts/source_ingestion.py, scripts/notebook_management.py, and scripts/chat_interaction.py before writing prompts.
Turn a rough goal into a useful prompt
Good inputs name the notebook purpose, source types, output format, and constraints. For example: “Create a notebook for quarterly market research, ingest 12 PDFs and 5 URLs, summarize key findings, extract disagreements, and draft a source-backed briefing.” That is better than “analyze these files” because open-notebook needs scope and output expectations to choose the right workflow.
Practical workflow that produces better results
Use this open-notebook guide order: create a notebook, ingest sources, verify processing status, then ask for notes, summaries, chat answers, or transformations. If you need automation, mirror the API examples in the scripts/ folder and keep the prompt aligned with what the backend actually supports, especially notebook IDs, source IDs, and async processing.
Inputs that materially improve output
Provide source list, desired notebook structure, model preference if you have one, and any privacy or deployment constraints. Be explicit about whether you want synthesis, comparison, extraction, or a question-answering pass. If you are using open-notebook on mixed media, say which sources are authoritative so the model does not over-weight low-quality material.
open-notebook skill FAQ
Is open-notebook only for local research?
No. It is strongest for local or self-hosted research, but the API and provider flexibility make it useful in team environments too. If you need complete data sovereignty, open-notebook is a better fit than a generic prompt over uploaded files.
How is it different from a normal prompt?
A normal prompt can summarize text once. The open-notebook skill is designed for an ongoing workflow: notebooks, sources, searchable context, chat sessions, and repeatable ingestion. That matters when your task is larger than a one-off answer.
When should I not use it?
Skip open-notebook if you only need a quick summary of one short document, if you cannot run a Docker-based stack, or if you do not need persistent notebooks and source tracking. It is also a poor fit if you want a no-setup consumer app rather than a self-hosted system.
Is it beginner friendly?
It is usable for beginners who follow the configuration steps, but it is more effective for users comfortable with Docker, environment variables, and API-driven tools. Beginners should start with a single notebook and a small source set before scaling up.
How to Improve open-notebook skill
Give the skill a narrower research target
The best open-notebook usage starts with a focused question, not a broad topic. “Compare these five clinical trial reports and surface safety concerns” will outperform “research this area” because the notebook can organize evidence around one decision.
Supply source quality and priority rules
Tell the system which sources are primary, which are supporting, and which should be ignored if they conflict. This reduces weak synthesis and helps the skill handle mixed material, especially in open-notebook for Data Analysis workflows where source quality drives the final answer.
Watch for the common failure modes
The main risks are vague notebook goals, too many unrelated sources, and unclear output format. If the first result is too generic, tighten the prompt with an audience, a decision to support, and a required structure such as bullets, table, or executive summary.
Iterate with notebook-aware follow-ups
After the first pass, ask for a second output that is more specific: “extract only claims with citations,” “compare notes across sources,” or “rewrite this into a concise brief for non-technical stakeholders.” Iterating inside the notebook usually produces better results than starting over with a broader prompt.