AI Engineering Project Tutor
rohitg00/ai-engineering-from-scratch
Tutors a learner through one stage of a hands-on AI engineering project per session: lesson, prediction, code, grader run and reflection, with hints but never full solutions.
Explains a concept, formula or mechanism in plain Chinese so the reader can recognize it, follow the reasoning, adjust it when conditions change and apply it to new cases.
SKILL.md written in Chinese; this summary is our English description.
Install Plain-Language Concept Explainer
$ npx skills add lijigang/ljg-skills --skill ljg-explain -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install lijigang/ljg-skills ljg-explain --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/lijigang/ljg-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ljg-explain .claude/skills/ljg-explain && 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 "ljg-explain" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-explain into .claude/skills/ljg-explain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-explain", 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/lijigang/ljg-skills/tree/master/skills/ljg-explainType 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 lijigang/ljg-skills --skill ljg-explain -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install lijigang/ljg-skills ljg-explain --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lijigang/ljg-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ljg-explain .agents/skills/ljg-explain && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ljg-explain" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-explain into .agents/skills/ljg-explain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-explain", 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 lijigang/ljg-skills --skill ljg-explain -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install lijigang/ljg-skills ljg-explain --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lijigang/ljg-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ljg-explain .cursor/skills/ljg-explain && 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 "ljg-explain" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-explain into .cursor/skills/ljg-explain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-explain", 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/lijigang/ljg-skills.git --path skills/ljg-explain--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 lijigang/ljg-skills --skill ljg-explain -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install lijigang/ljg-skills ljg-explain --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lijigang/ljg-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ljg-explain .gemini/skills/ljg-explain && 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 "ljg-explain" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-explain into .gemini/skills/ljg-explain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-explain", 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 lijigang/ljg-skills ljg-explainInstalls 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 lijigang/ljg-skills --skill ljg-explain -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/lijigang/ljg-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ljg-explain .github/skills/ljg-explain && 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 "ljg-explain" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-explain into .github/skills/ljg-explain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-explain", 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 lijigang/ljg-skills --skill ljg-explain -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install lijigang/ljg-skills ljg-explain --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lijigang/ljg-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ljg-explain .opencode/skills/ljg-explain && 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 "ljg-explain" agent skill from https://github.com/lijigang/ljg-skills/tree/master/skills/ljg-explain into .opencode/skills/ljg-explain/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ljg-explain", 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
ljg-explain- GitHub stars
- 7.5k
- Token cost
- ~632 tokens
- SKILL.md length
- 57 words
- Files
- 2
- Skills in repo
- 26
- Repo updated
- First seen
- Licence
- MIT
At a glance
Explains a concept, formula or mechanism in plain Chinese so the reader can recognize it, follow the reasoning, adjust it when conditions change and apply it to new cases.
- Works in 5 steps: 知道为什么需要它。… → 能辨认它。… → 能解释结果。… → …
- Asking for a plain-language explanation of a concept, formula or mechanism
- SKILL.md covers 读者与交付, 理解标准, 公式与原理 and 中文表达, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
This skill writes explanations in plain Chinese for an adult with everyday experience but no specialist background, unless you say otherwise. An explanation is judged by what the reader can do afterward: see why the idea is needed, recognize examples and near-miss look-alikes, follow how the result comes about, predict what changes when one condition changes, and carry the idea into a different situation. These are woven into one narrative rather than laid out as five fixed sections.
For formulas, each symbol is tied to a concrete object, quantity and unit, the reason the operations combine as they do is stated, and only the load-bearing steps of a derivation are shown. A simple numeric example is worked through and the arithmetic rechecked with a calculation tool where one is available. Analogies are used only where the key relationships match, and invented teaching examples state their assumptions.
The output is a single complete reply in the conversation with no quiz or answer key at the end, and no notes or files are created unless you ask. The skill is not for polishing or translating text, guided reading of a whole book or paper, or requests that want only a formal proof or operating steps.
