--- 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/_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: [--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 - **``** (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
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。`
`- Euler …`
`- 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 · ` | | `--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` | ` 面试 Cheat Sheet` or ` Quick Reference` | | `--lang` | `zh-CN` | ### Slug `` → kebab/snake-case `` 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/_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 for math/code/factual correctness and style discipline. ## Files to read (READ-ONLY) - Draft MD: - 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
answer. Specifically flag wrong year / wrong paper / wrong author / off-by-one indexing / inverted comparison. 4. historical_citations — Paper authors + year + venue. Flag wrong attributions (e.g., "DPO: Rafailov 2023 NeurIPS" must be checkable). 5. table_pipe_escape — Any markdown table cell containing `|x|` math (not `\lvert x \rvert`)? Cite line. 6. callout_list_collision — Any line matching the pattern `^> (?:💡|⚠️|✅|❌) \*\*[^*]+\*\* — (?:- |\d+\. )`? That swallows the list. 7. heading_consistency — All `## §N` and `### N.M` follow style guide (space after §N, no glued chars). 8. section_completeness — Sections §0..§10 (and §A if effort=max) present and non-trivial. 9. length_target — Within ±20% of target (600 for balanced, 1000 for max). 10. personal_info_leak — None of: SJTU JHC, JHC PhD, Server5, job market, /Users/, specific lab names like "John Hopcroft Center", company recruitment context. Return JSON: { "verdict": "PASS | WARN | FAIL", "checks": {: "pass|warn|fail with one-line note + file:line if applicable"}, "blocking_issues": ["..."], "warnings": ["..."] } Verdict: PASS = all pass, WARN = at most cosmetic issues (length slight off / cosmetic style), FAIL = any math/code/factual error OR personal-info leak OR table-pipe / callout-list bug. ``` ### Step 4 — Fix and loop (no hard cap — judge by trajectory) For each FAIL issue, edit the MD. Then re-invoke codex with a **fresh thread** (never reuse threadId). Stop when verdict = PASS or WARN with no FAIL items. **No hard round cap.** Use these heuristics instead: - ✅ **Keep going** if each round's FAIL items are *shrinking, concrete, enumerable* (e.g., citation year fixes, off-by-one, single-line code bugs). The reviewer is doing useful work — let it converge. - ⛔ **Stop and report** if the same issue keeps coming back (loop detected), or if the FAIL items shift to architectural / scope concerns that need user input, or if the round count exceeds ~6 without convergence. Most tutorials converge in 3-5 rounds. Going to 5-6 rounds is fine if substantive bugs are still being caught — the Video Generation tutorial (May 2026) went to 5 rounds and the final 2 rounds caught real citation errors and an over-attribution to Sora's patch size that would have shipped otherwise. ### Step 5 — Render via /render-html Call directly (do not invoke `/render-html` as a sub-skill; call its python script — gives clear control): ```bash python3 skills/render-html/scripts/render_html.py docs/tutorials/_tutorial.md \ --template academic \ --out docs/tutorials/_tutorial.html \ --title " 面试 Cheat Sheet" \ --subtitle "" \ --eyebrow "Interview Prep · " \ --author "" \ --lang zh-CN ``` `render_html.py` runs its own 13-check codex review automatically. If that FAILs, fix the MD (often a table-pipe or callout-list issue the math/code reviewer missed) and re-render. Note that `render_html.py` itself writes `_tutorial.review.json` for the render-stage audit. ### Step 6 — Combine audit trail After both reviews pass, merge math/code review history + render review history into one `docs/tutorials/_tutorial.review.json`: ```json { "skill": "interview-cheatsheet", "source": "docs/tutorials/_tutorial.md", "source_sha256_prefix": "<16-char prefix>", "output": "docs/tutorials/_tutorial.html", "topic": "", "effort": "balanced | max", "byline": "", "math_code_review": { "verdict": "PASS", "rounds": [ {"run": 1, "verdict": "...", "thread_id": "...", "issue": "...", "fix": "..."}, ... ] }, "render_review": { "verdict": "PASS", "rounds": [...] }, "summary": "", "rendered_at": "" } ``` ### Step 7 — Stop. Report to user. Do **NOT** `git add` / `git commit` / `git push`. Report: ``` ✅ /interview-cheatsheet "" complete. Files: docs/tutorials/_tutorial.md ( lines, bytes) docs/tutorials/_tutorial.html ( bytes, TOC entries) docs/tutorials/_tutorial.review.json Math/code review: PASS after rounds () Render review: PASS after rounds Length: lines (target ) Issues caught + fixed during review: - Suggested commit message: docs(tutorials): add cheat sheet (rendered via /render-html) ⚠️ Did NOT auto-commit — user reviews and pushes manually. Also update docs/tutorials/README.md to add the new row. ``` ## Update the index After the tutorial passes, optionally append a row to `docs/tutorials/README.md`: ``` | ** 面试 Cheat Sheet** | [`_tutorial.md`](_tutorial.md) | [`_tutorial.html`](https://wanshuiyin.github.io/Auto-claude-code-research-in-sleep/tutorials/_tutorial.html) | | ``` Suggest the row to the user but let them edit it in themselves if they want to curate. ## Key invariants (the ARIS rules baked in) | Invariant | How it's enforced | |---|---| | Executor != reviewer family | Claude drafts; gpt-5.5 reviews (math/code stage); gpt-5.5 reviews again (render stage) | | Fresh thread per reviewer call | Step 3 + render's own gate both use `mcp__codex__codex` not `codex-reply` | | Codex reasoning = xhigh | Hardcoded in Step 3 reviewer config | | Personal info redaction | Both math/code reviewer and render reviewer check; banlist in style guide | | Lessons-learned encoded | Table-pipe + callout-list collision rules in style guide AND review checks 5+6 | | No silent failure | If review FAILs and the FAIL set is no longer shrinking (loop) or hits ~6 rounds without convergence, stop and report — don't push | ## When NOT to use - Topic too broad — split into smaller scopes first - Topic outside ML/LLM core — this style guide assumes math + code + Chinese; for general topics use a different format or write directly - Already have a draft you want to edit — use Edit directly, this skill is for greenfield generation - Don't want HTML output — call `/render-html` separately or skip Step 5 ## Reference invocations ``` /interview-cheatsheet "RLHF / DPO / PPO" /interview-cheatsheet "MoE (Mixture-of-Experts)" — effort: max /interview-cheatsheet "KV Cache + Speculative Decoding" /interview-cheatsheet "Long-context: RoPE / YaRN / NTK / MLA" /interview-cheatsheet "Distributed Training (DDP / FSDP / ZeRO / TP / PP)" /interview-cheatsheet "Quantization (GPTQ / AWQ / INT4 / FP8 / SmoothQuant)" ``` ## Reference style files - Style canonical: `docs/tutorials/attention_tutorial.md` + `.html` - Style secondary: `docs/tutorials/flow_matching_tutorial.md` + `.html` - Review audit format: `docs/tutorials/attention_tutorial.review.json` ## Provenance Extracted from the two pilot tutorials (Attention + Flow Matching, May 2026). Both passed cross-model review; the attention tutorial required 3 review rounds — catching a table-pipe collision and a callout-list collision that were not obvious from the rendered output. Those lessons are now baked into the style guide and reviewer checks 5+6 so future tutorials don't repeat them.