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).
$ npx skills add wanshuiyin/ARIS-in-AI-Offer --skill interview-cheatsheet -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install wanshuiyin/ARIS-in-AI-Offer interview-cheatsheet --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/wanshuiyin/ARIS-in-AI-Offer.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/interview-cheatsheet .claude/skills/interview-cheatsheet && rm -rf skills-srcUse ~/.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/
Install the "interview-cheatsheet" agent skill from https://github.com/wanshuiyin/ARIS-in-AI-Offer/tree/main/skills/interview-cheatsheet into .claude/skills/interview-cheatsheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interview-cheatsheet", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/wanshuiyin/ARIS-in-AI-Offer/tree/main/skills/interview-cheatsheetType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add wanshuiyin/ARIS-in-AI-Offer --skill interview-cheatsheet -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install wanshuiyin/ARIS-in-AI-Offer interview-cheatsheet --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/ARIS-in-AI-Offer.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/interview-cheatsheet .agents/skills/interview-cheatsheet && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "interview-cheatsheet" agent skill from https://github.com/wanshuiyin/ARIS-in-AI-Offer/tree/main/skills/interview-cheatsheet into .agents/skills/interview-cheatsheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interview-cheatsheet", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add wanshuiyin/ARIS-in-AI-Offer --skill interview-cheatsheet -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install wanshuiyin/ARIS-in-AI-Offer interview-cheatsheet --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/ARIS-in-AI-Offer.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/interview-cheatsheet .cursor/skills/interview-cheatsheet && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "interview-cheatsheet" agent skill from https://github.com/wanshuiyin/ARIS-in-AI-Offer/tree/main/skills/interview-cheatsheet into .cursor/skills/interview-cheatsheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interview-cheatsheet", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/wanshuiyin/ARIS-in-AI-Offer.git --path skills/interview-cheatsheet--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add wanshuiyin/ARIS-in-AI-Offer --skill interview-cheatsheet -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install wanshuiyin/ARIS-in-AI-Offer interview-cheatsheet --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/ARIS-in-AI-Offer.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/interview-cheatsheet .gemini/skills/interview-cheatsheet && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "interview-cheatsheet" agent skill from https://github.com/wanshuiyin/ARIS-in-AI-Offer/tree/main/skills/interview-cheatsheet into .gemini/skills/interview-cheatsheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interview-cheatsheet", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install wanshuiyin/ARIS-in-AI-Offer interview-cheatsheetInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add wanshuiyin/ARIS-in-AI-Offer --skill interview-cheatsheet -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/wanshuiyin/ARIS-in-AI-Offer.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/interview-cheatsheet .github/skills/interview-cheatsheet && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "interview-cheatsheet" agent skill from https://github.com/wanshuiyin/ARIS-in-AI-Offer/tree/main/skills/interview-cheatsheet into .github/skills/interview-cheatsheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interview-cheatsheet", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add wanshuiyin/ARIS-in-AI-Offer --skill interview-cheatsheet -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install wanshuiyin/ARIS-in-AI-Offer interview-cheatsheet --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/wanshuiyin/ARIS-in-AI-Offer.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/interview-cheatsheet .opencode/skills/interview-cheatsheet && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "interview-cheatsheet" agent skill from https://github.com/wanshuiyin/ARIS-in-AI-Offer/tree/main/skills/interview-cheatsheet into .opencode/skills/interview-cheatsheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interview-cheatsheet", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
interview-cheatsheetGenerate 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/ARIS-in-AI-Offer. 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/<slugtutorial.{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…
Its SKILL.md is about 3.4k 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 Deep learning, Interview preparation and HTML artifacts. It works with PyTorch. The repository describes itself as: Bilingual (中文+EN) ML / LLM / diffusion / agent interview cheat sheets for AI 秋招 — generated by ARIS /interview-cheatsheet, rendered by /render-html into single-file HTML, reads…. The licence is MIT.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit c455e43. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
Bash(*)ReadWriteEditmcp__codex__codexFrom allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
gitpython3From the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
wanshuiyin.github.ioFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Interview Cheatsheet loads about 3.4k tokens when it runs. Until then it costs about 149 tokens; SKILL.md has 1,039 words of instructions outside code blocks.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Bash(*), Read, Write, Edit, mcp__codex__codexAutomated 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.
The full file from wanshuiyin/ARIS-in-AI-Offer at commit c455e43, republished under its MIT licence (© wanshuiyin). 1,039 words, ~3,377 tokens.
.claude/skills/interview-cheatsheet/SKILL.md (or your agent's skills folder).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.
<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.docs/tutorials/attention_tutorial.md as canonical reference)## §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| 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" |
| 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 |
<topic> → kebab/snake-case <slug> for filenames. e.g. "RLHF / DPO / PPO" → rlhf_dpo_ppo.