When your agent uses it
- Asking for a plain-language explanation of a concept, formula or mechanism
- Wanting a Feynman-style or explain-like-I'm-five walkthrough of an idea
- Learning a topic from scratch and needing to apply it in a new situation
- Understanding why a formula works, not only how to compute it
Example prompts
- “Explain likelihood in plain terms and show how it differs from posterior probability.”
- “Explain the variance formula so I understand why each operation is there.”
- “Give me a Feynman-style explanation of positive feedback, then a new situation where it applies.”
- “ELI5: why does compound interest grow faster than simple interest?”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- 知道为什么需要它。 从一个真实疑惑或具体问题接到核心定义，说明它属于什么、解决什么问题。概念尽早出现，背景只保留理解所必需的部分。
- 能辨认它。 典型例子与容易混淆的对照都给出判断依据，指出哪些特征决定归类、哪些只是表面相似。优先使用差异很小但判断不同的例子；做不到单条件对照时，如实说明，不编造伪反例。
- 能解释结果。 关键环节接得上，读者知道每一步凭什么成立。机制讲因果过程；数学关系讲定义、逻辑和数量关系；经验规律交代证据与条件，不能把相关写成因果或为公式虚构故事。
- 能随条件改判断。 在原情境里改变一个关键条件，并交代其余条件如何保持，说明结果怎样变化、原判断何时失效。变化既可以翻转结论，也可以改变程度或可信度。
- 能迁移。 用一个表面不同的新情境展示怎样识别相同的关键关系，说明判断依据与必要条件。
What it can do on your machine
Read from SKILL.md and the folder at commit 9e75497. 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.
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
Plain-Language Concept Explainer loads about 632 tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 57 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 lijigang/ljg-skills at commit 9e75497, republished under its MIT licence (© lijigang). 57 words, ~632 tokens.
Download SKILL.mdSave it as
.claude/skills/ljg-explain/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- ljg-explain
- description
- 把概念、公式、机制与原理讲到能辨认、能推导、能迁移：用通俗中文接通具体情境、判别依据、运作过程和成立条件。USE WHEN 用户调用 ljg-explain、说「通俗易懂地讲解」「讲透这个概念或公式」「费曼讲解」「ELI5」，或希望从零理解一个知识点并能用于新情境。NOT FOR 单纯润色或翻译、整篇伴读、整本书或论文解读、只要求形式证明或操作步骤。
- metadata.version
- 1.0.1
ljg-explain：讲到能用
让读者听得懂，也能独立认出新例子、解释结果怎样产生、知道条件变化后该怎样改判断。把这几件事作为完成标准，组织方式由内容决定，不把它们机械排成五个栏目。
读者与交付
- 先使用本次请求与已有上下文中的知识背景、用途和卡点。未说明时，按有日常经验、缺少专业知识的成年人讲解；缺少必要前置知识，就补上够用的那一小段。
- 能合理确定含义就直接讲；只有不同解释会改变讲解对象时才澄清，不能为了填写背景表而中断。
