Agent Builder
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
交互式测验，将你的 AI/ML 知识映射到 523 节课、20 个阶段的 AI Engineering from Scratch 课程起点。
Install Find Your Level
$ npx skills add fancyboi999/ai-engineering-from-scratch-zh --skill find-your-level -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install fancyboi999/ai-engineering-from-scratch-zh find-your-level --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/fancyboi999/ai-engineering-from-scratch-zh.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/find-your-level .claude/skills/find-your-level && 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 "find-your-level" agent skill from https://github.com/fancyboi999/ai-engineering-from-scratch-zh/tree/main/skills/find-your-level into .claude/skills/find-your-level/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "find-your-level", 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/fancyboi999/ai-engineering-from-scratch-zh/tree/main/skills/find-your-levelType 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 fancyboi999/ai-engineering-from-scratch-zh --skill find-your-level -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install fancyboi999/ai-engineering-from-scratch-zh find-your-level --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/fancyboi999/ai-engineering-from-scratch-zh.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/find-your-level .agents/skills/find-your-level && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "find-your-level" agent skill from https://github.com/fancyboi999/ai-engineering-from-scratch-zh/tree/main/skills/find-your-level into .agents/skills/find-your-level/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "find-your-level", 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 fancyboi999/ai-engineering-from-scratch-zh --skill find-your-level -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install fancyboi999/ai-engineering-from-scratch-zh find-your-level --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/fancyboi999/ai-engineering-from-scratch-zh.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/find-your-level .cursor/skills/find-your-level && 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 "find-your-level" agent skill from https://github.com/fancyboi999/ai-engineering-from-scratch-zh/tree/main/skills/find-your-level into .cursor/skills/find-your-level/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "find-your-level", 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/fancyboi999/ai-engineering-from-scratch-zh.git --path skills/find-your-level--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 fancyboi999/ai-engineering-from-scratch-zh --skill find-your-level -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install fancyboi999/ai-engineering-from-scratch-zh find-your-level --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/fancyboi999/ai-engineering-from-scratch-zh.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/find-your-level .gemini/skills/find-your-level && 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 "find-your-level" agent skill from https://github.com/fancyboi999/ai-engineering-from-scratch-zh/tree/main/skills/find-your-level into .gemini/skills/find-your-level/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "find-your-level", 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 fancyboi999/ai-engineering-from-scratch-zh find-your-levelInstalls 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 fancyboi999/ai-engineering-from-scratch-zh --skill find-your-level -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/fancyboi999/ai-engineering-from-scratch-zh.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/find-your-level .github/skills/find-your-level && 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 "find-your-level" agent skill from https://github.com/fancyboi999/ai-engineering-from-scratch-zh/tree/main/skills/find-your-level into .github/skills/find-your-level/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "find-your-level", 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 fancyboi999/ai-engineering-from-scratch-zh --skill find-your-level -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install fancyboi999/ai-engineering-from-scratch-zh find-your-level --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/fancyboi999/ai-engineering-from-scratch-zh.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/find-your-level .opencode/skills/find-your-level && 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 "find-your-level" agent skill from https://github.com/fancyboi999/ai-engineering-from-scratch-zh/tree/main/skills/find-your-level into .opencode/skills/find-your-level/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "find-your-level", 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.
Facts
- Skill name
find-your-level- GitHub stars
- 1.2k
- Token cost
- ~1k tokens
- SKILL.md length
- 345 words
- Files
- 2 (incl. references)
- Skills in repo
- 14
- Repo updated
- First seen
- Licence
- MIT
At a glance
交互式测验，将你的 AI/ML 知识映射到 523 节课、20 个阶段的 AI Engineering from Scratch 课程起点。
- AI & LLM Engineering work in your project
- SKILL.md covers 测验结构, 评分, 进行测验 and 五轮全部完成后, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Find Your Level is an agent skill from fancyboi999/ai-engineering-from-scratch-zh. 交互式测验，将你的 AI/ML 知识映射到 523 节课、20 个阶段的 AI Engineering from Scratch 课程起点。 触发短语：“我应该从哪里开始”、“帮我定位水平”、“我懂什么”、“哪个阶段”、 “评估我的知识”、“分级测试”、“跳过前面内容”，或 "where should I start", "find my level", "what do I know", "which phase", "assess my knowledge", "placement test", "skip ahead"
Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/answer-key.md`).
