/interview-cheatsheet

name: interview-cheatsheet
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). Cross-model codex review checks math, code, historical citations, and style discipline; then /render-html produces a single-file HTML with academic-newspaper template. Output: docs/tutorials/<slug>_tutorial.{md,html,review.json}. 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."
argument-hint: <topic> [--effort balanced|max] [--byline "Name (姓名), Affiliation"] [--commit false]
allowed-tools: Bash(*), Read, Write, Edit, mcp__codex__codex

/interview-cheatsheet — long-form Chinese ML/LLM interview prep

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 `"Ruofeng Yang (杨若峰), Shanghai Jiao Tong University"`) — 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)

## §0 TL;DR — callout intro line + numbered list of 5-7 takeaways
## §1 直觉 — why this matters; analogy; one-paragraph mental model
## §2 核心公式 — main formula + derivation (variance / scaling / boundary)
## §3 实现细节 — 50-80 line from-scratch PyTorch
## §4-7 变体 / 工程实践 / 常见 bug — variants, comparison tables, footguns
## §8 复杂度 / 资源 — time + memory complexity
## §9 与相关方法对比 — placement in the ecosystem
## §10 25 高频面试题 — L1 (10 必会) + L2 (10 进阶) + L3 (5 顶级 lab), all with <details><summary> collapsible answers
## §A 附录 (optional) — sanity-check output, reference list

Conventions — bake the established lessons in

| Rule | Why | Example | |---|---|---| | Heading format `## §N Title` with **space after §N** | Older versions had `§0TL;DR` glued | `## §0 TL;DR Cheat Sheet` | | Math in table cells: use `\lvert ... \rvert` not `\|...\|` | `\|` inside markdown table = cell separator → row break | `$\text{score}_{ij} - m \cdot \lvert i-j \rvert$` | | Callouts with body list: **split** into callout intro line + separate list | Otherwise the list's first item is swallowed by the callout, then items 2..N restart numbering at 1 | `> 💡 **Sampler 选择** — 按 NFE/质量排序如下。`<br/>`- Euler …`<br/>`- Heun …` | | Callout prefixes only: `💡` `⚠️` `✅` `❌` (others won't get class) | renderer maps these to `callout-info/warn/good/bad` | `> ⚠️ **FP16 overflow** — 即使除了 √d_k …` | | Math: `$...$` inline, `$$...$$` display, `$$\boxed{...}$$` for key boxes | MathJax CDN; literal in source | — | | Code: ```python fences, **real PyTorch that would run** | reviewer will check executability | — | | Personal-info banlist: `SJTU JHC`, `JHC PhD`, `Server5`, `job market`, `/Users/...`, specific lab/company names | reviewer flags as FAIL | byline goes via `--author` at render time, not in body | | Language: Chinese primary, English technical terms in-place | matches established cheat-sheet style | "softmax 饱和", "vector field" |

Eyebrow / subtitle / title naming

| Field | Pattern | |---|---| | `--eyebrow` | `Interview Prep · <Topic>` | | `--subtitle` | one Chinese sentence describing scope (e.g. `公式推导 + From-Scratch 代码 + 25 高频题（L1 必会 · L2 进阶 · L3 顶级 lab）`) | | `--title` | `<Topic> 面试 Cheat Sheet` or `<Topic> Quick Reference` | | `--lang` | `zh-CN` |

Slug

`<topic>` → kebab/snake-case `<slug>` for filenames. e.g. "RLHF / DPO / PPO" → `rlhf_dpo_ppo`.

Workflow

Step 1 — Plan structure (no files written)

Internally sketch:

• 12-14 section titles
• List of major formulas (with derivation outline for each)
• List of code blocks (skeleton + what it demonstrates)
• 25 interview questions sorted by L1 / L2 / L3 difficulty (each with one-line expected answer)
• Comparison table topics (e.g., "RLHF vs DPO vs IPO vs SimPO")

If the topic is too broad to fit in one cheat sheet, **stop and ask the user to scope** before drafting.

Step 2 — Draft MD

Write directly to `docs/tutorials/<slug>_tutorial.md`. Follow the style guide. Length target: 600 lines (balanced) or 1000 lines (max), ±20%.

Step 3 — Cross-model math/code review (codex 5.5 xhigh, FRESH thread)

Invoke `mcp__codex__codex` with `model: gpt-5.5`, `config: {model_reasoning_effort: xhigh}`, `sandbox: read-only`, fresh thread (never `codex-reply`).

Reviewer prompt:

You are reviewing a long-form Chinese interview-prep tutorial on <TOPIC> for math/code/factual correctness and style discipline.

## Files to read (READ-ONLY)
- Draft MD: <MD_PATH>
- Style reference: /Users/yangruofeng/Desktop/aris_paper_discussion/aris_repo/docs/tutorials/attention_tutorial.md
  (Read this only for STYLE — do NOT score the draft against the reference's content topic.)

## Return JSON with these 10 checks

1. formula_correctness — Independently re-derive each $$ display formula. Flag any error with file:line.
2. code_correctness — For each python block: would it run? Does it implement the stated math? Imports / shapes / device handling consistent?
3. interview_answer_correctness — Each L1/L2/L3 question's <details> answer. Specifically flag wrong year / wrong paper / wrong author / off-by-one indexing / inverted comparison.
4. historical