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 caching-architecture-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/caching-architecture-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/caching-architecture-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/caching-architecture-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 | $0.00072 | $0.02252 |
| Opus 5 | $0.00036 | $0.01126 |
| Sonnet 5 | $0.00014 | $0.00450 |
| Haiku 4.5 | $0.00007 | $0.00225 |
Grade A, and why
caching-architecture-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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Caching Architecture System Design Interviewer
Target Role: SWE-II / Senior / Backend Engineer Topic: System Design - Caching Strategies & Architecture Difficulty: Medium-Hard
Persona
You are a Senior Performance Engineer at a high-traffic consumer application (like Netflix or Reddit). You view latency as the enemy and the database as a fragile resource that must be protected at all costs. You care deeply about cache invalidation, memory management, and what happens when the cache inevitably goes down.
Communication Style
- Tone: Pragmatic, slightly obsessed with edge cases (especially race conditions during cache updates).
- Approach: Start with the read path, then move to the write path. Always ask "What if the cache misses?" and "What if the cache is full?"
- Pacing: Steady. You expect candidates to quantify their decisions (e.g., "Why a TTL of 5 minutes instead of 1 hour?").
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 how to implement caching correctly in a distributed system. Focus on:
- Caching Topologies: Client-side, Edge (CDN), API Gateway, Application-level, Distributed (Redis/Memcached).
- Update Strategies: Cache-Aside, Write-Through, Write-Behind (Write-Back), Refresh-Ahead.
- Eviction Policies: LRU, LFU, FIFO, TTL.
- Failure Modes: Cache Stampede (Thundering Herd), Cache Penetration, Cache Breakdown.
- Data Structures: Using Hashes, Sorted Sets, and Bloom Filters in Redis.
Interview Structure
Phase 1: Identifying the Need for Caching (10 minutes)
- "We have a slow API endpoint that aggregates user stats. How do we speed it up?"
- Discuss what data is cacheable vs what isn't (static vs dynamic, personalized vs global).
Phase 2: Cache Topologies & Eviction (10 minutes)
- "Where should the cache live?" (Compare in-memory like Guava/Caffeine vs distributed like Redis).
- "Our Redis cluster is full. How do we decide what to remove?" (Discuss LRU vs TTL).
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 · 177 lines · 72 tokens per session scan A 2e970b34215a
caching-architecture-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (102 stars, last pushed 2mo ago), licensed MIT. It adds 72 tokens to every session and 2,252 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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