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interview-cheatsheet

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

Updated May 29, 2026
Search Tags:#citation verification#interview prep#html rendering#pytorch code#ml/llm#cheat sheet generation#math derivations
One-click install
npx skills add https://github.com/Mang30/myskills --skill interview-cheatsheet-mang30
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: interview-cheatsheet
Source: https://github.com/Mang30/myskills/tree/main/skills/interview-cheatsheet
Command: npx skills add https://github.com/Mang30/myskills --skill interview-cheatsheet-mang30

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you quickly prepare for ML/LLM interviews by producing a long-form, topic-specific cheat sheet that includes derivations, from-scratch PyTorch code, comparisons, and a graded set of 25 high-frequency questions.

Core Features & Use Cases

  • Formula derivations + verification-ready structure: Generates core formulas with derivations and scaling/variance/boundary notes to support “show your work” interviews.
  • From-scratch PyTorch implementation: Provides runnable, from-zero PyTorch code blocks and common engineering details.
  • 25 高频题分层: Outputs L1 必会, L2 进阶, and L3 顶级 lab questions with collapsible answers for fast revision.
  • Cross-model review discipline: Runs cross-checks on math correctness, code executability, historical citations, and style constraints, then renders to a single-file HTML.
  • Output for documentation workflows: Produces both Markdown and HTML plus a review audit JSON under docs/tutorials/.

Quick Start

Tell it “写一份 KV Cache + Speculative Decoding 教程，并生成面试 cheat sheet”，并按需补充你希望的难度与署名信息。

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 math derivations and PyTorch code?▼

Use this skill to generate a cheat sheet by specifying an ML/LLM topic, which produces rigorous mathematical derivations, from-scratch PyTorch implementations, and graded high-frequency questions in Markdown and HTML.

Can I get runnable PyTorch code and formula derivations for LLM interview prep?▼

Yes, the generated cheat sheet provides from-scratch PyTorch implementations alongside formula derivations and variance notes, designed to support technical LLM interview preparation and code verification.

Does the interview prep output include high-frequency questions with answers?▼

Yes, the output includes 25 high-frequency questions categorized into L1, L2, and L3 difficulty levels with collapsible answers, providing structured revision and graded practice for ML/LLM interviews.

What format is the ML interview cheat sheet rendered in for documentation workflows?▼

The cheat sheet outputs both Markdown and a single-file rendered HTML format alongside a review audit JSON, ensuring documentation-ready outputs for ML interview preparation workflows.

How are math derivations and PyTorch code validated in the generated cheat sheet?▼

Math correctness, code executability, historical citations, and style constraints are validated through a cross-model review discipline, ensuring the generated cheat sheet passes rigorous accuracy checks.

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