# Interview Cheatsheet

Generate a long-form Chinese interview-prep cheat sheet on a specific ML/LLM topic, ...

- Repository: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep (18443 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill interview-cheatsheet -g -a claude-code -y`
Codex: `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill interview-cheatsheet -g -a codex -y`
Cursor: `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill interview-cheatsheet -g -a cursor -y`
Gemini CLI: `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill interview-cheatsheet -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add wanshuiyin/Auto-claude-code-research-in-sleep`

## Description

Generate a long-form Chinese interview-prep cheat sheet on a specific ML/LLM topic, formulas with derivations, from-scratch PyTorch code, comparison tables, and 25 高频面试题 (L1 必会 / L2 进阶 / L3 顶级 lab). Use when the user says '写面试 cheat sheet', '写一份 X 教程', '帮我准备 Y 面试题', '出一份 X 速查', or wants a 600-1000 line Chinese tutorial on a specific ML topic.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# /interview-cheatsheet, long-form Chinese ML/LLM interview prep

> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md):
> resolve invocation → `.aris/reviewer.json` → Claude Code default (Codex MCP)
> with `reviewer_route.py` through the canonical helper resolver, preserving this
> slot's tier, fresh contexts, source/image inputs and deterministic gates.
> Codex examples/pins below apply only to `backend=codex`; other routes use their
> own first-call/reply contract. Save the route and actual returned identity;
> unavailable selections are `REVIEW_UNAVAILABLE`. Classify acceptance from actual
> families/completion/scope. Forward invocation reviewer/model/effort overrides
> to reviewer-bearing children. Generation workers are unchanged.


Generate one comprehensive Chinese cheat sheet per invocation: formulas + derivations + from-scratch code + 25 高频题. Output passes cross-model math/code review before rendering. **Detect-only by default: never auto-commits.**

## Inputs

- **`<topic>`** (required), narrow enough for one 600-1000 line tutorial. Good: "RLHF / DPO / PPO", "MoE", "KV Cache + Speculative Decoding". Bad (too broad): "all of LLM training", "diffusion" (split into Forward Process / Sampling / CFG separately).
- **`--effort`** (default `balanced`), `balanced` ≈ 600 lines, `max` ≈ 1000 lines with deeper proofs and more L3 questions.
- **`--byline`** (default `"<Your Name>, <Affiliation>"`), passed to `/render-html --author`.
- **`--commit`** (default `false`), if `false` (default), stop after rendering; user reviews and commits. Never push without explicit user approval.

## Style guide, STRICT (read `docs/tutorials/attention_tutorial.md` as canonical reference)

### Section skeleton (12-14 sections)

The rest: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/blob/HEAD/skills/interview-cheatsheet/SKILL.md