Internally sketch:
If the topic is too broad to fit in one cheat sheet, stop and ask the user to scope before drafting.
Write directly to docs/tutorials/<slug>_tutorial.md. Follow the style guide. Length target: 600 lines (balanced) or 1000 lines (max), ±20%.
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_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": {<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.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:
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.
Call directly (do not invoke /render-html as a sub-skill; call its python script — gives clear control):
python3 tools/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-CNRun the render-fidelity gate yourself — you (the orchestrating agent), not the script. render_html.py is pure stdlib: it only writes the HTML; it does not call Codex and does not write any .review.json. After rendering, fire a fresh mcp__codex__codex thread (never codex-reply) auditing render fidelity + safety exactly as the HTML Review Gate in skills/render-html/SKILL.md (Step 4) specifies. If it FAILs, fix the MD (often a table-pipe or callout-list issue the math/code reviewer missed) and re-render. You then merge the render verdict into the combined .review.json in Step 6 — the script never produces it.
After both reviews pass, merge math/code review history + render review history into one docs/tutorials/<slug>_tutorial.review.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>"
}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.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.
| 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 |
/render-html separately or skip Step 5/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)"docs/tutorials/attention_tutorial.md + .htmldocs/tutorials/flow_matching_tutorial.md + .htmldocs/tutorials/attention_tutorial.review.jsonExtracted 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
Just SKILL.md in skills/interview-cheatsheet of wanshuiyin/ARIS-in-AI-Offer.
Open the folder on GitHubat commit c455e43
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Interview Cheatsheet this skillwanshuiyin/ARIS-in-AI-Offer | 582 | — | ~3.4k | Automated safety check: Notes | MIT | |
| Interview Cheatsheetwanshuiyin/Auto-claude-code-research-in-sleep | 17k | 1 repos | ~3.3k | Automated safety check: Notes | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Add Torch Shapes Examplefacebook/pyrefly | 7.1k | — | ~1.3k | Automated safety check: Pass | MIT | |
| MUSA GPU Training Optimizeropen-infra-skills/infra-skills | 141 | — | ~1.7k | Automated safety check: Pass | Apache-2.0 |
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).
pytorch/pytorch
Add unsigned integer (uint) type support to PyTorch operators by updating ATDISPATCH macros.
Orchestra-Research/AI-Research-SKILLs
Explains OpenAI's CLIP model for zero-shot image classification, image-text similarity, semantic image search and content moderation, with install steps and code patterns.
facebook/pyrefly
A skill your agent uses when adding a new PyTorch model to Pyrefly's shape-tracking example corpus under tensor-shapes/pyrefly-torch-stubs/examples — i.e.
open-infra-skills/infra-skills
Profiles, benchmarks and tunes AI training workloads on Moore Threads MUSA GPUs with a measurement-first process that keeps model behavior unchanged.
pytorch/pytorch
Manage CI for PyTorch ghstack stacks by running CI where its results are useful now and deferring other PRs with [no-ci].
wanshuiyin/ARIS-in-AI-Offer
Generate a fact-checked academic personal homepage from a CV, optionally augmented by an existing manual homepage and an assets directory.
wanshuiyin/ARIS-in-AI-Offer
Render an ARIS Markdown / JSON artifact (IDEAREPORT, AUTOREVIEW, KILLARGUMENT, PAPERPLAN, research-wiki state, etc.) into a single-file HTML view designed for human reading.
Works with
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/ARIS-in-AI-Offer. 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 fits situations like: the user says 写面试 cheat sheet; wants a 600-1000 line Chinese tutorial on a specific ML topic.
Run `npx skills add wanshuiyin/ARIS-in-AI-Offer --skill interview-cheatsheet -a claude-code`. Or copy the skill folder (skills/interview-cheatsheet in wanshuiyin/ARIS-in-AI-Offer) into .claude/skills/interview-cheatsheet in your project. Claude Code loads it when a task matches its description.
Run `npx skills add wanshuiyin/ARIS-in-AI-Offer --skill interview-cheatsheet -a codex`. Or copy the skill folder (skills/interview-cheatsheet in wanshuiyin/ARIS-in-AI-Offer) into .agents/skills/interview-cheatsheet in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wanshuiyin/ARIS-in-AI-Offer --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.
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.
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.
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.
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.
About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Interview Cheatsheet: Interview Cheatsheet (wanshuiyin/Auto-claude-code-research-in-sleep, 17k stars), Add Uint Support (pytorch/pytorch, 104k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Add Torch Shapes Example (facebook/pyrefly, 7.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
wanshuiyin (a GitHub user) maintains it in wanshuiyin/ARIS-in-AI-Offer, which has 582 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on October 6, 2026.
Source: wanshuiyin/ARIS-in-AI-Offer on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.
