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interview-cheatsheet

Generate Chinese ML/LLM interview cheat sheets with formulas, PyTorch code, and questions.

357|13|Updated May 19, 2026
Search Tags:#llm#interview#pytorch#cheat sheet#ml#html rendering#math derivation
One-click install
npx skills add https://github.com/wanshuiyin/ARIS-in-AI-Offer --skill interview-cheatsheet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: interview-cheatsheet
Source: https://github.com/wanshuiyin/ARIS-in-AI-Offer/tree/main/skills/interview-cheatsheet
Command: npx skills add https://github.com/wanshuiyin/ARIS-in-AI-Offer --skill interview-cheatsheet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mcp__codex__codex.

What problem does it solve?

It saves you from manually assembling a high-quality, competition-grade interview prep guide for a specific ML/LLM topic by producing a single long-form Chinese cheat sheet with formulas, derivations, runnable from-scratch PyTorch code, and a full set of 高频面试题.

Core Features & Use Cases

  • Topic-scoped, exam-ready tutorials: Converts a chosen ML/LLM topic into a 600–1000 line Chinese tutorial with a fixed section skeleton and L1/L2/L3 question coverage.
  • Derivation + from-scratch code together: Includes core formulas with derivations and implements the key ideas in real PyTorch code for review and practice.
  • Cross-model review and render pipeline: Performs math/code/factual/citation checks before generating a final academic-style HTML output, suitable for study and sharing.
  • Use cases: Prepare for 秋招/面试, create study notes for a specific technology (e.g., KV Cache, DPO/PPO/RLHF, MoE), and generate a durable reference page for later revision.

Quick Start

Use the interview-cheatsheet skill to generate an AI interview tutorial by asking for: 写一份 KV Cache + Speculative Decoding 面试 cheat sheet。

Frequently Asked Questions about interview-cheatsheet

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate a comprehensive ML interview cheat sheet with PyTorch code and mathematical derivations?▼

You generate an ML interview cheat sheet by specifying a target topic, which produces a 600-1000 line Chinese tutorial featuring core formulas, step-by-step math derivations, runnable from-scratch PyTorch code, and 25 high-frequency interview questions.

What is the structure of the generated LLM interview preparation guide?▼

The LLM interview preparation guide follows a strict 12-14 section structure that covers systems, training objectives, and inference acceleration scenarios, ensuring comprehensive scope-limited review across L1/L2/L3 question difficulties.

Can I get a cheat sheet for specific topics like KV Cache or RLHF for my autumn recruitment technical interviews?▼

Yes, you can request topic-scoped cheat sheets for specific areas like KV Cache or RLHF, generating exam-ready Chinese tutorials tailored for autumn recruitment and technical interview discussions.

Does the generated interview cheat sheet output as HTML for easy sharing and study?▼

The interview cheat sheet outputs as an academic-style HTML rendering after passing cross-model math, code, factual, and citation review gates, producing a durable reference page suitable for study and sharing.

What machine learning topics are supported for generating technical interview notes?▼

Supported machine learning topics include LLM systems, training objectives such as DPO/PPO/RLHF, inference acceleration scenarios like Speculative Decoding, and MoE, converting any chosen topic into a long-form Chinese tutorial.

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