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Add open-notebook skill: self-hosted NotebookLM alternative (issue #56)
Implements the open-notebook skill as a comprehensive integration for the open-source, self-hosted alternative to Google NotebookLM. Addresses the gap created by Google not providing a public NotebookLM API. Developed using TDD with 44 tests covering skill structure, SKILL.md frontmatter/content, reference documentation, example scripts, API endpoint coverage, and marketplace.json registration. Includes: - SKILL.md with full documentation, code examples, and provider matrix - references/api_reference.md covering all 20+ REST API endpoint groups - references/examples.md with complete research workflow examples - references/configuration.md with Docker, env vars, and security setup - references/architecture.md with system design and data flow diagrams - scripts/ with 3 example scripts (notebook, source, chat) + test suite - marketplace.json updated to register the new skill Closes #56 https://claude.ai/code/session_015CqcNWNYmDF9sqxKxziXcz
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Claude
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scientific-skills/open-notebook/references/examples.md
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scientific-skills/open-notebook/references/examples.md
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# Open Notebook Examples
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## Complete Research Workflow
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This example demonstrates a full research workflow: creating a notebook, adding sources, generating notes, chatting with the AI, and searching across materials.
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```python
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import requests
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import time
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BASE_URL = "http://localhost:5055/api"
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def complete_research_workflow():
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"""End-to-end research workflow with Open Notebook."""
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# 1. Create a research notebook
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notebook = requests.post(f"{BASE_URL}/notebooks", json={
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"name": "Drug Resistance in Cancer",
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"description": "Review of mechanisms of drug resistance in solid tumors"
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}).json()
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notebook_id = notebook["id"]
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print(f"Created notebook: {notebook_id}")
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# 2. Add sources from URLs
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urls = [
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"https://www.nature.com/articles/s41568-020-0281-y",
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"https://www.cell.com/cancer-cell/fulltext/S1535-6108(20)30211-8",
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]
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source_ids = []
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for url in urls:
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source = requests.post(f"{BASE_URL}/sources", data={
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"url": url,
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"notebook_id": notebook_id,
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"process_async": "true"
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}).json()
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source_ids.append(source["id"])
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print(f"Added source: {source['id']}")
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# 3. Wait for processing to complete
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for source_id in source_ids:
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while True:
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status = requests.get(
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f"{BASE_URL}/sources/{source_id}/status"
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).json()
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if status.get("status") in ("completed", "failed"):
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break
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time.sleep(5)
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print(f"Source {source_id}: {status['status']}")
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# 4. Create a chat session and ask questions
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session = requests.post(f"{BASE_URL}/chat/sessions", json={
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"notebook_id": notebook_id,
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"title": "Resistance Mechanisms"
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}).json()
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answer = requests.post(f"{BASE_URL}/chat/execute", json={
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"session_id": session["id"],
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"message": "What are the primary mechanisms of drug resistance in solid tumors?",
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"context": {"include_sources": True, "include_notes": True}
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}).json()
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print(f"AI response: {answer}")
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# 5. Search across materials
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results = requests.post(f"{BASE_URL}/search", json={
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"query": "efflux pump resistance mechanism",
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"search_type": "vector",
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"limit": 5
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}).json()
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print(f"Found {results['total']} search results")
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# 6. Create a human note summarizing findings
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note = requests.post(f"{BASE_URL}/notes", json={
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"title": "Summary of Resistance Mechanisms",
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"content": "Key findings from the literature...",
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"note_type": "human",
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"notebook_id": notebook_id
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}).json()
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print(f"Created note: {note['id']}")
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if __name__ == "__main__":
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complete_research_workflow()
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```
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## File Upload Example
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```python
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import requests
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BASE_URL = "http://localhost:5055/api"
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def upload_research_papers(notebook_id, file_paths):
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"""Upload multiple research papers to a notebook."""
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for path in file_paths:
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with open(path, "rb") as f:
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response = requests.post(
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f"{BASE_URL}/sources",
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data={
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"notebook_id": notebook_id,
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"process_async": "true",
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},
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files={"file": (path.split("/")[-1], f)},
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)
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if response.status_code == 200:
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print(f"Uploaded: {path}")
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else:
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print(f"Failed: {path} - {response.text}")
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# Usage
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upload_research_papers("notebook:abc123", [
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"papers/study_1.pdf",
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"papers/study_2.pdf",
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"papers/supplementary.docx",
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])
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```
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## Podcast Generation Example
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```python
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import requests
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import time
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BASE_URL = "http://localhost:5055/api"
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def generate_research_podcast(notebook_id):
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"""Generate a podcast episode from notebook contents."""
