Notebooklm

NotebookLM Research Assistant Skill

Query Google NotebookLM for source-grounded answers from your uploaded documents. Each question opens a fresh browser session, retrieves the answer, and closes.

When to Use

Trigger when user:

• Mentions NotebookLM or shares a NotebookLM URL
• Asks to query their notebooks/documentation
• Uses phrases like "ask my NotebookLM", "check my docs", "query my notebook"

Critical: Always Use run.py Wrapper

NEVER call scripts directly. ALWAYS use python scripts/run.py [script]:

# ✅ CORRECT:
python scripts/run.py auth_manager.py status
python scripts/run.py notebook_manager.py list

# ❌ WRONG (fails without venv):
python scripts/auth_manager.py status

The wrapper auto-creates .venv, installs deps, and activates the environment.

Core Workflow

1. Check Auth

python scripts/run.py auth_manager.py status

If not authenticated → python scripts/run.py auth_manager.py setup (browser opens for Google login).

2. Manage Notebooks

python scripts/run.py notebook_manager.py list
python scripts/run.py notebook_manager.py add --url URL --name NAME --description DESC --topics TOPICS
python scripts/run.py notebook_manager.py search --query QUERY
python scripts/run.py notebook_manager.py activate --id ID
python scripts/run.py notebook_manager.py remove --id ID

3. Smart Add (when user doesn't provide details)

Query the notebook first to discover its content, then add with accurate metadata:

# Step 1: Discover content
python scripts/run.py ask_question.py --question "What is the content of this notebook? What topics are covered?" --notebook-url "[URL]"

# Step 2: Add with discovered info
python scripts/run.py notebook_manager.py add --url "[URL]" --name "[discovered]" --description "[discovered]" --topics "[discovered]"

NEVER guess descriptions — use Smart Add or ask the user.

4. Ask Questions

python scripts/run.py ask_question.py --question "Your question" [--notebook-id ID] [--notebook-url URL] [--show-browser]

Follow-Up Protocol

Every NotebookLM answer ends with: "Is that ALL you need to know?"

1. STOP — Don't immediately respond to user
2. ANALYZE — Compare answer to user's original request
3. IDENTIFY GAPS — Determine if more information needed
4. ASK FOLLOW-UP — If gaps exist, query again with context
5. SYNTHESIZE — Combine all answers before responding to user

Decision Flow

User mentions NotebookLM
  → Check auth (auth_manager.py status)
  → If not auth'd → setup (browser visible)
  → Check/Add notebook (notebook_manager.py list/add)
  → Activate notebook (notebook_manager.py activate --id ID)
  → Ask question (ask_question.py --question "...")
  → Follow-up until complete
  → Synthesize and respond

Data Storage

All data stored locally in data/ within the skill directory:

• library.json — Notebook metadata
• auth_info.json — Authentication status
• browser_state/ — Browser cookies and session

Protected by .gitignore — never committed to git.

Limitations

• No session persistence (each question = new browser session)
• Rate limits on free Google accounts (~50 queries/day)
• Manual upload required (user adds docs to NotebookLM web UI)
• Browser overhead (few seconds per question)

References

For detailed information, see:

• references/api_reference.md — Full script API, arguments, and examples
• references/troubleshooting.md — Common issues and solutions
• references/usage_patterns.md — Best practices and workflow patterns
• references/configuration.md — Environment variables and .env setup

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit

Memory-First Protocol

python3 execution/memory_manager.py auto --query "NotebookLM patterns and prior queries"

Storing Results

python3 execution/memory_manager.py store \
  --content "NotebookLM: <what was queried/decided>" \
  --type technical --project <project> \
  --tags notebooklm documentation

Multi-Agent Collaboration

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "NotebookLM query completed — <summary>" \
  --project <project>