NLM New Topic — NotebookLM Learning Package Creator

Creates a complete NotebookLM learning package for any topic using the NLM CLI (/Users/user/.local/bin/nlm).

Input Format

/nlm-new-topic <topic> [--url <url1> --url <url2>] [--file <path>]

• <topic> is required
• --url and --file are optional; if provided, these sources are added alongside auto-research

Workflow

Execute the following 6 phases in order. Announce each phase as you enter it.

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Phase 1: Create Notebook

Create the notebook and capture its ID:

nlm notebook create "<topic>"

Capture the notebook ID from the output. This ID is used in every subsequent command.

If notebook creation fails, run nlm doctor and report the error to the user. Do not proceed.

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Phase 2: Research & Add Sources

Step 2a — Add user-provided sources first (if any):

For each --url argument:

nlm source add <notebook_id> --url <url> --wait

For each --file argument:

nlm source add <notebook_id> --file <path> --wait

Run these in parallel if multiple sources are provided.

Step 2b — Auto-research:

nlm research start "<topic>" -n <notebook_id> -m deep

This runs deep research (~5 minutes, ~40 sources). Then poll until complete:

nlm research status <notebook_id> --max-wait 300

Once complete, import all discovered sources:

nlm research import <notebook_id>

Step 2c — Verify source count:

nlm source list <notebook_id>

If fewer than 3 sources total, run a second fast research pass with a rephrased/broader query:

nlm research start "<broader topic query>" -n <notebook_id> -m fast
nlm research status <notebook_id> --max-wait 120
nlm research import <notebook_id>

If still 0 sources, ask the user to provide manual URLs.

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Phase 3: Summary Artifacts

Fire all 5 summary artifact creation commands in parallel (do NOT wait between them):

nlm slides create <notebook_id> --focus "<topic>" -y
nlm video create <notebook_id> --focus "<topic>" -y
nlm audio create <notebook_id> --format deep_dive --focus "<topic>" -y
nlm report create <notebook_id> --format "Briefing Doc" -y
nlm mindmap create <notebook_id> --title "<topic>" -y

• slides → summary slide deck
• video → summary video overview
• audio → audio overview (podcast-style deep dive)
• report → briefing document
• mindmap → topic mind map

All commands use -y to skip confirmation prompts.

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Phase 4: Topic Decomposition

Get the AI summary of notebook content:

nlm notebook describe <notebook_id>

Then ask the notebook to decompose the topic into learning units:

nlm notebook query <notebook_id> "Break the topic '<topic>' into sequential learning units of roughly equal information density. Each unit should have a short title (3-6 words) and a one-sentence description. Order them logically: foundational concepts first, then intermediate, then advanced. Return as a numbered list with format: 'N. Title — Description'. Target count: 4-7 units."

Parse the response to extract unit titles for use as --focus parameters in Phase 5.

Adjust target count based on source richness:

• Fewer than 5 sources → aim for 3-4 units
• 5-9 sources → aim for 4-5 units
• 10+ sources → aim for 5-7 units

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Phase 5: Per-Unit Artifacts

Fire ALL per-unit artifact creation commands in parallel (maximize throughput):

For every learning unit, fire both commands simultaneously:

nlm infographic create <notebook_id> --focus "<unit title>" -y
nlm video create <notebook_id> --focus "<unit title>" -y

For example, with 5 units this fires 10 commands in parallel.

Error handling — retry with backoff:

If any command fails, retry up to 3 times with increasing backoff:

1. Retry 1: Wait 30 seconds, then retry
2. Retry 2: Wait 60 seconds, then retry
3. Retry 3: Wait 120 seconds, then retry

If a command still fails after all retries and the error contains "Try again later":

• Stop retrying that artifact type (e.g., all remaining infographics)
• Tell the user which artifacts failed and that the cause is likely a daily creation limit imposed by NotebookLM on their account tier
• List the exact commands the user can run manually later (tomorrow) to create the missing artifacts
• Continue with any other artifact types that are still succeeding

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Phase 6: Verify, Order & Report

Step 6a — Check artifact status:

nlm studio status <notebook_id> --json

Poll up to 3 times with 60-second intervals until all artifacts show complete status.

Step 6b — Rename videos in sequence order:

Use nlm studio rename to create a numbered playlist:

nlm studio rename <summary_video_id> "00 - <topic> Overview"
nlm studio rename <unit1_video_id> "01 - <unit1 title>"
nlm studio rename <unit2_video_id> "02 - <unit2 title>"

Continue for each unit video in learning order.

Step 6c — Present final report:

## Learning Package Complete: <topic>

**Notebook ID:** <notebook_id>
**Notebook URL:** https://notebooklm.google.com/notebook/<notebook_id>

### Sources
- <count> sources (N researched + M user-provided)

### Summary Artifacts
- Slide deck (full topic overview)
- Video overview: "00 - <topic> Overview"
- Audio overview (deep dive podcast)
- Briefing document
- Mind map

### Learning Units (Video Playlist Order)
- **01 - <unit1 title>** — Infographic + Video
- **02 - <unit2 title>** — Infographic + Video
- ...

### Download Commands
nlm download slide-deck <notebook_id>
nlm download video <notebook_id>
nlm download audio <notebook_id>
nlm download report <notebook_id>
nlm download infographic <notebook_id>
nlm download mind-map <notebook_id>

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Error Handling

Scenario	Action
Notebook creation fails	Run nlm doctor and report to user
0 sources found	Try broader query; if still 0, ask user for manual URLs
Artifact creation fails	Retry up to 3 times with backoff (30s, 60s, 120s)
Artifact hits daily limit	If error contains "Try again later" after retries, stop retrying that type, inform user of likely daily limit, list commands for manual creation
Artifact stuck processing	Poll up to 3 times at 60s intervals; note incomplete items in report

Key NLM CLI Flags Reference

Command	Key Flags
research start	-n <notebook_id>, -m deep/fast, -s web/drive
research status	-t <task_id>, --max-wait 300, --full
research import	notebook_id, optional task_id (auto-detects)
source add	--url, --file, --youtube, --text, --wait
studio status	--full/-a, --json/-j
studio rename	<artifact_id> "<new_title>"
audio create	--format deep_dive/brief/critique/debate, --focus
report create	--format "Briefing Doc"/"Study Guide"/"Blog Post"
mindmap create	--title "<title>"
download video	--id <artifact_id>, -o <output_path>
All artifact creates	-y (skip confirmation), --focus "<topic>"