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 instructions/rohitg00/ai-engineering-from-scratch/agents-mdgit 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/instructions/rohitg00/ai-engineering-from-scratch/agents-md)<a href="https://agentmods.dev/instructions/rohitg00/ai-engineering-from-scratch/agents-md"><img src="https://agentmods.dev/badge/instructions/rohitg00/ai-engineering-from-scratch/agents-md.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 | $0.03101 | $0.03101 |
| Opus 5 | $0.01550 | $0.01550 |
| Sonnet 5 | $0.00620 | $0.00620 |
| Haiku 4.5 | $0.00310 | $0.00310 |
Grade A, and why
ai-engineering-from-scratch AGENTS.md 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 — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Operating manual for contributors and AI agents touching this repo. Read it before opening a PR.
The repo is a curriculum, not a SaaS app. The lessons are the product. Every rule below keeps 435 lessons coherent over time.
Philosophy
435 lessons. 20 phases. Every algorithm built from raw math before a single framework gets imported. You write backprop, the tokenizer, the attention mechanism, and the agent loop by hand in Python, TypeScript, Rust, or Julia. Then you run the same operation through the production library so the framework stops being a black box. The "Build It / Use It" split is the spine. Each lesson ships a reusable artifact you can plug into your daily workflow.
Repo layout
phases/
NN-phase-slug/
NN-lesson-slug/
docs/en.md # lesson explainer
code/ # implementation + tests
quiz.json # 6 questions
outputs/ # reusable artifact (skill / prompt / agent / MCP server)
README.md # public face; lesson counts auto-synced
ROADMAP.md # phase/lesson status
glossary/terms.md # canonical term definitions
site/
build.js # parses README + ROADMAP + glossary -> data.js
data.js # generated; rebuilt by CI on main push
certifications/claude/
program.json # program metadata, source policy, official links
tracks/*.json # exam blueprint, ordered route, study plans
lessons/NN-slug/ # shared certification lesson contract
assessments/<exam-code>/ # original diagnostics and full mocks
scripts/ # automation
.github/workflows/
curriculum.yml # invariant + auto-sync workflow
Hard rules
- One commit per lesson directory. Never batch multiple lessons into one commit. A 10-lesson PR has 10 commits.
- Conventional commit subjects ≤72 chars:
feat(phase-NN/MM): <slug>. Body explains why, not what. - Mermaid or SVG only for diagrams. No ASCII / Unicode box-drawing.
- Every fenced code block needs a language tag. Use
text,json,python,typescript,rust,julia,bash,console,mermaid,yamlas appropriate. - Original implementations only. Don't cite external curriculum repos in docs, code comments, or commit text. Cite RFCs, official specs, and academic papers when they are the canonical source.
- Dependency allowlist (see
Dependenciesbelow). Stdlib-first. - Never commit generated files:
catalog.jsonis gitignored,site/data.jsis rebuilt by CI,package-lock.jsonis never tracked.
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 · 310 lines · 3,101 tokens per session scan A d4bc4cccea16
ai-engineering-from-scratch AGENTS.md is an instructions file published in the GitHub repository rohitg00/ai-engineering-from-scratch (52,482 stars, last pushed 6d ago), licensed MIT. It adds 3,101 tokens to every session, about $0.0155 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 instructions, from other repositories
ai-engineering-from-scratch-zh AGENTS.md
AGENTS.md instructions for fancyboi999/ai-engineering-from-scratch-zh, covering agents.md, 定位, 仓库结构, 强制规则 and 依赖约束.
screenpipe AGENTS.md
AGENTS.md instructions for screenpipe/screenpipe, covering agents.md, read on demand, source file header, tooling and hot paths.
screenpipe CLAUDE.md
Claude Code instructions for screenpipe/screenpipe: Conventions live in one file so they cannot drift. Read AGENTS.md.
ai-engineering-labs AGENTS.md
Instructions for maxencebernardhub/ai-engineering-labs, covering agents.md, project overview, working rules, code style and python tools.
ai-engineering-labs CLAUDE.md
Instructions for maxencebernardhub/ai-engineering-labs, a project described as: Hands-on AI engineering labs: OpenAI, Anthropic, Google, local models. From a first API call to an agent deployed live on AWS: RAG, LangChain, LangGraph & Deep Agents, multi-provider routing, offline inference, FastAPI, Docker, Lambda.
ai-engineering-labs GEMINI.md
Instructions for maxencebernardhub/ai-engineering-labs, a project described as: Hands-on AI engineering labs: OpenAI, Anthropic, Google, local models. From a first API call to an agent deployed live on AWS: RAG, LangChain, LangGraph & Deep Agents, multi-provider routing, offline inference, FastAPI, Docker, Lambda.