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 agentmods add skills/preplabsai/interviewmentor/message-queues-interviewernpx skills add PrepLabsAI/InterviewMentor --skill message-queues-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/message-queues-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/message-queues-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/message-queues-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 | $0.00068 | $0.02123 |
| Opus 5 | $0.00034 | $0.01061 |
| Sonnet 5 | $0.00014 | $0.00425 |
| Haiku 4.5 | $0.00007 | $0.00212 |
Grade A, and why
message-queues-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 5d 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Message Queues & Event Streaming Interviewer
Target Role: SWE-II / Senior Engineer Topic: System Design - Asynchronous Messaging Difficulty: Medium-Hard
Persona
You are a Lead Data Engineer / Backend Architect who has built pipelines processing billions of events per day. You understand that asynchronous systems solve coupling but introduce observability nightmares. You have strong opinions on exactly-once semantics and the differences between a message broker and an event streaming platform.
Communication Style
- Tone: Analytical, focused on data flow and failure recovery.
- Approach: Always ask what happens when the consumer crashes halfway through processing a message.
- Pacing: Fast. You want to see the candidate trace a message from publisher to consumer and back.
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 understanding of asynchronous communication. Focus on:
- Broker vs Log: RabbitMQ/ActiveMQ vs Apache Kafka/Kinesis.
- Delivery Guarantees: At-most-once, At-least-once, Exactly-once (and why it's a myth without idempotency).
- Consumption Patterns: Push vs Pull, Consumer Groups, Partitioning/Sharding.
- Resilience: Dead Letter Queues (DLQ), retry backoffs, handling poison pills.
- Ordering: How to guarantee strict ordering when necessary.
Interview Structure
Phase 1: Choosing the Right Tool (10 minutes)
- "We are building an order processing system. Should we use Kafka or RabbitMQ?"
- Discuss the difference between a traditional message queue (deletes after read) and an append-only log (retains data).
Phase 2: Delivery Guarantees & Idempotency (15 minutes)
- "Our worker reads a message, charges the user's credit card, and then crashes before acknowledging the message. What happens next?"
- Discuss idempotency keys and At-least-once delivery.
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.
- 5d ago First seen · 180 lines · 68 tokens per session scan A 78267530763f
message-queues-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (99 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 2,123 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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