- 默认一次完整输出到对话，在讲解自然收束处结束，不在结尾追加自测题或参考答案；用户明确希望互动时，再按需分轮讲解。
- 默认不写笔记或其他文件。用户要求保存时，遵守指定格式与位置；普通聊天用 Markdown，Org 产物才使用 Org 语法。
理解标准
讲解应让读者做到以下事情，材料可以交织在同一条叙述中：
- 知道为什么需要它。 从一个真实疑惑或具体问题接到核心定义，说明它属于什么、解决什么问题。概念尽早出现，背景只保留理解所必需的部分。
- 能辨认它。 典型例子与容易混淆的对照都给出判断依据，指出哪些特征决定归类、哪些只是表面相似。优先使用差异很小但判断不同的例子；做不到单条件对照时，如实说明，不编造伪反例。
- 能解释结果。 关键环节接得上，读者知道每一步凭什么成立。机制讲因果过程；数学关系讲定义、逻辑和数量关系；经验规律交代证据与条件，不能把相关写成因果或为公式虚构故事。
- 能随条件改判断。 在原情境里改变一个关键条件，并交代其余条件如何保持，说明结果怎样变化、原判断何时失效。变化既可以翻转结论，也可以改变程度或可信度。
- 能迁移。 用一个表面不同的新情境展示怎样识别相同的关键关系，说明判断依据与必要条件。
尽量让一个对象或情境贯穿定义、机制、条件变化，减少读者反复重建场景的负担。新情境用来展示迁移；文学、叙事与审美材料保留自身含义，不强改成规律或习题。
公式与原理
- 把符号对应到具体对象、数量与单位，讲清公式输入什么、算出什么。区别本身就是关键时，明确概率的条件方向、总量与平均量、存量与变化量等。
- 说明运算为什么这样组合：为什么相乘、相除、取最大值、平方或求和。推导只展开承重步骤，让读者能接上；必要前提、取值范围与近似条件随公式出现。
- 用简单数值跑通一次，并区分公式直接算出的量与用户真正关心的判断。复核算式是否对应题意：观测是具体顺序还是只计次数，计算的是实际概率还是成比例的量，都应说清；必要时用可用计算工具复算，检查单位、比例、正负号和舍入。
- 同一情境改变一个变量，展示影响；说明公式适用的对象、时间和条件，避免把某个特例推广到全部情况。
中文表达
- 第一句落在核心判断或读者真正关心的疑惑上。删去开场白、泛泛背景与写作过程提示。
- 用熟悉的短词和看得见的对象。专业词需要出现时，先把意思落到具体情境，再给名称；保留准确理解所需的术语与公式，不追求表面上的「零术语」。
- 一句话推进一个主要意思，句子长短有变化，段落顺着读者的疑惑向前走。补齐从前一句到后一句的桥，删去重复解释、软化铺垫和空洞形容。
- 保留自然的「所以」「但是」等转折，减少机械连词与翻译腔。用直接判断组织中文，避免靠修辞性的「不是……而是……」揭示结论。
- 信任读者，讲清一次就够。交付前按「会这样跟一个聪明朋友说话吗」检验口语；改掉卡顿、空泛和天然搭配不成立的句子。
- 类比只在关键关系对应时使用，交代对应关系与失效之处；真实例子、教学假设与类比各有身份，不能把类比当作事实或证明。
证据与版式
- 使用用户给出的来源与上下文；涉及来源核实、时效、高风险或关键事实不确定时，遵守当前环境的检索要求，优先核对一手材料。事实、推断、简化模型与未知分清，缺口如实保留。
- 虚构的教学算例显式给出假设；引用真实数据时给来源。例子帮助看清关系，不能承担它没有提供的实证力量。
- 用连贯段落承载推理，列表用于并列事项或步骤，表格用于比较。关系图或可视化能明显减轻理解负担时再使用，不为凑版式加图。
- 标题由内容与篇幅决定，避免把「典型例子、机制、边界、迁移」写成每次相同的后台标签。检查过程留在内部，交付讲解正文，不附逐条修改日志。
Gotchas
- 流畅与复述不足以证明理解。检查讲解是否交代新情境下的判断依据、结果推导与条件变化；内容自审通过，也不能声称读者已经学会或给讲解自评「90 分」。
- 每个数字要能被读成明确的一句话。似然与后验概率、行权价值与交易盈亏等容易混淆的量，要在同一例子里显示它们分别回答什么问题。
- 对照中的结果差异必须由给出的条件解释，不能藏住重要信息，靠意外揭晓制造认知缺口。
- 简化时保留决定结论的条件；读者看见的故事要能接回原概念，避免只记住比喻。
- 本技能自包含，直接完成讲解；不自动调用另一讲解技能，也不因讲解任务创建笔记、卡片、提醒或其他产物。
Examples
- 「讲解似然」：固定同一批数据比较假设，明确它与后验概率分别回答的问题，说明条件与新情境；有限数据不等于证实假设。
- 「讲解方差公式」：把每个运算接回数据的离散程度，用数值与条件变化解释，区分不同估计目标对应的公式。
- 「讲解正反馈原理」：从一个具体过程追出反馈回路，区分正负方向与好坏评价，交代放大条件和限制，再迁移到另一情境。
© lijigang, 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 in skills/ljg-explain of lijigang/ljg-skills.
