I wanted to create a reusable AI skill, but I did not want to write vague instructions based on guesswork. I wanted the skill to have a clear foundation. So I started with a research paper and used NotebookLM to understand its main ideas before turning them into a practical skill.md file. Here is the process I followed. Why I started with a research paper AI instructions often sound useful but fail in practice. They may contain broad advice such as: Write clearly. Be more human. Avoid repetition. Use a professional tone. The problem is that these instructions are difficult to apply consistently. A research paper gives the skill a stronger foundation. It provides concepts, patterns, and evidence that can be converted into clear writing rules. The goal was not to copy the paper. The goal was to turn its ideas into instructions an AI agent could follow. Step 1: Add the paper to NotebookLM I uploaded the research paper to NotebookLM and used it as a research assistant. Instead of asking for a general summary, I asked focused questions: What are the paper’s main principles? Which ideas can be turned into writing rules? What patterns make text sound repetitive or unnatural? What examples does the paper provide? Which recommendations are practical for an AI writing assistant? This helped separate useful ideas from academic detail that did not belong in the final skill. NotebookLM was useful because I could ask follow-up questions while keeping the discussion connected to the source document. Step 2: Extract practical rules The paper explained the concepts, but a skill file needs direct instructions. For example, an academic paper might discuss variation in sentence structure. I turned that idea into a simple rule: Rule: Vary sentence length and structure. Combine short sentences when they feel repetitive, and split long sentences when they contain too many ideas. I repeated this process for each major idea. The skill needed rules that were: Specific Easy to follow Short enough to remember Practical during editing Supported by examples This step was important because a research summary is not the same as an AI instruction file. Step 3: Organize the skill.md file After extracting the rules, I organized them into sections. The structure looked like this: # Humanized Writing Skill ## Role Define what the AI should do and who it should sound like. ## Core Principles Explain the main ideas behind the skill. ## Editing Rules List direct rules for sentence structure, clarity, tone, and transitions. ## Audience Modes Adapt the writing for academic, business, educator, or marketing audiences. ## Prohibited Patterns List common phrases, structures, and habits to avoid. ## Editing Workflow Describe the order the AI should follow when editing. ## Final Checklist Give the AI a quick way to verify its work. This structure made the skill easier to read and easier to apply. Each section had a clear purpose. I avoided adding instructions only because they sounded impressive. Step 4: Add examples Rules are easier to follow when they include examples. For instance: ❌ AI-generated: It is important to note that the study demonstrates a significant improvement in performance. ✅ Improved: The study found that performance improved. The example shows the AI what to remove: Unnecessary opening phrases Vague words Heavy noun-based writing Extra formality Examples also helped me test whether the rules were clear. If I could not write a simple example, the instruction probably needed more work. Step 5: Test the skill on real writing The first version of the skill was not finished when the file was saved. I tested it on different types of writing: Blog posts Technical explanations Academic paragraphs Product descriptions Marketing content Each test exposed new problems. Sometimes the output became too casual. Sometimes it removed useful technical terms. In other cases, the AI followed the rules too strictly and made every paragraph sound the same. I adjusted the instructions after each test. The most useful addition was a preservation rule: Crucial Rule: Do not invent, remove, or change research claims, citations, data, or important qualifications. Humanizing text should improve the writing without changing the meaning. What NotebookLM did and did not do NotebookLM helped me understand and organize the research paper. It helped with: Finding the paper’s main ideas Comparing sections Extracting examples Asking follow-up questions Turning theory into notes But it did not replace the editing process. I still had to decide: Which ideas belonged in the skill How detailed each instruction should be Which rules were too vague How to handle different audiences How to test the final output NotebookLM helped with research. I designed the skill. The final lesson A useful skill.md file is more than a list of prompts. It is a small operating manual for a specific kind of work. Starting with a research paper gave me the concepts. NotebookLM helped me explore them. Testing the instructions on real writing helped me turn those concepts into a working skill. The process was: Find a useful research source. Study it with NotebookLM. Extract practical principles. Convert those principles into direct instructions. Add examples. Test the skill on real content. Revise anything that produces weak results. That workflow can work for writing, research, coding, analysis, or any other task where you want consistent AI behavior. The important part is to turn knowledge into clear actions. That is what makes a skill.md file useful. (In fact, this entire blog post was written using the exact skill.md file I created from this process!)