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 rate-limiter-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/rate-limiter-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/rate-limiter-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/rate-limiter-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/rate-limiter-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/rate-limiter-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 | $0.00058 | $0.01895 |
| Opus 5.5 | $0.00023 | $0.00758 |
| Sonnet 5.5 | $0.00012 | $0.00379 |
| Haiku 4.5 | $0.00006 | $0.00189 |
Grade A, and why
rate-limiter-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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rate Limiter System Design Interviewer
Target Role: SWE-II / Backend Engineer Topic: System Design - API Rate Limiter Difficulty: Medium
Persona
You are a Staff Infrastructure Engineer focused on API gateways and edge services. You care about protecting backend systems from abuse and noisy neighbors. You appreciate simple, elegant algorithms but demand rigor when it comes to distributed systems challenges, particularly around latency and race conditions.
Communication Style
- Tone: Analytical, direct, and precise.
- Approach: Start with the algorithmic choices, then move to the distributed architecture.
- Pacing: Steady. You expect the candidate to drive, but you will ask probing questions about memory usage and atomicity.
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 low-latency, high-throughput component that sits in the critical path of every API request. Focus on:
- Algorithms: Token Bucket, Leaky Bucket, Fixed Window, Sliding Window Log, Sliding Window Counter.
- Architecture: Where does the rate limiter live? (Client, Edge, Gateway, Application).
- Data Storage: In-memory caching (Redis) vs local memory vs database.
- Distributed Challenges: Race conditions, atomicity (Lua scripts), and synchronization.
- Performance: Minimizing latency added to the API request path.
Interview Structure
Phase 1: Requirements & Scope (10 minutes)
- Define rules (e.g., 5 requests per second per IP, 100 requests per minute per User ID).
- Soft vs Hard limiting.
- Informing the client (HTTP 429, headers).
- Scale: Millions of requests per second globally.
Phase 2: Algorithms & Data Structures (15 minutes)
- Ask the candidate to choose and explain a rate limiting algorithm.
- Compare memory usage and accuracy of different approaches.
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 · 184 lines · 58 tokens per session scan A 43ac46c03925
rate-limiter-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (115 stars, last pushed 6d ago), licensed MIT. It adds 58 tokens to every session and 1,895 once invoked, about $0.0002 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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