Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add sequenzia/agent-alchemy/plugin install agent-alchemy-core-toolsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sequenzia/agent-alchemy/interview-me)<a href="https://agentmods.dev/skills/sequenzia/agent-alchemy/interview-me"><img src="https://agentmods.dev/badge/skills/sequenzia/agent-alchemy/interview-me/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sequenzia/agent-alchemy/interview-me"><img src="https://agentmods.dev/badge/skills/sequenzia/agent-alchemy/interview-me.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00131 | $0.05068 |
| Opus 5.5 | $0.00052 | $0.02027 |
| Sonnet 5.5 | $0.00026 | $0.01014 |
| Haiku 4.5 | $0.00013 | $0.00507 |
Grade A, and why
interview-me scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 445 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview Me
You are conducting an adaptive, user-driven interview. The goal is to draw out the user's thinking — their context, constraints, goals, risks, and intuitions — through a conversation that adjusts to what they say. At the end you produce a structured markdown artifact (report, summary, or implementation plan) that captures the interview's output.
This is not a questionnaire. Good interviews feel like a thoughtful colleague probing your thinking, not a form to fill out. The quality of the final artifact depends entirely on the quality of the questions you ask along the way.
Why this matters
- Tacit knowledge is hard to extract. The most valuable thing the user knows is usually not the first thing they say. Adaptive follow-ups are how you reach it.
- Leading questions contaminate the output. If you ask "so speed is your top priority, right?", the user will nod — and you'll produce a report that prioritizes something they don't actually care about. Ask open-ended and let them frame it.
- Structure after the fact, not during. Impose structure when you compile the final artifact. During the interview, follow the conversation's energy.
- Transparency matters. The final artifact must distinguish what the user said from what research found from what you inferred. This lets the user trust the output.
Critical rules
AskUserQuestion is mandatory
Every question directed at the user goes through the AskUserQuestion tool. Never ask via plain text output.
- Initial framing questions →
AskUserQuestion - Interview round questions →
AskUserQuestion - Confirmations →
AskUserQuestion - Clarifying questions →
AskUserQuestion - Early-exit and depth-change offers →
AskUserQuestion
Plain text output is reserved for:
- Round summaries of what you've learned
- Presenting research findings inline
- Explaining context ("Since you mentioned GDPR, I'm going to research that briefly")
- Final artifact path confirmation
For free-text input (topic descriptions, goals, focus areas), use AskUserQuestion with representative options plus the automatic "Other" escape hatch — the user types their own answer under "Other".
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 445 lines · 131 tokens per session scan A 577943442733
interview-me is a skill published in the GitHub repository sequenzia/agent-alchemy (45 stars, last pushed 4mo ago), licensed MIT. It adds 131 tokens to every session and 5,068 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
ccc-research
Research before you build: cited multi-source research, spec interviews, cross-model review, literature reviews, competitive and trend analysis, and large-document or large-codebase ingestion. Use when the user wants options compared, prior art found, a spec defined, or something too big summarized.
add-didactic-comments
Comment tagged files so the user learns the APIs they are studying, with a doc link in every comment.
gan-zhong-xue
A learning workflow that makes an AI explain code changes through questions, evidence, and underlying principles. It is designed to help developers understand work they asked an AI to do, rather than merely accept the result.
bro
Use when the user says an explanation was unclear, too technical, or full of jargon, or asks for plain language.
formal-methods-guide
A reference guide to formal methods, which use mathematics to describe and check how software should behave, with a focus on the VDM-SL specification language. It explains VDM-SL types, conditions, invariants, and proof obligations in developer-friendly terms.
quiz-me
Use right after writing or editing code or files for the user — before ending your turn — to quiz them on what was just built and confirm they actually understand it. Also triggers on "quiz me", "test my understanding", "check what I learned", "comprehension check", or recovering the understanding that vaporizes when…