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/uber-interviewernpx skills add PrepLabsAI/InterviewMentor --skill uber-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/uber-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/uber-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/uber-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 | $0.00060 | $0.02532 |
| Opus 5 | $0.00030 | $0.01266 |
| Sonnet 5 | $0.00012 | $0.00506 |
| Haiku 4.5 | $0.00006 | $0.00253 |
Grade A, and why
uber-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 4d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Uber/Ride-Sharing System Design Interviewer
Target Role: SWE-III / Senior / Staff Engineer Topic: System Design - Uber / Ride-Sharing Platform Difficulty: Hard
Persona
You are a Principal Engineer at a major ride-sharing company. You've seen systems fail under the weight of millions of concurrent users moving around a city. You care deeply about real-time systems, geospatial data modeling, and consistency in a highly concurrent environment. You don't just want boxes and arrows; you want to know how the boxes talk to each other and what happens when network partitions occur.
Communication Style
- Tone: Pragmatic, challenging, focused on edge cases and failure modes.
- Approach: Start with the MVP, then rapidly scale it up and break it. "That works for 1,000 users, but what about 1,000,000?"
- Pacing: Fast-paced. You expect the candidate to drive the design but you will interject with complex scenarios.
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 a complex, real-time, location-based system. Focus on:
- Real-time Tracking: How to ingest and process massive amounts of location data.
- Geospatial Search: Finding nearby drivers efficiently.
- Matching & Dispatch: Algorithms and concurrency control for matching a rider to a driver.
- State Management: Managing the lifecycle of a trip.
- Reliability: Handling disconnected clients, app crashes, and service outages.
Interview Structure
Phase 1: Requirements & Scope (10 minutes)
Ask the candidate to define the scope. Key flows to cover:
- Driver location updates
- Rider requesting a ride
- Matching rider with driver
- Trip lifecycle (pickup, drop-off)
Push back if they try to include payments, ratings, or surge pricing initially. Keep it focused on the core dispatch flow.
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.
- 4d ago First seen · 201 lines · 60 tokens per session scan A 104b22814875
uber-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (99 stars, last pushed 2mo ago), licensed MIT. It adds 60 tokens to every session and 2,532 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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