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Agent skill

Interview Cheatsheet

by wanshuiyin in wanshuiyin/Auto-claude-code-research-in-sleep

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).

MITAuto-check: notesAI & LLM Engineering

Install Interview Cheatsheet

skills CLI
$ npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill interview-cheatsheet -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wanshuiyin/Auto-claude-code-research-in-sleep interview-cheatsheet --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/interview-cheatsheet .claude/skills/interview-cheatsheet && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
interview-cheatsheet
GitHub stars
17k
Used in
1 other repo
Token cost
~3.3k tokens
SKILL.md length
983 words
Files
1
Skills in repo
26
Repo updated
First seen
Licence
MIT

At a glance

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).

  • Works in 7 steps: Plan structure (no files written) → Draft MD → Cross-model math/code review (codex… → …
  • The user says 写面试 cheat sheet
  • SKILL.md covers Inputs, Style guide — STRICT (read…, Workflow and Update the index, plus 5 more sections
  • Calls git and python3; reaches wanshuiyin.github.io

What it does

Interview Cheatsheet is an agent skill from wanshuiyin/Auto-claude-code-research-in-sleep. 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.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Interview preparation and Deep learning. It works with PyTorch. The repository describes itself as: ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework… The licence is MIT.

When your agent uses it

  • The user says 写面试 cheat sheet
  • Wants a 600-1000 line Chinese tutorial on a specific ML topic

Example prompts

  • “写面试 cheat sheet”
  • “写一份 X 教程”
  • “帮我准备 Y 面试题”
  • “/interview-cheatsheet”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash(*), Read, Write, Edit, mcp__codex__codex

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Plan structure (no files written)
  2. Draft MD
  3. Cross-model math/code review (codex gpt-6-astra xhigh, FRESH thread)
  4. Fix and loop (no hard cap — judge by trajectory)
  5. Render via /render-html
  6. Combine audit trail
  7. Stop. Report to user.

What it can do on your machine

Read from SKILL.md and the folder at commit 26b95cf. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(*)
    • Read
    • Write
    • Edit
    • mcp__codex__codex

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • wanshuiyin.github.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Interview Cheatsheet loads about 3.3k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 983 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~92
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash(*), Read, Write, Edit, mcp__codex__codex

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wanshuiyin/Auto-claude-code-research-in-sleep at commit 26b95cf, republished under its MIT licence (© wanshuiyin). 983 words, ~3,287 tokens.

Download SKILL.mdSave it as .claude/skills/interview-cheatsheet/SKILL.md (or your agent's skills folder).
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). 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.
allowed-tools
Bash(*), Read, Write, Edit, mcp__codex__codex
argument-hint
<topic> [--effort balanced|max] [--byline "Name (姓名), Affiliation"] [--commit false]

/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 "<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)
## §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
RuleWhyExample
Heading format ## §N Title with space after §NOlder 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 listOtherwise 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 boxesMathJax CDN; literal in source—
Code: ```python fences, real PyTorch that would runreviewer will check executability—
Personal-info banlist: owner's institution/lab/center names, degree-program affiliations, private server aliases, job-search context, /Users/... paths, specific lab/company namesreviewer flags as FAILbyline goes via --author at render time, not in body
Language: Chinese primary, English technical terms in-placematches established cheat-sheet style"softmax 饱和", "vector field"
Eyebrow / subtitle / title naming
FieldPattern
--eyebrowInterview Prep · <Topic>
--subtitleone 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
--langzh-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 gpt-6-astra xhigh, FRESH thread)

Invoke mcp__codex__codex with model: gpt-6-astra, 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: 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_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: the owner's institution / lab / center names, degree-program affiliations, private server aliases, job-search or recruitment context, absolute `/Users/...` paths. (Keep the concrete string banlist in local untracked notes — the public SKILL defines only the CATEGORIES; listing the real values here would itself be the leak.)

Return JSON:
{
  "verdict": "PASS | WARN | FAIL",
  "checks": {<check_name>: "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.

Show full SKILL.md (360 more words)Show less
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/<slug>_tutorial.md \
  --template academic \
  --out docs/tutorials/<slug>_tutorial.html \
  --title "<Topic> 面试 Cheat Sheet" \
  --subtitle "<one-line scope summary>" \
  --eyebrow "Interview Prep · <Topic>" \
  --author "<byline>" \
  --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 <slug>_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/<slug>_tutorial.review.json:

json
{
  "skill": "interview-cheatsheet",
  "source": "docs/tutorials/<slug>_tutorial.md",
  "source_sha256_prefix": "<16-char prefix>",
  "output": "docs/tutorials/<slug>_tutorial.html",
  "topic": "<TOPIC>",
  "effort": "balanced | max",
  "byline": "<author string>",
  "math_code_review": {
    "verdict": "PASS",
    "rounds": [
      {"run": 1, "verdict": "...", "thread_id": "...", "issue": "...", "fix": "..."},
      ...
    ]
  },
  "render_review": {
    "verdict": "PASS",
    "rounds": [...]
  },
  "summary": "<one-line: N-round math/code review + M-round render review settled at PASS>",
  "rendered_at": "<YYYY-MM-DD>"
}
Step 7 — Stop. Report to user.

