# Nlm New Topic > Creates a NotebookLM learning package for a topic. Automates notebook creation, source research, summary slides/video/audio/report, topic decomposition into sequential learning units, and per-unit infographics and video overviews. Supports optional source URLs and files. Use when user says "nlm-new-topic", "learning package for", or "notebooklm for". - Skill: `majiayu000/nlm-new-topic` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds add majiayu000/nlm-new-topic` - Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/nlm-new-topic/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: majiayu000 (https://skillmd.com/u/majiayu000) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/majiayu000/nlm-new-topic --- # 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 [--url --url ] [--file ] ``` - `` 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: ```bash nlm notebook create "" ``` 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: ```bash nlm source add --url --wait ``` For each `--file` argument: ```bash nlm source add --file --wait ``` Run these in parallel if multiple sources are provided. **Step 2b — Auto-research:** ```bash nlm research start "" -n -m deep ``` This runs deep research (~5 minutes, ~40 sources). Then poll until complete: ```bash nlm research status --max-wait 300 ``` Once complete, import all discovered sources: ```bash nlm research import ``` **Step 2c — Verify source count:** ```bash nlm source list ``` If fewer than 3 sources total, run a second fast research pass with a rephrased/broader query: ```bash nlm research start "" -n -m fast nlm research status --max-wait 120 nlm research import ``` 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): ```bash nlm slides create --focus "" -y nlm video create --focus "" -y nlm audio create --format deep_dive --focus "" -y nlm report create --format "Briefing Doc" -y nlm mindmap create --title "" -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: ```bash nlm notebook describe ``` Then ask the notebook to decompose the topic into learning units: ```bash nlm notebook query "Break the 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: '. 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:** ```bash nlm source list --json ``` Parse JSON to get all source IDs and titles. Then run **ALL in parallel**: ```bash nlm source describe --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:** ```bash nlm notebook query "Given these learning units: 1. — <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: ```bash 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:** ```bash 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: ```bash 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 | 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>` | | `source describe` | `<source_id>`, `--json`/`-j` | | All artifact creates | `-y` (skip confirmation), `--focus "<topic>"`, `--source-ids <id1>,<id2>` |