Expert Interview
This is how you extract the stuff AI can't fake. It asks 2-4 targeted questions to pull out contrarian takes, specific numbers, project failures, and scope limitations, then packages the answers into a knowledge document you can feed directly into write-content. The questions adapt by content type (how-to vs thought leadership vs case study), and it follows up when answers get interesting. The honest take: this only works if you actually have experience worth extracting. If you're writing about something you haven't done, no amount of good questions will save you. But if you've shipped the project or lived the problem, this turns 10 minutes of conversation into the kind of first-party material that makes content impossible to commoditize.
- skills.sh installs
- 134
- GitHub stars
- 383
- last updated
- May 12, 2026
Install
$ npx -y skills add inhouseseo/superseo-skills --skill expert-interview --agent claude-codeInstalls into .claude/skills of the current project.
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)
Sponsors
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- Repository
- github.com/inhouseseo/superseo-skills
- License
- Apache-2.0
- First seen
- Jun 11, 2026
