expert-interview
Skill1 starsChanged 7 months agoApache-2.0View on GitHub
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.
In this repository10 other files it gives its agents.
- content-briefSkill
- eeat-auditSkill
- featured-snippet-optimizerSkill
- improve-contentSkill
- keyword-deep-diveSkill
- linkbuildingSkill
- page-auditSkill
- semantic-gap-analysisSkill
- topic-cluster-planningSkill
- write-contentSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 22
Ask 2-4 questions, one at a time.
- Distinctive: line 44
Ask one question at a time.
- Distinctive: line 53
After the interview, organize answers into a structured knowledge document:
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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)
- "What do most people get wrong about [topic]?" — forces a contrarian or non-obvious take
- "Can you give me a specific example — a client, a project, a number?" — extracts first-party data that can't be fabricated
- "What surprised you when you actually did this?" — gets unexpected results and failure stories
- "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 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 bywrite-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 towrite-contentfor 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)
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