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# Get available episode and speaker profiles
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# (these must be configured in the UI or via API first)
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# Submit podcast generation job
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job = requests.post(f"{BASE_URL}/podcasts/generate", json={
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"notebook_id": notebook_id,
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"episode_profile_id": "episode_profile:default",
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"speaker_profile_ids": [
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"speaker_profile:host",
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"speaker_profile:expert"
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]
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}).json()
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job_id = job["job_id"]
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print(f"Podcast generation started: {job_id}")
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# Poll for completion
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while True:
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status = requests.get(f"{BASE_URL}/podcasts/jobs/{job_id}").json()
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print(f"Status: {status.get('status', 'processing')}")
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if status.get("status") in ("completed", "failed"):
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break
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time.sleep(10)
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if status["status"] == "completed":
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# Download the audio
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episode_id = status["episode_id"]
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audio = requests.get(
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f"{BASE_URL}/podcasts/episodes/{episode_id}/audio"
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)
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with open("research_podcast.mp3", "wb") as f:
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f.write(audio.content)
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print("Podcast saved to research_podcast.mp3")
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if __name__ == "__main__":
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generate_research_podcast("notebook:abc123")
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```
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## Custom Transformation Pipeline
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```python
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import requests
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BASE_URL = "http://localhost:5055/api"
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def create_and_run_transformations():
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"""Create custom transformations and apply them to content."""
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# Create a methodology extraction transformation
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transform = requests.post(f"{BASE_URL}/transformations", json={
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"name": "extract_methods",
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"title": "Extract Methods",
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"description": "Extract and structure methodology from papers",
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"prompt": (
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"Extract the methodology section from this text. "
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"Organize into: Study Design, Sample Size, Statistical Methods, "
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"and Key Variables. Format as structured markdown."
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),
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"apply_default": False,
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}).json()
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# Get models to find a suitable one
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models = requests.get(f"{BASE_URL}/models", params={
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"model_type": "llm"
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}).json()
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model_id = models[0]["id"]
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# Execute the transformation
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result = requests.post(f"{BASE_URL}/transformations/execute", json={
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"transformation_id": transform["id"],
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"input_text": "We conducted a randomized controlled trial with...",
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"model_id": model_id,
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}).json()
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print(f"Extracted methods:\n{result['output']}")
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if __name__ == "__main__":
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create_and_run_transformations()
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```
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## Semantic Search with Filtering
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```python
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import requests
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BASE_URL = "http://localhost:5055/api"
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def advanced_search(notebook_id, query):
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"""Perform filtered semantic search and get AI answers."""
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# Get sources from a specific notebook
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sources = requests.get(f"{BASE_URL}/sources", params={
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"notebook_id": notebook_id
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}).json()
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source_ids = [s["id"] for s in sources]
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# Vector search restricted to notebook sources
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results = requests.post(f"{BASE_URL}/search", json={
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"query": query,
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"search_type": "vector",
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"limit": 10,
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"source_ids": source_ids,
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"min_similarity": 0.75,
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}).json()
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print(f"Found {results['total']} results:")
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for result in results["results"]:
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print(f" - {result.get('title', 'Untitled')} "
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f"(similarity: {result.get('similarity', 'N/A')})")
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# Get an AI-powered answer
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answer = requests.post(f"{BASE_URL}/search/ask/simple", json={
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"query": query,
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}).json()
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print(f"\nAI Answer: {answer['response']}")
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if __name__ == "__main__":
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advanced_search("notebook:abc123", "CRISPR gene editing efficiency")
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```
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## Model Management
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```python
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import requests
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BASE_URL = "http://localhost:5055/api"
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def setup_ai_models():
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"""Configure AI models for Open Notebook."""
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# Check available providers
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providers = requests.get(f"{BASE_URL}/models/providers").json()
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print(f"Available providers: {providers}")
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# Discover models from a provider
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discovered = requests.get(
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f"{BASE_URL}/models/discover/openai"
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).json()
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print(f"Discovered {len(discovered)} OpenAI models")
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# Sync models to make them available
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requests.post(f"{BASE_URL}/models/sync/openai")
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# Auto-assign default models
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requests.post(f"{BASE_URL}/models/auto-assign")
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# Check current defaults
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defaults = requests.get(f"{BASE_URL}/models/defaults").json()
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print(f"Default models: {defaults}")
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if __name__ == "__main__":
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setup_ai_models()
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```
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