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 7 phases in order. Announce each phase as you enter it.

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.

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.

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.

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 MUST be mutually exclusive — no concept should appear in more than one unit. Each unit should have a short title (3-6 words), a one-sentence description of what it covers, and a one-sentence boundary note stating what it does NOT cover (i.e., what belongs to adjacent units). Order them logically: foundational concepts first, then intermediate, then advanced. Return as a numbered list with format: 'N. Title — Description. Boundary: <what this unit excludes>'. Target count: 4-7 units."

Parse the response to extract for each unit:

• Title (used for video naming)
• Description (what the unit covers)
• Boundary (what the unit excludes)

All three are used to build the --focus parameter in Phase 6.

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

Phase 5: Source-to-Unit Mapping

Map notebook sources to relevant learning units so each unit's artifacts only draw from topically relevant sources.

If the notebook has only 1 source, skip this phase entirely — all artifacts use that single source.

Step 5a — Collect source summaries:

nlm source list <notebook_id> --json

Parse JSON to get all source IDs and titles. Then run ALL in parallel:

nlm source describe <source_id> --json   # for each source

Collect id, title, summary (truncate to ~100 chars), and keywords (first 5). If source describe fails for a source, use only its title in the mapping prompt.

Step 5b — Map sources to units:

nlm notebook query <notebook_id> "Given these learning units:
1. <title> — <description>
2. <title> — <description>
...
And these sources:
- <source_id> '<title>': <summary_snippet> [keywords: kw1, kw2]
- <source_id> '<title>': <summary_snippet> [keywords: kw1, kw2]
...
For each learning unit, list ONLY the source IDs directly relevant to that unit's specific topic. A source may appear in multiple units. Format:
Unit 1: <id>, <id>, ...
Unit 2: <id>, <id>, ..."

Step 5c — Parse the mapping:

Build map: unit_number → [source_id1, source_id2, ...]

Fallback rules:

• If a unit maps to 0 sources → use ALL sources for that unit (omit --source-ids)
• If the mapping query returns unparseable output → use ALL sources for all units (degrades gracefully to current behavior)

Phase 6: Per-Unit Artifacts

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

For every learning unit, fire both commands simultaneously using an enriched focus prompt that scopes content and prevents overlap with neighboring units. Include --source-ids from the Phase 5 mapping to restrict each artifact to relevant sources only:

nlm infographic create <notebook_id> --focus "Unit <N> of <total>: <unit title>. <unit description>. Does NOT cover: <boundary>." --source-ids <id1>,<id2>,<id3> -y
nlm video create <notebook_id> --focus "Unit <N> of <total>: <unit title>. <unit description>. Does NOT cover: <boundary>." --source-ids <id1>,<id2>,<id3> -y

Build the focus string from the parsed decomposition output (title + description + boundary). Use the source IDs from the Phase 5 mapping for each unit. If a unit has no mapped sources (fallback), omit --source-ids entirely for that unit. 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

Phase 7: Verify, Order & Report

Step 7a — 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 7b — 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 7c — 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 (<N> sources)
- **02 - <unit2 title>** — Infographic + Video (<N> sources)
- ...

### 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>

Error Handling

Key NLM CLI Flags Reference