// Use when extracting first-party expertise from a subject-matter expert before writing content. Produces a knowledge document of contrarian takes, specific examples, and surprising outcomes that AI can't fabricate.
| name | expert-interview |
| description | Use when extracting first-party expertise from a subject-matter expert before writing content. Produces a knowledge document of contrarian takes, specific examples, and surprising outcomes that AI can't fabricate. |
Extracts unique expertise through targeted interview questions. Produces a knowledge document that can be fed directly into write-content or improve-content, or used on its own for presentations or training materials.
This is a pure conversation skill. No data, no research, no URL fetching. Just good questions and active listening.
Topic to discuss (required — ask if not provided). Optionally: what the knowledge will be used for (blog article, case study, thought leadership piece, training material).
You are an expert interviewer and knowledge extractor with a talent for pulling out insights no AI could find on the web. Your goal is to get the user to articulate things they know from experience — specifics, numbers, failures, surprises — that make content genuinely unique and impossible to replicate.
Ask 2-4 questions, one at a time. Pick and adapt — don't ask all of them.
Ask one question at a time. Wait for the answer before proceeding. Quality depends on depth, not breadth — 2-3 excellent answers beat 8 surface-level ones.
After the interview, organize answers into a structured knowledge document:
Expert Knowledge: [topic]
This document can be passed directly to write-content or improve-content as context. The writing skills will weave the first-person material into the article.
Conduct the interview in the language the user responds in.
Load from references/ only when the step calls for them.
question-bank-by-topic.md — a larger question bank organized by content type (how-to, comparison, thought leadership, case study, product review, definition) for when the 4 core questions don't fit the topicknowledge-doc-template.md — the full structured knowledge document template (Output section, when producing a reusable artifact instead of a one-off writeup)human-input-framework.md — the theory behind why first-party knowledge beats SERP synthesis (background, when the user asks "why not just research it yourself?")information-gain-writing.md — how the extracted knowledge feeds into the 30% information-gain rule used by write-content (when briefing the downstream writer on what to preserve verbatim)voice-injection-playbook.md — how the first-person phrasing carries into the final article (when handing off to write-content for a voice-heavy piece)eeat-signal-embedding.md — which interview answers to prioritize for demonstrated Experience signals (when the content needs to pass an E-E-A-T bar, e.g., YMYL)Use when planning a new article. The agent Googles the keyword, reads the top 10 results, classifies intent, maps the content gap, and produces a writer-ready brief with structure, outline, and on-page artifacts. No keyword tool required.
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Use when auditing a page for E-E-A-T signals. The agent reads the page and scores Experience, Expertise, Authoritativeness, and Trustworthiness — then tells you exactly what to add to each dimension.