Do NOT git add / git commit / git push. Report:

✅ /interview-cheatsheet "<TOPIC>" complete.

  Files:
    docs/tutorials/<slug>_tutorial.md          (<lines> lines, <bytes> bytes)
    docs/tutorials/<slug>_tutorial.html        (<bytes> bytes, <TOC> TOC entries)
    docs/tutorials/<slug>_tutorial.review.json

  Math/code review:  PASS after <N> rounds (<thread IDs>)
  Render review:     PASS after <M> rounds
  Length:            <actual> lines (target <effort>)

  Issues caught + fixed during review:
    - <one line per non-trivial fix>

  Suggested commit message:
    docs(tutorials): add <Topic> 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:

| **<Topic> 面试 Cheat Sheet** | [`<slug>_tutorial.md`](<slug>_tutorial.md) | [`<slug>_tutorial.html`](https://wanshuiyin.github.io/Auto-claude-code-research-in-sleep/tutorials/<slug>_tutorial.html) | <one-line topic list> |

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)

InvariantHow it's enforced
Executor != reviewer familyClaude drafts; gpt-6-astra reviews (math/code stage); gpt-6-astra reviews again (render stage)
Fresh thread per reviewer callStep 3 + render's own gate both use mcp__codex__codex not codex-reply
Codex reasoning = xhighHardcoded in Step 3 reviewer config
Personal info redactionBoth math/code reviewer and render reviewer check; banlist in style guide
Lessons-learned encodedTable-pipe + callout-list collision rules in style guide AND review checks 5+6
No silent failureIf 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.

© wanshuiyin, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/interview-cheatsheet of wanshuiyin/Auto-claude-code-research-in-sleep.

Open the folder on GitHubat commit 26b95cf

Used in 1 other repository

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wanshuiyin/Auto-claude-code-research-in-sleep, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Interview Cheatsheet next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Works with

Categories

Questions about Interview Cheatsheet

What does Interview Cheatsheet do?

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). Interview Cheatsheet is an agent skill from wanshuiyin/Auto-claude-code-research-in-sleep. 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).

When should I use Interview Cheatsheet?

Interview Cheatsheet fits situations like: the user says 写面试 cheat sheet; wants a 600-1000 line Chinese tutorial on a specific ML topic.

How do I install Interview Cheatsheet in Claude Code?

Run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill interview-cheatsheet -a claude-code`. Or copy the skill folder (skills/interview-cheatsheet in wanshuiyin/Auto-claude-code-research-in-sleep) into .claude/skills/interview-cheatsheet in your project. Claude Code loads it when a task matches its description.

How do I install Interview Cheatsheet in Codex?

Run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill interview-cheatsheet -a codex`. Or copy the skill folder (skills/interview-cheatsheet in wanshuiyin/Auto-claude-code-research-in-sleep) into .agents/skills/interview-cheatsheet in your project. Codex loads it when a task matches its description.

Can I use Interview Cheatsheet in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill interview-cheatsheet -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/interview-cheatsheet, .gemini/skills/interview-cheatsheet, .github/skills/interview-cheatsheet and .opencode/skills/interview-cheatsheet in your project.

What does Interview Cheatsheet need to run?

Going by SKILL.md and its folder, Interview Cheatsheet needs the command-line tools its instructions call (git and python3). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash(*), Read, Write, Edit, mcp__codex__codex.

Does Interview Cheatsheet access the network?

SKILL.md names 1 domain. In commands or code: wanshuiyin.github.io; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Interview Cheatsheet safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Interview Cheatsheet use?

Interview Cheatsheet is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Interview Cheatsheet use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Interview Cheatsheet?

Skills that share tags, products or a category with Interview Cheatsheet: Interview Cheatsheet (wanshuiyin/ARIS-in-AI-Offer, 582 stars), Render HTML (wanshuiyin/ARIS-in-AI-Offer, 582 stars), Add Uint Support (pytorch/pytorch, 104k stars) and CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Interview Cheatsheet?

wanshuiyin (a GitHub user) maintains it in wanshuiyin/Auto-claude-code-research-in-sleep, which has 17,205 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on October 7, 2026.

Source: wanshuiyin/Auto-claude-code-research-in-sleep on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.