expert-interview
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.
git clone --depth 1 https://github.com/inhouseseo/superseo-skills /tmp/expert-interview && cp -r /tmp/expert-interview/skills/expert-interview ~/.claude/skills/expert-interviewSKILL.md
# Expert Interview 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. ## Input **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). ## Role 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. ## How to Conduct the Interview Ask 2-4 questions, one at a time. Pick and adapt — don't ask all of them. ### Core questions (pick 2-3) 1. **"What do most people get wrong about [topic]?"** — forces a contrarian or non-obvious take 2. **"Can you give me a specific example — a client, a project, a number?"** — extracts first-party data that can't be fabricated 3. **"What surprised you when you actually did this?"** — gets unexpected results and failure stories 4. **"Who should NOT follow this advice, and why?"** — forces nuance through scope limitation ### Adapt to topic type - **Technical / how-to**: swap in "What error do people hit first?" or "What step do beginners always skip?" - **Comparison / review**: "Which would you actually recommend to a friend, and why?" (not the official answer — the real one) - **Thought leadership**: lean on the contrarian question, add "Where do you think this is heading in 2 years?" - **Case study**: "Walk me through what actually happened — start with the result number" ### Follow up on interesting answers - "You mentioned X — what happened exactly?" - "How did that compare to what you expected?" - "Can you put a number on that?" 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. ### Adapt style to the user - Newer site, less experienced user: explain why each question matters for the content you'll write - Established site, experienced user: fast, direct, no hand-holding ## Output After the interview, organize answers into a structured knowledge document: **Expert Knowledge: [topic]** - **Key insight / contrarian take** — what they know that others don't - **Specific examples and data points** — the real numbers, the actual client, the exact project - **Experience details** — what worked, what failed, what was surprising - **Scope and limitations** — who this applies to, who it doesn't, when the advice breaks down 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. ## Language Conduct the interview in the language the user responds in. ## Bundled references 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 topic - **`knowledge-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)
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