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interview-cheatsheet

Generate Chinese ML/LLM interview cheat sheets with formulas, PyTorch code, and 25 questions.

Updated May 25, 2026
Search Tags:#interview prep#question bank#html rendering#ml math#pytorch code#long-form tutorial
One-click install
npx skills add https://github.com/duypham2801/ThS_LLM --skill interview-cheatsheet-duypham2801
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: interview-cheatsheet
Source: https://github.com/duypham2801/ThS_LLM/tree/main/.claude/skills/interview-cheatsheet
Command: npx skills add https://github.com/duypham2801/ThS_LLM --skill interview-cheatsheet-duypham2801

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It converts a specific ML/LLM topic into a structured, long-form Chinese interview-prep cheat sheet so you can quickly master formulas, implementations, and high-frequency questions without manually searching or stitching notes.

Core Features & Use Cases

  • Formula + derivation coverage: Includes core equations with derivations and key assumptions to support confident explanations.
  • From-scratch PyTorch-style code: Provides runnable, from-scratch implementations aligned with the described math.
  • 25 高频面试题分层练习: Supplies 25 questions with L1/L2/L3 difficulty and detailed collapsible answers for targeted revision.
  • Cross-model review workflow: Performs math/code/factual and style checks, then renders a single-file HTML tutorial.
  • Output packaging for reuse: Writes MD/HTML plus an audit JSON to a predictable docs/tutorials path for later study.

Quick Start

Ask: 写面试 cheat sheet，主题是 "KV Cache + Speculative Decoding"，并要求生成一份 600-1000 行的中文教程与 25 高频面试题。

Frequently Asked Questions about interview-cheatsheet

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

FAQPage Schema
How do I generate an ML interview cheat sheet with derivations and from-scratch PyTorch code?▼

To generate an ML interview cheat sheet, specify an ML or LLM topic to receive a long-form tutorial containing math derivations, from-scratch PyTorch code, and 25 high-frequency interview questions with layered answers.

Can I get structured Chinese interview prep notes for LLM topics like KV Cache and Speculative Decoding?▼

Yes, you can request Chinese interview prep notes for LLM topics like KV Cache and Speculative Decoding. The output provides a single-file HTML tutorial with formatted tables, callouts, and collapsible answers.

What's the best way to prepare for ML math and implementation questions simultaneously?▼

The best way to prepare for ML math and implementation questions is using a cheat sheet that integrates core equations with derivations alongside runnable PyTorch implementations, ensuring math and code align for consistent revision.

Does the generated interview question bank include difficulty levels for targeted study?▼

Yes, the generated interview question bank includes 25 high-frequency questions categorized into L1, L2, and L3 difficulty levels with detailed collapsible answers to support targeted topic refresh and exam preparation.

How are math correctness and citation accuracy validated in the generated tutorial?▼

Math correctness and citation accuracy are validated through a cross-model review workflow that performs cross-checks for math and code correctness, citation accuracy, table rendering safety, and personal-info leak prevention, outputting an audit JSON.

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