Analysis

system-design-interviewer

by Debashis2007

Pressure-test a system design or run a mock system-design interview. Use to evaluate a proposed architecture for scale, reliability, and trade-offs, or to practice interviews with structured feedback.

v1.0.0New
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Install this skill

Run this command in your terminal. No account required — it auto-detects your AI tool and installs the skill file.

npx @skills-hub-ai/cli install system-design-interviewer-2
Or download directly:
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Setup by platform

Claude Code

~/.claude/skills/<skill>/SKILL.md

Setup guide →

Cursor

~/.cursor/skills/<skill>/SKILL.md

Setup guide →

Windsurf

~/.windsurf/skills/<skill>/SKILL.md, or add to .windsurfrules in your project

Setup guide →

GitHub Copilot

.github/copilot-instructions.md (project-level), paste skill content into instructions

Setup guide →

Cline

~/.cline/skills/<skill>/SKILL.md, or add to .clinerules in your project

Setup guide →

Codex CLI

~/.codex/skills/<skill>/SKILL.md, or reference via codex.md instructions file

Setup guide →

Gemini CLI

GEMINI.md (project-level) or ~/.gemini/skills/<skill>/SKILL.md

Setup guide →

Install

One-click setup for your editor

Run in your project root

npx @skills-hub-ai/cli install system-design-interviewer-2 --target claude-code

Examples

Prompt

Can you design a scalable real-time chat system for millions of users?

Output

First, clarify requirements: real-time messaging, 1 million daily active users, 10,000 messages/sec, latency <200ms, high availability. Estimate peak QPS: assume 10× daily active, so 100,000 QPS. Data model: messages stored with user and timestamp, access patterns favor recent messages. Use a distributed message broker like Kafka for decoupling, with a pub/sub layer. Store messages in a NoSQL store like Cassandra for scalability, with a separate cache layer (Redis) for recent messages. For scaling reads, implement read replicas and CDN for static content. Handle failures with multi-AZ deployment, message queues for durability, and monitoring. Trade-offs: eventual consistency for high throughput, sacrificing immediate delivery guarantees. Next bottleneck: message storage write throughput; evolve with sharding and batching. Reliability: replicas, failover, alerting. Focus on latency and fault tolerance to meet scale and availability goals.

Prompt

Evaluate a proposed architecture for a photo storage service with billions of images.

Output

Begin by defining scope: store billions of images, support high read throughput, ensure durability, and scale storage cost-effectively. Estimate storage growth: 1 billion images/year, averaging 1MB each, so 1PB/year. Read/write ratio: mostly reads, with occasional uploads. High-level design: users upload images via API, images stored in object storage like S3, with a CDN for fast delivery. Metadata stored in a relational DB for indexing. Use a content delivery network to serve images globally. Deep dive into data store choice: object storage for cost-effective large-scale storage, with metadata in SQL for consistency. For scaling reads, leverage CDN and caching of popular images. Handle failures with multi-region object storage, versioning, and backups. Reliability: multi-AZ deployment, monitoring, alerting. Trade-offs include eventual consistency in object storage, sacrificing immediate consistency for durability and cost. Bottleneck at storage ingress; plan for sharding uploads and parallel processing. At 10× scale, storage costs and network bandwidth become critical, requiring tiered storage and optimized caching.

Instructions

This skill doesn’t include stateful context yet, instructions only. Learn about stateful skills.

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Frequently asked questions about system-design-interviewer

What does the system-design-interviewer skill do?

Pressure-test a system design or run a mock system-design interview. Use to evaluate a proposed architecture for scale, reliability, and trade-offs, or to practice interviews with structured feedback. It's a reusable SKILL.md instruction set that loads into your AI coding assistant on demand, no prompt engineering, no copy-pasting every session.

How do I install the system-design-interviewer skill?

Run `npx @skills-hub-ai/cli install system-design-interviewer-2` from your terminal. The CLI writes the SKILL.md to the correct location for your AI tool (e.g. ~/.claude/skills/system-design-interviewer-2/ for Claude Code or ~/.cursor/skills/ for Cursor with --target cursor) and adds it to your project's .skills.json lockfile.

Which AI tools does system-design-interviewer work with?

system-design-interviewer runs in Claude Code, Cursor, Windsurf, GitHub Copilot, Cline, Codex CLI, Gemini CLI. It follows the open Agent Skills standard (SKILL.md), so the same skill works in every supported tool without modification.

Is the system-design-interviewer skill free?

Yes. Every skill on skills-hub.ai is free and open-source. There are no premium tiers, paywalls, or usage limits. You only pay for whatever AI assistant you're already using.

How do I use system-design-interviewer after installing it?

In Claude Code, type `/system-design-interviewer-2` (or whatever slash command the skill registers) and the AI follows the skill's instructions immediately. You can also reference it by name in natural language, your AI loads the skill into context when relevant.

Can I share the system-design-interviewer skill with my team?

Yes. Commit your project's .skills.json lockfile and teammates run `npx @skills-hub-ai/cli install` (no args) to install every skill at the exact version you pinned. Organization-scoped installs work via skills-hub.ai organizations.