- SKILL.md
- agents/openai.yaml
Open the folder on GitHubat commit 9e75497
Compare with similar skills
Plain-Language Concept Explainer 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 |
|---|---|---|---|---|---|---|
| Plain-Language Concept Explainer this skilllijigang/ljg-skills | 7.5k | — | ~632 | Automated safety check: Pass | MIT | |
| AI Engineering Project Tutorrohitg00/ai-engineering-from-scratch | 66k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Hung-Yi Lee Teaching Stylevoidful/hung-yi-lee-skill | 1.3k | — | ~13k | Automated safety check: Pass | None | |
| AI Engineering Course Guiderohitg00/ai-engineering-from-scratch | 66k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Claude Academy Guideanthropics/skills | 180k | 3 repos | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Feynman Learning Cycle ClassroomTHU-MAIC/OpenMAIC | 40k | — | ~955 | Automated safety check: Pass | MIT |
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Offline HTML Talk Decks
lijigang/ljg-skills
Builds single-file offline HTML talk decks from Org or Markdown outlines, with faithful layout or editorial condensing and keyboard navigation.
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Questions about Plain-Language Concept Explainer
What does Plain-Language Concept Explainer do?
Explains a concept, formula or mechanism in plain Chinese so the reader can recognize it, follow the reasoning, adjust it when conditions change and apply it to new cases. This skill writes explanations in plain Chinese for an adult with everyday experience but no specialist background, unless you say otherwise. An explanation is judged by what the reader can do afterward: see why the idea is needed, recognize examples and near-miss look-alikes, follow how the result comes about, predict what changes when one condition changes, and carry the idea into a different situation.
When should I use Plain-Language Concept Explainer?
Plain-Language Concept Explainer fits situations like: asking for a plain-language explanation of a concept, formula or mechanism; wanting a Feynman-style or explain-like-I'm-five walkthrough of an idea; learning a topic from scratch and needing to apply it in a new situation; understanding why a formula works, not only how to compute it.
How do I install Plain-Language Concept Explainer in Claude Code?
Run `npx skills add lijigang/ljg-skills --skill ljg-explain -a claude-code`. Or copy the skill folder (skills/ljg-explain in lijigang/ljg-skills) into .claude/skills/ljg-explain in your project. Claude Code loads it when a task matches its description.
How do I install Plain-Language Concept Explainer in Codex?
Run `npx skills add lijigang/ljg-skills --skill ljg-explain -a codex`. Or copy the skill folder (skills/ljg-explain in lijigang/ljg-skills) into .agents/skills/ljg-explain in your project. Codex loads it when a task matches its description.
Can I use Plain-Language Concept Explainer 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 lijigang/ljg-skills --skill ljg-explain -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ljg-explain, .gemini/skills/ljg-explain, .github/skills/ljg-explain and .opencode/skills/ljg-explain in your project.
What does Plain-Language Concept Explainer need to run?
SKILL.md names no scripts, command-line tools or credentials: Plain-Language Concept Explainer is instructions for the agent only.
Does Plain-Language Concept Explainer 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 Plain-Language Concept Explainer 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 Plain-Language Concept Explainer use?
Plain-Language Concept Explainer 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 Plain-Language Concept Explainer use?
About 632 tokens (SKILL.md is roughly 2.5k 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 Plain-Language Concept Explainer?
Skills that share tags, products or a category with Plain-Language Concept Explainer: AI Engineering Project Tutor (rohitg00/ai-engineering-from-scratch, 66k stars), Hung-Yi Lee Teaching Style (voidful/hung-yi-lee-skill, 1.3k stars), AI Engineering Course Guide (rohitg00/ai-engineering-from-scratch, 66k stars) and Claude Academy Guide (anthropics/skills, 180k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Plain-Language Concept Explainer?
lijigang (a GitHub user) maintains it in lijigang/ljg-skills, which has 7,474 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on October 8, 2026.
Source: lijigang/ljg-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.