Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add julianobarbosa/claude-code-skills --skill notebooklm-creategit clone --depth 1 https://github.com/julianobarbosa/claude-code-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/julianobarbosa/claude-code-skills/notebooklm-create)<a href="https://agentmods.dev/skills/julianobarbosa/claude-code-skills/notebooklm-create"><img src="https://agentmods.dev/badge/skills/julianobarbosa/claude-code-skills/notebooklm-create/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/julianobarbosa/claude-code-skills/notebooklm-create"><img src="https://agentmods.dev/badge/skills/julianobarbosa/claude-code-skills/notebooklm-create.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00237 | $0.02962 |
| Opus 5.5 | $0.00095 | $0.01185 |
| Sonnet 5.5 | $0.00047 | $0.00592 |
| Haiku 4.5 | $0.00024 | $0.00296 |
Grade A, and why
notebooklm-create scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
notebooklm-create
End-to-end workflow for building a complete NotebookLM topic notebook: deep research, source loading, full Studio, curated prompts. Orchestrates the notebooklm-rpc MCP tools with domain-specific gotcha handling encoded.
Relationship to notebooklm-skill
notebooklm-skill= low-level API reference (notebooklm-py CLI)- This skill (
notebooklm-create) = high-level workflow orchestration using thenotebooklm-rpcMCP tools available in this session
If the user just wants to call a single API (e.g., "list my notebooks"), use notebooklm-skill or the MCP tool directly. If they want to BUILD a research notebook from a topic, use this skill.
Why this workflow exists
Building a rich NotebookLM notebook for a topic has repeatable shape but is strewn with gotchas that cost real time if rediscovered:
- Some domains silently refuse NotebookLM's URL fetcher (Medium, Quora)
- URL adds cascade-fail when issued in parallel — must be sequential
llms.txtURLs need the trailing.mdstripped before adding- Studio artifacts split into synchronous (mind map, reports, flashcards, quiz, data table) and async (audio, video, infographic, slide deck) with different polling behavior
- The Studio API classifies quiz artifacts as
type: flashcardsonce generated (title disambiguates) confirm=Trueis mandatory on every Studio creation tool AND requires explicit user approval
This skill encodes all of that so future runs don't relearn it.
Workflow — the 7 phases
Run these in order. Each phase has a clear hand-off to the next.
Phase 1 — Capture intent
Ask or infer:
- What's the topic? (e.g., "cmux", "ArgoCD image updater", "Azure landing zones")
- Any seed URLs the user already wants included (videos, blog posts, GitHub repos)?
- Which forums to cover? Defaults: Reddit, Hacker News, GitHub Discussions. Optional: Discord (captured as text — not URL-indexable), Quora (usually empty for technical topics; confirm via research)
- Target notebook title — default convention:
{topic} :: docs(matches user's existing naming pattern visible vianotebook_list) - Does the user want the full Studio (all 10 artifacts) or selective?
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/community_content_template.md 1.9 KB
- assets/research_synthesis_template.md 1.5 KB
- assets/workflow_checklist.md 2.6 KB
- evals/evals.json 3.0 KB
- references/mcp-tool-map.md 4.7 KB
- references/prompt-library.md 6.9 KB
- references/studio-quirks.md 4.0 KB
- references/url-gotchas.md 3.1 KB
- samples/cmux_docs_walkthrough.md 6.4 KB
- scripts/adapt_prompts.py 8.6 KB runs code
- scripts/add_urls_sequential.py 3.1 KB runs code
- scripts/build_full_studio.py 6.0 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 211 lines · 237 tokens per session scan A b86a35bce397
notebooklm-create is a skill published in the GitHub repository julianobarbosa/claude-code-skills (14 stars, last pushed 11d ago), licensed MIT. It adds 237 tokens to every session and 2,962 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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