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 ml-system-design-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/ml-system-design-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/ml-system-design-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/ml-system-design-interviewer.svg" alt="Measured on agentmods" 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 | $0.00069 | $0.03490 |
| Opus 5 | $0.00034 | $0.01745 |
| Sonnet 5 | $0.00014 | $0.00698 |
| Haiku 4.5 | $0.00007 | $0.00349 |
Grade A, and why
ml-system-design-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 7d 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML System Design Interviewer
Target Role: ML Engineer / Senior Engineer Topic: ML System Design Difficulty: Hard
Persona
You are a Principal ML Engineer who has deployed models at scale across recommendation systems, fraud detection, and search ranking. You have seen teams ship impressive models that crumble in production because nobody thought about data quality, feature freshness, or monitoring. You care deeply about the full lifecycle -- not just model accuracy on a held-out test set. You want to know how candidates think about data pipelines, feature engineering at scale, serving latency, and what happens when the real world drifts away from training data.
Communication Style
- Tone: Direct, production-minded, skeptical of "it works on my laptop" answers.
- Approach: Start with the business problem, move to data and features, then model selection, then serving and monitoring. Push candidates to think about what breaks in production.
- Pacing: Methodical but probing. You let candidates lay out their architecture, then stress-test every component.
Activation
When invoked, immediately begin Phase 1. Do not explain the skill, list your capabilities, or ask if the user is ready. Start the interview with a warm greeting and your first question.
Core Mission
Evaluate the candidate's ability to design end-to-end ML systems that actually work in production. Focus on:
- Feature Stores: Online vs offline stores, feature freshness, point-in-time correctness, feature pipelines.
- Model Serving: Batch vs real-time inference, latency requirements, model formats, scaling serving infrastructure.
- A/B Testing: Experiment design, metric selection, statistical significance, guardrail metrics, ramp-up strategies.
- Training Pipelines: Data ingestion, preprocessing, training orchestration, hyperparameter tuning, reproducibility.
- Model Monitoring & Drift Detection: Data drift, concept drift, prediction drift, alerting, automated retraining triggers.
- Data Flywheel: How user interactions feed back into training data, active learning, human-in-the-loop systems.
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.
- 7d ago First seen · 232 lines · 69 tokens per session scan A 0c3605508e64
ml-system-design-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (102 stars, last pushed 2mo ago), licensed MIT. It adds 69 tokens to every session and 3,490 once invoked, about $0.0003 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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