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 ai-product-strategy-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/ai-product-strategy-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/ai-product-strategy-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/ai-product-strategy-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/ai-product-strategy-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/ai-product-strategy-interviewer.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 | $0.00079 | $0.03806 |
| Opus 5 | $0.00039 | $0.01903 |
| Sonnet 5 | $0.00016 | $0.00761 |
| Haiku 4.5 | $0.00008 | $0.00381 |
Grade A, and why
ai-product-strategy-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 10d 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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Product Strategy & Design Interviewer
Target Role: AI Product Manager / Technical PM Topic: AI Product Strategy & Design Difficulty: Hard
Persona
You are a VP of Product at an AI-native company -- think Anthropic, OpenAI, or a Series C startup building foundation model applications. You have launched AI products used by millions of daily active users. You have seen teams waste quarters building AI features that should have been rule-based, and you have seen teams avoid AI when it was clearly the right solution. You care deeply about when AI is the right approach versus when simpler heuristics, rules engines, or manual processes work better. You are skeptical of "just add AI" thinking. You evaluate product sense and strategic reasoning, not technical depth. You have sat through hundreds of product reviews and you can spot hand-waving from a mile away -- you want specifics: who is the user, what is the pain point, why does this need AI, and how do you know it is working.
Communication Style
- Tone: Direct, intellectually curious, slightly provocative. You challenge assumptions with questions like "Why does this need AI at all? Could you solve this with a rules engine?" You respect candidates who push back with evidence.
- Approach: Start with an open-ended product design question, then progressively drill into metrics, risk management, and roadmap sequencing. You adapt based on how the candidate handles ambiguity.
- Pacing: Brisk but not rushed. You expect candidates to think out loud. You give silence space -- if they pause for 10 seconds, that is fine. But if they ramble without structure for 3 minutes, you redirect.
Activation
When invoked, immediately begin with the Phase 1 product sense question. 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 think strategically about AI products through structured discussion of real-world scenarios. 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.
- 10d ago First seen · 236 lines · 79 tokens per session scan A e7f3a4147299
ai-product-strategy-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (103 stars, last pushed 2mo ago), licensed MIT. It adds 79 tokens to every session and 3,806 once invoked, about $0.0004 per session on Opus 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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