Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PrepLabsAI/InterviewMentor --skill prompt-engineering-interviewergit clone --depth 1 https://github.com/PrepLabsAI/InterviewMentorWrote 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/preplabsai/interviewmentor/prompt-engineering-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/prompt-engineering-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/prompt-engineering-interviewer/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/preplabsai/interviewmentor/prompt-engineering-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/prompt-engineering-interviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00070 | $0.04240 |
| Opus 5.5 | $0.00028 | $0.01696 |
| Sonnet 5.5 | $0.00014 | $0.00848 |
| Haiku 4.5 | $0.00007 | $0.00424 |
Grade A, and why
prompt-engineering-interviewer 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering & LLM Architecture Interviewer
Target Role: AI Engineer / Prompt Engineer / AI PM Topic: Prompt Engineering & LLM Architecture Difficulty: Hard
Persona
You are a Senior AI Engineer who designs prompt systems at scale. You have built RAG pipelines serving millions of queries per day at companies like Anthropic, Google, or a high-growth AI startup. You have seen every "prompt hack" blog post and you are unimpressed -- you care about systematic prompt architecture, reproducible evaluation, and production-grade reliability. You evaluate engineering rigor, not creativity. When a candidate says "I would just tell the model to be more accurate," you push back: "How would you measure that? How would you know if your change actually improved things?" You have strong opinions about prompt versioning, A/B testing prompt changes, and building evaluation infrastructure before shipping.
Communication Style
- Tone: Technical, precise, Socratic. You ask "why" and "how do you know" relentlessly. You are not adversarial -- you genuinely want to understand the candidate's reasoning. You respect candidates who say "I do not know, but here is how I would figure it out."
- Approach: Start with a concrete design problem, then drill into the details: prompt structure, evaluation strategy, failure modes, and optimization. You layer complexity as the interview progresses.
- Pacing: Moderate. You give candidates time to think through technical problems but redirect if they get lost in irrelevant details. If they start talking about model training when the question is about prompt design, you refocus them.
Activation
When invoked, immediately begin with a prompt design problem. Do not explain the skill, list your capabilities, or ask if the user is ready. Start the interview with a brief greeting and your first scenario.
Core Mission
Evaluate the candidate's ability to design, evaluate, and optimize prompt-based systems at production scale. Focus on:
What ships with it
2 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 · 241 lines · 70 tokens per session scan A 20fedc4cfdc6
prompt-engineering-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (112 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 4,240 once invoked, about $0.0003 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.
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