It sits in AI & LLM Engineering. The repository describes itself as: Agent工程师最全学习路径 · 从零精通 AI 工程 · 20 阶段 503 课 · 中文全量翻译 + 配套站点 + 动画讲解视频 · 如何成为 AI Agent 工程师的修成指南. The licence is MIT.
When your agent uses it
- AI & LLM Engineering work in your project
Example prompts
- “我应该从哪里开始”
- “帮我定位水平”
- “评估我的知识”
- “/find-your-level”
What it can do on your machine
Read from SKILL.md and the folder at commit d6c7b73. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Find Your Level loads about 1k tokens when it runs, and up to ~1.4k if it reads all its reference files. Until then it costs about 70 tokens; SKILL.md has 345 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.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
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 fancyboi999/ai-engineering-from-scratch-zh at commit d6c7b73, republished under its MIT licence (© fancyboi999). 345 words, ~1,026 tokens.
Download SKILL.mdSave it as
.claude/skills/find-your-level/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- find-your-level
- description
- 交互式测验，将你的 AI/ML 知识映射到 523 节课、20 个阶段的 AI Engineering from Scratch 课程起点。 触发短语：“我应该从哪里开始”、“帮我定位水平”、“我懂什么”、“哪个阶段”、 “评估我的知识”、“分级测试”、“跳过前面内容”，或 "where should I start", "find my level", "what do I know", "which phase", "assess my knowledge", "placement test", "skip ahead"
- version
- 1.0.0
- tags
- assessment, onboarding, curriculum, ai-engineering
找到你的水平
你正在为 AI Engineering from Scratch 课程（20 个阶段、523 节课）进行分级测验。你的任务是找出学习者应从哪里开始，让他们跳过已掌握的材料，恰好从有挑战的地方起步。适用于任何 agent。
测验结构
共有 5 个知识领域，每个领域 2 道题，共 10 题。每轮展示 2 道题（每个领域一轮）。学习者答完一轮的两题后，为该领域评分再进入下一轮。
评分
每题 1 分（0 = 错误或空白，1 = 正确）。每个领域得分 0-2。总分范围为 0-10。
进行测验
先简短问候学习者，随即进入第 1 轮。环境有结构化问题/选项工具时，每题都使用它；否则用纯文本展示带字母的选项并等待回复。每轮后，先告知该领域得分（例如“数学与统计：2/2”）再进入下一轮。说明保持简短。所有答案解释都留到最后。
答案隔离
答案键特意存放在本测验正文之外的 references/answer-key.md。学习者提交当前轮两题答案之前，不要读取该引用。之后只读取当前轮的答案键、进行评分，并在五轮全部完成前保持解释私密。不要预加载后续轮次。
回复格式示例中绝不放入真实答案字母、可能答案或答案分布。纯文本时必须严格使用中性提示：请按此格式回复：Q1: <letter>, Q2: <letter>。替换当前题号，但两个值均保留为 <letter>。
第 1 轮 —— 数学与统计
Q1. 有两个向量，a = [1, 2, 3] 和 b = [4, 5, 6]。它们的点积是多少？
- A) 32
- B) 21
- C) 15
- D) 27
Q2. 一枚公平硬币抛掷 3 次。恰好出现 2 次正面的概率是多少？
- A) 1/4
- B) 1/2
- C) 1/8
- D) 3/8
第 2 轮 —— 经典机器学习
Q3. 在一个含 90% 负样本和 10% 正样本的分类任务中，模型把所有样本都预测为负类。它的准确率是多少？
- A) 50%
- B) 90%
- C) 10%
- D) 0%
Q4. 下列哪一项是 Random Forest 的超参数？
- A) 学得的划分阈值
- B) 叶节点预测值
- C) 树的数量
- D) 每个节点的 Gini impurity
第 3 轮 —— 深度学习
Q5. 在反向传播中，链式法则计算什么？
- A) 每个可训练权重的损失梯度
- B) 当前优化器的最佳学习率
- C) 网络所需层数的精确值
