AI Engineering from Scratch is a free, open-source curriculum that teaches people to build AI systems through lessons and reusable artifacts such as prompts, skills, agents, and MCP servers. It is for learners who want practical foundations or want to create AI applications, and the catalogue skills support parts of that curriculum.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/rohitg00/ai-engineering-from-scratch/start-learningnpx skills add rohitg00/ai-engineering-from-scratch --skill start-learninggit clone --depth 1 https://github.com/rohitg00/ai-engineering-from-scratchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rohitg00/ai-engineering-from-scratch/start-learning)<a href="https://agentmods.dev/skills/rohitg00/ai-engineering-from-scratch/start-learning"><img src="https://agentmods.dev/badge/skills/rohitg00/ai-engineering-from-scratch/start-learning.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00074 | $0.01974 |
| Opus 5 | $0.00037 | $0.00987 |
| Sonnet 5 | $0.00015 | $0.00395 |
| Haiku 4.5 | $0.00007 | $0.00197 |
Grade A, and why
start-learning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Start Learning
You are onboarding a learner into the AI Engineering from Scratch
curriculum: 523 lessons across 20 phases, from linear algebra to autonomous
agents. Your job is to produce LEARNING.md, a single file in the current
directory that captures why they are learning, where they should start, and
what their path looks like. Every later learn session reads and updates
this file, so treat it as the learner's source of truth.
Works with any agent. If your environment has a structured question/option tool, use it for every question; otherwise present lettered options as plain text and wait for the reply.
Host invocation contract
Skill names are portable, but invocation syntax belongs to the host. Before showing a next command, use the correct form:
- Codex:
start-learning,learn,course-guide, and otherskill-nameforms, or tell the learner to choose the skill from/skills. - Claude Code:
/start-learning,/learn,/course-guide, and other/skill-nameforms. - Other compatible hosts: natural language such as
Use learn to start my first lesson.
Never present a Claude Code slash command as universal syntax. When the host is unknown, use the natural-language form.
Resume routing across course modes
Before generic onboarding, resolve every "resume" or "continue" request against these supported state files and their route owners:
LEARNING.mdbelongs tolearnfor the full curriculum.MCP-LEARNING.mdbelongs tolearn-mcpfor the Model Context Protocol (MCP) route.MCP-ENGINEERING-LEARNING.mdis the legacy filename for that samelearn-mcproute, not a separate route.AGENT-SKILLS-LEARNING.mdbelongs tolearn-agent-skills.CLAUDE-CERTIFICATION.mdbelongs toclaude-certification.
If the learner names a route in a resume or continue request, dispatch to its owner immediately even when other state files exist, then stop this skill.
For an unnamed resume or continue request, collect the owners whose state files
exist, grouping both MCP filenames under learn-mcp. If exactly one route owner
remains, invoke it and stop this skill before generic onboarding. learn-mcp
owns legacy-file migration and collision reporting. If two or more route owners
remain, list their learner-facing route names and ask which route to resume
before running placement or changing any state. If none exist, continue with
generic onboarding. Never infer a route from file recency or merge one route's
progress into another state file.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 182 lines · 74 tokens per session scan A 36ac84162b24
start-learning is a skill published in the GitHub repository rohitg00/ai-engineering-from-scratch (52,482 stars, last pushed 6d ago), licensed MIT. It adds 74 tokens to every session and 1,974 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
start-learning
AI Engineering from Scratch 课程的一次性入门流程（523 节课、20 个阶段）。访谈学习者、 运行分级测验，并写入由 learn skill 驱动的持久学习计划 LEARNING.md。触发短语： “开始学习”、“设置课程”、“开始课程”、“带我入门”、“创建学习计划”，或 "start learning", "set up the course", "begin the curriculum", "onboard me", "create my learning plan".
find-your-level
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".
learn
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 me", "continue the course", "let's learn", "resume learning".
course-guide
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", "which lesson covers", "course guide", "I'm stuck on", "what should I do next", "teach me MCP", "teach me Agent…
claude-certification
AI Engineering from Scratch 中四条独立 Claude 认证路线的 AI 原生导师与入门流程。适用于学习者 希望选择 Claude 认证、备考 CCAO-F、CCDV-F、CCAR-F 或 CCAR-P、继续认证路径、以交互方式学习 下一课、运行并验证实践实验、构建并评分产物、参加诊断或模拟测评，或通过 GitHub 上的 Claude Code、 Codex、ChatGPT、Cursor 或其他 agent 补强薄弱考试领域时。.
check-understanding
AI Engineering from Scratch 的阶段测验。用于“给我测验一下”、“测试阶段”、“检查我的理解”、“我掌握第 3 阶段了吗”，也支持 "quiz me", "test phase", "check my understanding", "do I know phase 3" 或 /check-understanding 。.