Open Notebook

What it does

When your agent uses it

• Organizing PDFs, web pages and videos for a research project into notebooks
• Chatting with a set of documents on your own infrastructure
• Generating a multi-speaker podcast from research material
• Adding sources to Open Notebook programmatically through its REST API

Example prompts

• “Create a notebook called Battery Research and add these three PDFs as sources.”
• “Ask my notebook what the sources say about solid-state electrolytes.”
• “Generate a two-speaker podcast from the sources in my market analysis notebook.”

Requirements

• Docker Desktop to run Open Notebook
• An API key for at least one AI provider, or a local Ollama
• Python 3, for the bundled scripts

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1Navigate to Settings > API Keys in the UI
2. 2Add credentials for your preferred provider (OpenAI, Anthropic, etc.)
3. 3Test the connection and discover available models
4. 4Register models for use across the platform

What it can do on your machine

Read from SKILL.md and the folder at commit efc0b96. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 4 files in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • curl
  • docker-compose

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • raw.githubusercontent.com
  • arxiv.org

  Also links to:

  • github.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • OPEN_NOTEBOOK_ENCRYPTION_KEY
  • OPEN_NOTEBOOK_PASSWORD

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Open Notebook loads about 2.6k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 160 tokens; SKILL.md has 722 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~160

When it runs · the whole SKILL.md, loaded when a task matches

~2.6k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~10k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from agent-skills-hub/agent-skills-hub at commit efc0b96, republished under its MIT licence (© agent-skills-hub). 722 words, ~2,613 tokens.

name

open-notebook

description

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.

license

MIT

metadata.skill-author

K-Dense Inc.

# Download the docker-compose file
curl -o docker-compose.yml https://raw.githubusercontent.com/lfnovo/open-notebook/main/docker-compose.yml

# Set the required encryption key
export OPEN_NOTEBOOK_ENCRYPTION_KEY="your-secret-key-here"

# Launch the services
docker-compose up -d

import requests

BASE_URL = "http://localhost:5055/api"

# Add a credential for an AI provider
response = requests.post(f"{BASE_URL}/credentials", json={
    "provider": "openai",
    "name": "My OpenAI Key",
    "api_key": "sk-..."
})
credential = response.json()

# Discover available models
response = requests.post(
    f"{BASE_URL}/credentials/{credential['id']}/discover"
)
discovered = response.json()

# Register discovered models
requests.post(
    f"{BASE_URL}/credentials/{credential['id']}/register-models",
    json={"model_ids": [m["id"] for m in discovered["models"]]}
)

import requests

BASE_URL = "http://localhost:5055/api"

# Create a notebook
response = requests.post(f"{BASE_URL}/notebooks", json={
    "name": "Cancer Genomics Research",
    "description": "Literature review on tumor mutational burden"
})
notebook = response.json()
notebook_id = notebook["id"]

# Add a web URL source
response = requests.post(f"{BASE_URL}/sources", data={
    "url": "https://arxiv.org/abs/2301.00001",
    "notebook_id": notebook_id,
    "process_async": "true"
})
source = response.json()

# Upload a PDF file
with open("paper.pdf", "rb") as f:
    response = requests.post(
        f"{BASE_URL}/sources",
        data={"notebook_id": notebook_id},
        files={"file": ("paper.pdf", f, "application/pdf")}
    )

# Create a human note
response = requests.post(f"{BASE_URL}/notes", json={
    "title": "Key Findings",
    "content": "TMB correlates with immunotherapy response in NSCLC...",
    "note_type": "human",
    "notebook_id": notebook_id
})

# Create a chat session
session = requests.post(f"{BASE_URL}/chat/sessions", json={
    "notebook_id": notebook_id,
    "title": "TMB Discussion"
}).json()

# Send a message with context from sources
response = requests.post(f"{BASE_URL}/chat/execute", json={
    "session_id": session["id"],
    "message": "What are the key biomarkers for immunotherapy response?",
    "context": {"include_sources": True, "include_notes": True}
})

# Vector search across the knowledge base
results = requests.post(f"{BASE_URL}/search", json={
    "query": "tumor mutational burden immunotherapy",
    "search_type": "vector",
    "limit": 10
}).json()

# Ask a question with AI-powered answer
answer = requests.post(f"{BASE_URL}/search/ask/simple", json={
    "query": "How does TMB predict checkpoint inhibitor response?"
}).json()

# Generate a podcast episode
job = requests.post(f"{BASE_URL}/podcasts/generate", json={
    "notebook_id": notebook_id,
    "episode_profile_id": episode_profile_id,
    "speaker_profile_ids": [speaker1_id, speaker2_id]
}).json()

# Check generation status
status = requests.get(f"{BASE_URL}/podcasts/jobs/{job['job_id']}").json()

# Download audio when ready
audio = requests.get(
    f"{BASE_URL}/podcasts/episodes/{status['episode_id']}/audio"
)

# Create a custom transformation
transform = requests.post(f"{BASE_URL}/transformations", json={
    "name": "extract_methods",
    "title": "Extract Methods",
    "description": "Extract methodology details from papers",
    "prompt": "Extract and summarize the methodology section...",
    "apply_default": False
}).json()

# Execute transformation on text
result = requests.post(f"{BASE_URL}/transformations/execute", json={
    "transformation_id": transform["id"],
    "input_text": "...",
    "model_id": "model_id_here"
}).json()

© agent-skills-hub, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references) in skills/open-notebook of agent-skills-hub/agent-skills-hub.

• SKILL.md
• references/api_reference.md
• references/architecture.md
• references/configuration.md
• references/examples.md
• scripts/chat_interaction.py
• scripts/notebook_management.py
• scripts/source_ingestion.py
• scripts/test_open_notebook_skill.py

Open the folder on GitHubat commit efc0b96

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in agent-skills-hub/agent-skills-hub, which our catalogue first saw on October 9, 2026.

• LeonChaoX/qinyan-academic-skillsskills/14-文档处理与数据工具/open-notebook
• FreedomIntelligence/OpenClaw-Medical-Skillsskills/open-notebook
• BioTender-max/awesome-bio-agent-skillsskills/kdense/open-notebook