Building Claude Skills With NotebookLM: A Technical Autopsy

There's a workflow making the rounds that deserves examination: using Google's NotebookLM to generate custom instruction files for Anthropic's Claude AI. Julian Goldie, an SEO consultant and AI automation enthusiast, posted a tutorial that's garnered attention for its straightforward approach to what sounds like a technical process. The pitch is simple: combine two AI systems to create specialized "skills" that make Claude better at specific tasks—and supposedly eliminate hallucinations in the process.

I wanted to understand what's actually happening here, what the practical value is, and where the claims strain credibility.

The Mechanics: What's Actually Being Built

Goldie's workflow centers on a concept Anthropic introduced called "skills"—essentially structured markdown files that tell Claude how to approach specific tasks. Think of them as detailed instruction manuals. Instead of writing these from scratch, Goldie proposes using NotebookLM as a research intermediary.

The process goes like this: You feed NotebookLM sources—PDFs, articles, documentation, whatever's relevant to your task. Then you ask it to synthesize those sources into a skill file formatted for Claude. You drop that file into Claude's skills folder, and theoretically, Claude now has expert-level knowledge about that specific task.

As Goldie puts it: "Think of skills like little instruction folders. You give Claude a folder. Inside that folder, there's a file called skill.md. That file tells Claude exactly how to do a specific task. A recipe. A cheat sheet. A coach whispering in Claude's ear."

The technical implementation is straightforward. NotebookLM, powered by Google's Gemini model, reads your sources and answers questions based exclusively on that material. This source-grounding is NotebookLM's defining feature—it won't pull information from its training data if it's not in your uploaded documents. When you ask it to generate a Claude skill file, it structures that grounded information into a format Claude can follow.

The Hallucination Claim: Oversold But Not Wrong

Goldie makes a bold claim: this approach "cuts hallucinations like a hot knife through butter." This needs unpacking.

NotebookLM does reduce hallucinations in the research phase—that's accurate. Because it only references your uploaded sources, you're not getting invented statistics or fabricated studies. This is valuable when you're building instruction sets that need to be factually accurate.

But here's where it gets murky: Those skills are just instructions. When Claude uses them, it's still Claude doing the actual work. The skill file might say "write a landing page with these seven elements," but Claude still has to generate that landing page from its own knowledge base. The skill provides structure and guidance, but it doesn't magically prevent Claude from occasionally making things up during execution.

What you're really getting is better-informed instructions, not hallucination-proof output. It's a meaningful improvement—garbage instructions produce garbage results—but it's not the silver bullet the framing suggests.

Where This Actually Matters

Strip away the hype, and there's something useful here. The workflow addresses a real problem: most people don't know how to write effective AI instructions. They either write prompts that are too vague ("write me a good email") or too prescriptive (three paragraphs of micro-specifications that Claude would've figured out anyway).

Using NotebookLM as a research tool to build these instruction files makes sense for tasks where:

1. You have domain expertise you need to encode. If you've accumulated best practices, case studies, or documentation, NotebookLM can help structure that knowledge into reusable instructions.

2. The task is repetitive and benefits from consistency. Goldie's example of welcome emails for community members is apt—you want the same structure, tone, and information every time.

3. You're working in a specialized domain. Generic AI knowledge won't cut it for niche industries or proprietary processes. Source-grounded skills give you a way to teach Claude your specific context.

Goldie suggests building skills for "cold email writing, sales scripts, customer support replies, product descriptions, SEO content, lead magnets, onboarding flows, YouTube scripts." Some of these make more sense than others. Customer support replies with specific policy information? Absolutely. Generic SEO content? You're probably overthinking it.

The Ecosystem Play Nobody's Talking About

What's interesting about this workflow isn't just the technical process—it's what it reveals about the current AI landscape. You're using a Google product (NotebookLM, powered by Gemini) to create better inputs for an Anthropic product (Claude). Neither company intended this exact use case, but users are cobbling together their own solutions because no single AI system does everything they need.

This is the reality of AI tools in 2024: they're puzzle pieces, not complete solutions. NotebookLM is excellent at source-grounded research but limited in other ways. Claude is strong at following complex instructions and maintaining context. The workflow exists because both have gaps the other fills.

As Goldie notes: "That's the power of mixing Gemini's research with Claude's execution. Two AI giants working together for you while you sleep."

The sleep part is marketing speak, but the observation about complementary strengths is sound.

What Gets Lost in the Tutorial Format

Goldie's tutorial is clearly aimed at people who want immediately actionable steps. That's fine—tutorials serve a purpose. But the format flattens some important nuance:

Skills still require judgment. Goldie advises: "Always double-check what Notebook LM gives you. It's grounded in your sources. But you still want to read the skill file before pushing it into Claude. Make sure the steps make sense. Make sure the tone matches." This is the right advice, but it contradicts the earlier promise of effortless automation. You're not eliminating work; you're shifting it.

Source quality determines everything. "Quality in, quality out," Goldie says, which is correct but understated. If your sources are mediocre blog posts and thin documentation, NotebookLM will give you mediocre skills. The tool doesn't add insight that isn't in your materials.

The skill paradigm has limits. Not every task benefits from rigid instruction files. Some work requires flexibility, improvisation, or accessing knowledge that can't be pre-encoded. The skill approach works best for structured, repeatable tasks—which is a subset of what people use AI for.

The Version Control Suggestion Actually Matters

One of Goldie's recommendations stands out: "Version your skills. Save old versions. Save new versions. Track what works. Track what doesn't. Treat them like little software products. Cuz they kind of are."

This is probably the most technically sound advice in the entire tutorial. If you're going to build custom instruction files, you should absolutely version them. When a skill stops working well, you need to know what changed. When you improve one, you want to understand what made it better.

This also hints at a longer-term pattern: as people build libraries of AI instructions, they'll need tooling that doesn't exist yet. Version control for prompts and skills. A/B testing frameworks. Performance analytics. Right now, everyone's doing this ad hoc. Someone will build the infrastructure layer eventually.

What This Means for Non-Marketers

Goldie runs an SEO agency and markets AI automation courses. His use cases skew heavily toward marketing and community management. That's his world, and it shapes the examples.

But the core workflow—using a source-grounded AI to generate instruction files for a more capable AI—has applications beyond landing pages and email campaigns. Software teams could build skills for code review standards. Legal teams could encode research methodologies. Medical practices could structure clinical documentation workflows.

The principle generalizes better than the examples suggest. The question is whether the juice is worth the squeeze for your specific use case. Building and maintaining a skill library requires overhead. For some teams, that overhead pays off. For others, it's faster to just write good prompts.

The workflow isn't revolutionary, but it's not useless either. It's a practical technique for a specific problem: encoding domain knowledge into reusable AI instructions. Whether that problem is yours depends on how much repetitive, specialized work you're trying to automate—and how much time you're willing to invest in building the infrastructure to do it.

Marcus Chen-Ramirez covers AI and technology for Buzzrag.