- D) 每个训练步骤使用的 batch size
Q6. ResNet 中的 residual connections（skip connections）主要解决什么问题？
- A) 小型训练数据集上的泛化不佳
- B) 从持久化存储加载 batch 很慢
- C) 模型推理时 activation memory 很高
- D) 在极深网络中的 gradient flow 太弱
第 4 轮 —— NLP 与 Transformer
Q7. 在 Transformer 架构中，attention mechanism 在什么之间计算？
- A) 像素和标签
- B) 仅 Encoder 和 Decoder
- C) Queries、Keys 和 Values
- D) 仅 Embeddings 和 positions
Q8. 使用 LoRA（Low-Rank Adaptation）微调大型语言模型的主要益处是什么？
- A) 从全新随机初始化开始，重新训练基础模型中的每一个参数
- B) 冻结基础模型权重，只训练低秩 adapters
- C) 无需带标签的示例或任务专属训练数据
- D) 复制模型层以提高适应容量
第 5 轮 —— 应用 AI
Q9. 在 RAG（Retrieval-Augmented Generation）系统中，LLM 生成答案之前会发生什么？
- A) 检索相关文档并加入模型 prompt
- B) 针对用户当前问题完整重新训练模型
- C) 用户在每次模型请求前选择所有上下文段落
- D) 模型只搜索预训练参数值
Q10. 在多 agent 系统中，“coordinator” 或 “orchestrator” agent 的首要职责是什么？
- A) 用一个通用模型替换所有专用 agent
- B) 分派任务、路由消息并协调其他 agent
- C) 最大化每次 agent 交互的 token 用量
- D) 为系统故障准备一份完全相同的备用模型
五轮全部完成后
展示领域拆分和总分：
数学与统计： X/2
经典机器学习： X/2
深度学习： X/2
NLP 与 Transformer： X/2
应用 AI： X/2
----------------------------
总分： X/10分数到起点的映射
| 总分 | 起点 | 含义 |
|---|---|---|
| 0-3 | 阶段 1：数学基础 | 从基础开始 |
| 4-5 | 阶段 3：深度学习核心 | 已有数学和 ML 基础 |
| 6-7 | 阶段 7：Transformer 深入剖析 | 已懂 DL，该学习 transformer 了 |
| 8-9 | 阶段 11：LLM 工程 | 基础扎实，可直接进入 LLM 应用 |
| 10 | 阶段 14：Agent 工程 | 你全都会，开始构建 agent |
个性化学习路径
揭示起点后，生成覆盖全部 20 个阶段的 markdown 表格。用分数决定每阶段状态。起点之前的阶段标记为 Skip（学习者已掌握材料）；起点及之后标记为 Do。如果学习者在对应可跳过阶段的领域得分为 1/2，则该阶段标记为 Review 而不是 Skip。
用于检测复习项的领域到阶段映射：
- 数学与统计（1/2） -> 将阶段 1 标为
Review - 经典机器学习（1/2） -> 将阶段 2 标为
Review - 深度学习（1/2） -> 将阶段 3 标为
Review - NLP 与 Transformer（1/2） -> 将阶段 5 和 7 标为
Review - 应用 AI（1/2） -> 将阶段 14 标为
Review
从 ROADMAP.md 读取时间估算（规范唯一事实来源）。每个阶段标题都以 (~N hours) 格式包含预计小时数。解析这些值，不能使用硬编码数字，以便学习路径始终随路线图估算更新。仓库未在本地克隆时，从下面地址获取：
https://raw.githubusercontent.com/fancyboi999/ai-engineering-from-scratch-zh/main/ROADMAP.md。
输出格式
按此格式生成表格：
| Phase | Name | Status | Est. Hours |
|-------|------|--------|------------|
| 0 | Setup & Tooling | Skip | -- |
| 1 | Math Foundations | Review | 30 |
| 2 | ML Fundamentals | Skip | -- |
| 3 | Deep Learning Core | Do | 20 |
| ... | ... | ... | ... |表格规则：
Skip阶段的小时数显示--（不计入总数）。Review阶段显示完整小时数（学习者应快速复习）。Do阶段显示完整小时数。- 阶段 0（环境设置与工具）无论得分如何都始终为
Skip（它是工具设置，不是知识）。 - 汇总
Review与Do阶段的小时数，并在底部展示总计。
表格后添加一句总时长：你的个性化路径：约 X 小时，覆盖 Y 个阶段。
接着给出简短建议：从哪一阶段开始，以及基于最弱领域应首先关注什么。
最后给出下一步：/start-learning 会把此次定位保存到持久化的 LEARNING.md 学习计划，/learn 会开始交互式教授第一课。
© fancyboi999, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file (references) in skills/find-your-level of fancyboi999/ai-engineering-from-scratch-zh.
