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expert-interview

@inhouseseo · 收录于 1 周前

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.

适合你,如果需要从专家口中挖出AI编不出的独家观点和案例

/ 下载安装
expert-interview.skill双击,或拖进 Claude 桌面版 / Cowork,即完成安装↓ .skill↓ .zip
用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录
Claude Code~/.claude/skills/(项目级 .claude/skills/)
Codex CLI~/.codex/skills/
Cursor自动读取上面两处目录
其他工具见其文档的「skills」目录;两个下载是同一份文件,只是名字不同
/ 通过 npx 安装 校验哈希
npx oh-my-skill add inhouseseo/superseo-skills/expert-interview
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- inhouseseo/superseo-skills/expert-interview
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify inhouseseo/superseo-skills/expert-interview
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用

技能原文 SKILL.md作者撰写 · Apache-2.0 · 0d8b6fc

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)
按 Apache-2.0 许可原样转载,未经改动 · 在 GitHub 查看 →

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