- SKILL.md
- references/answer-key.md
Open the folder on GitHubat commit d6c7b73
Compare with similar skills
Find Your Level 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 |
|---|---|---|---|---|---|---|
| Find Your Level this skillfancyboi999/ai-engineering-from-scratch-zh | 1.2k | — | ~1k | Automated safety check: Pass | MIT | |
| Agent BuildershareAI-lab/learn-claude-code | 78k | 4 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3.3k | Automated safety check: Pass | MIT | |
| 1passwordtrpc-group/trpc-agent-go | 1.9k | 14 repos | ~656 | Automated safety check: Pass | Apache-2.0 |
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Course Guide
fancyboi999/ai-engineering-from-scratch-zh
AI Engineering from Scratch 课程的主题路由器。给它一个主题、问题或正在处理的 bug， 它会指出精确教授它的课程，以及下一条正确命令。触发短语： “在哪里学习”、“哪节课涵盖”、“课程导航”、“我卡在”、“接下来该做什么”、 “教我 MCP”、“教我 Agent Skills”、“在哪里准备 Claude certification”，或 "where do I…
Learn
fancyboi999/ai-engineering-from-scratch-zh
AI Engineering from Scratch 课程的交互式课程 tutor。读取 LEARNING.md，获取下一课， 在终端按章节教学，结尾测验并记录进度。可在克隆仓库中或完全通过 raw.githubusercontent.com 工作—— 无需设置。触发短语：“下一课”、“教我”、“继续课程”、“我们来学习”、“继续学习”，或 "next lesson", "teach…
Learn Agent Skills
fancyboi999/ai-engineering-from-scratch-zh
AI Engineering from Scratch 中 Agent Skills Engineering 路线的专注交互 tutor。
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Questions about Find Your Level
What does Find Your Level do?
交互式测验，将你的 AI/ML 知识映射到 523 节课、20 个阶段的 AI Engineering from Scratch 课程起点。. Find Your Level is an agent skill from fancyboi999/ai-engineering-from-scratch-zh.
When should I use Find Your Level?
Find Your Level fits situations like: AI & LLM Engineering work in your project.
How do I install Find Your Level in Claude Code?
Run `npx skills add fancyboi999/ai-engineering-from-scratch-zh --skill find-your-level -a claude-code`. Or copy the skill folder (skills/find-your-level in fancyboi999/ai-engineering-from-scratch-zh) into .claude/skills/find-your-level in your project. Claude Code loads it when a task matches its description.
How do I install Find Your Level in Codex?
Run `npx skills add fancyboi999/ai-engineering-from-scratch-zh --skill find-your-level -a codex`. Or copy the skill folder (skills/find-your-level in fancyboi999/ai-engineering-from-scratch-zh) into .agents/skills/find-your-level in your project. Codex loads it when a task matches its description.
Can I use Find Your Level 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 fancyboi999/ai-engineering-from-scratch-zh --skill find-your-level -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/find-your-level, .gemini/skills/find-your-level, .github/skills/find-your-level and .opencode/skills/find-your-level in your project.
What does Find Your Level need to run?
SKILL.md names no scripts, command-line tools or credentials: Find Your Level is instructions for the agent only.
Does Find Your Level access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Find Your Level safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Find Your Level use?
Find Your Level 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 Find Your Level use?
About 1k tokens (SKILL.md is roughly 4.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 331 tokens, read only when the agent opens those files.
What are the alternatives to Find Your Level?
Skills that share tags, products or a category with Find Your Level: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (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 Find Your Level?
fancyboi999 (a GitHub user) maintains it in fancyboi999/ai-engineering-from-scratch-zh, which has 1,204 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 9, 2026.
Source: fancyboi999/ai-engineering-from-scratch-zh on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.